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Traditional robotic devices are often bulky and rigid, making it difficult for them to adapt to the soft and complex shapes of the human body. In stark contrast, soft robots, as a burgeoning class of robotic technology, showcase exceptional flexibility and adaptability, positioning them as compelling contenders for a diverse array of applications. High-performance electrically responsive artificial muscle materials (ERAMMs), as key driving components of soft robots, can achieve efficient motion and deformation, as well as more flexible and precise robot control, attracting widespread attention. This paper reviews the latest advancements in high-performance ERAMMs and their applications in the field of soft robot actuation, using ionic polymer-metal composites and dielectric elastomers as typical cases. Firstly, the definition, characteristics, and electro-driven working principles of high-performance ERAMMs are introduced. Then, the material design and synthesis, fabrication processes and optimization, as well as characterization and testing methods of the ERAMMs are summarized. Furthermore, various applications of two typical ERAMMs in the field of soft robot actuation are discussed in detail. Finally, the challenges and future directions in current research are analyzed and anticipated. This review paper aims to provide researchers with a reference for understanding the latest research progress in high-performance ERAMMs and to guide the development and application of soft robots. • Inspired by the good performance of natural muscles, extensive attention has been paid to the study of artificial muscles. Based on the structure and properties of artificial muscles, the research progress of high-performance artificial muscle materials is systematically reviewed, the working principle of the device-driven deformation is comprehensively analyzed, and the material design, preparation process and characterization methods are deeply analyzed. • The opportunities and challenges of high-performance artificial muscle materials, especially ionic polymer-metal composites and dielectric elastomers, are discussed, and their future research directions are proposed, laying the foundation for further innovative research on artificial muscle materials and their application development in the field of soft robot actuation. [Display omitted] [ABSTRACT FROM AUTHOR]

This research presents a novel approach to robotic grasping by integrating an attention mechanism and advanced U-Net architectures, specifically UNet and UNet++, into the Generative Grasping Convolutional Neural Network (GG-CNN). The proposed method, Attention UNet++ GG-CNN, aims to improve the performance and accuracy of robotic grasping predictions significantly. The attention mechanism allows the model to focus on the most relevant features in the depth image, enhancing the quality of grasp predictions. The UNet and UNet++ structures, renowned for their efficiency in semantic segmentation tasks, are adapted to generate a pixel-wise grasp quality map, providing a one-to-one mapping from depth images. The UNet++ structure, with its nested and dense skip pathways, further improves the model's ability to capture and propagate low-level details to higher-level features. This approach overcomes the limitations of traditional deep learning grasping techniques by avoiding discrete sampling of grasp candidates and reducing computation times. Preliminary results indicate that the Attention UNet++ GG-CNN significantly improves the accuracy and performance of robotic grasping,with the Attention UNet++ GG-CNN achieving Intersection over Union (IoU) scores up to 95.86%, and average performance metrics across 25%, 50%, and 75% thresholds reaching as high as 88.29%, confirming its potential for more effective and reliable robotic manipulation in various settings, paving the way for more efficient and reliable robotic manipulation in dynamic environments. [ABSTRACT FROM AUTHOR]

A controller is proposed to guide a multi-articulated robot vehicle (MARV) moving backwards, following a certain path. The ability to avoid fixed and moving obstacles is included, using the null space-based control technique to manage the conflicting tasks of following a path and avoiding obstacles. Additionally, control gains are modulated, thus reducing the risk of jackknifing. These new approaches are the contributions of the article. Results of laboratory-scale experiments with a MARV pushing one and two trailers are presented and discussed, which validate the proposed controller. Simulation results are also presented considering a MARV with three trailers, showing that the proposed controller can be adopted for larger articulated chains and another experiment that shows that it is possible to avoid moving obstacles. • Control of a multi-articulated robotic vehicle to follow a path when moving backwards. • Evasive maneuvers to avoid static or dynamic obstacles in the desired path. • Dynamic compensation to reduce the position errors for the rear of the last trailer. • Null space-based control approach hierarchic execution of complimentary subtasks. • Controller gain modulation as a tool to reduce the possibility of jackknife. [ABSTRACT FROM AUTHOR]

This paper introduces a novel resource-efficient control structure for remote path-following control of autonomous vehicles based on a comprehensive combination of Kalman filtering, non-uniform dual-rate sampling, periodic event-triggered communication, and prediction-based and packet-based control techniques. An essential component of the control solution is a non-uniform dual-rate extended Kalman filter (NUDREKF), which includes an h-step ahead prediction stage. The prediction error of the NUDREKF is ensured to be exponentially mean-square bounded. The algorithmic implementation of the filter is straightforward and triggered by periodic event conditions. The main goal of the approach is to achieve efficient usage of resources in a wireless networked control system (WNCS), while maintaining satisfactory path-following behavior for the vehicle (a holonomic Mecanum-wheeled robot). The proposal is additionally capable of coping with typical drawbacks of WNCS such as time-varying delays, and packet dropouts and disorder. A Simscape Multibody simulation application reveals reductions of up to 93% in resource usage compared to a nominal time-triggered control solution. The simulation results are experimentally validated in the holonomic Mecanum-wheeled robotic platform. • A novel, comprehensive periodic event-triggered networked control solution is proposed. • High reduction of resource usage (of up to 93%) can be reached while maintaining acceptable control performance. • A non-uniform dual-rate extended Kalman filter (NUDREKF) is essential in the control structure. • Some conditions are provided such that the prediction error of the NUDREKF is exponentially mean square bounded. • Real path-following control tests for a holonomic Mecanum-wheeled robot validate the control solution. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive survey of the integration of machine learning techniques into robotic grasping, with a special emphasis on the challenges and advancements for space applications. The incorporation of artificial intelligence, particularly through deep learning, reinforcement learning, transfer learning, convolutional neural networks and recurrent neural networks, has significantly revolutionized robotic grasping. These advancements facilitate autonomous, efficient, and sophisticated manipulation in the challenging environment of outer space, transitioning from traditional mechanical grippers to sophisticated systems powered by advanced algorithms. This transition highlights the critical integration of sensory perception, grasp planning, and execution mechanisms, enhancing robots' capabilities to perceive, interact with, and manipulate objects with unprecedented precision and adaptability. The article meticulously outlines significant advancements achieved through the deployment of convolutional neural networks for visual information processing, RNNs for sequential decision-making, RL for autonomous strategy refinement, and transfer learning for leveraging pre-learned knowledge in novel tasks. These technologies address the unique challenges of space environments, such as varied textures, occlusions, microgravity conditions, and the sim-to-real gap, by enhancing sample efficiency, improving sim-to-real transfer capabilities, and integrating multimodal data for better object localization and pose estimation. Furthermore, the review explores the specific challenges faced in space robotic grasping, including handling varied textures and occlusions, adapting to unpredictable conditions, achieving real-time processing, and ensuring safety and reliability. It proposes future research directions focused on overcoming these hurdles, such as enhanced generalization through multimodal learning, robust sim-to-real transfer techniques, and the development of collaborative robotics and swarm intelligence. Critical to the development of ML models for robotic grasping are the roles of specialized datasets and simulation environments. Datasets like the Cornell Grasping Dataset and the Yale-CMU-Berkeley Object, along with simulation platforms such as Gazebo and PyBullet, provide essential resources for training, testing, and refining ML models. These tools enable researchers to simulate complex robotic systems and interactions within realistic environments, fostering rapid iterations on design and control strategies. In summary, this article offers in-depth insights into the progress, current challenges, and future prospects of machine learning techniques in robotic grasping for space exploration. It showcases significant strides made in the field and charts a path forward, emphasizing the need for innovative solutions to navigate the complexities of robotic manipulation in outer space. Through the strategic integration of advanced ML techniques, the development of adaptable and efficient robotic systems for space applications continues to advance, promising to unlock new possibilities in space exploration and beyond. • Surveys machine learning's application in space robotic grasping, noting key advances. • Highlights CNNs and RNNs' roles in robotic perception and action. • Discusses deep learning's evolution in autonomous space robotics. • Outlines future directions, emphasizing sim-to-real and multimodal learning. [ABSTRACT FROM AUTHOR]

The present study aimed to evaluate the safety of robot-assisted screw placement in 125 cases after introducing a spinal robotics system and to identify the situations where deviation was likely to occur. The subjects were 125 consecutive patients who underwent robotic-assisted screw placement using a spinal robotics system (Mazor X Stealth Edition, Medtronic) from April 2021 to January 2023. The 1048 screws placed with robotic assistance were evaluated. We investigated intraoperative adverse events of the robotics system and complications occurring within 30 days after surgery. We evaluated screw accuracy and deviation and compared them for vertebral levels, screw insertion methods (open traditional pedicle screw [Open-PS], cortical bone trajectory screw [CBT], percutaneous pedicle screw [PPS], and S2 alar iliac screw [S2AIS]), diagnosis, and phases of surgical cases. The deviation rate of robotic-assisted screw placement for spine surgery was 2.2%. Complications were reoperation due to implant-related neurological deficit in 0.8% and surgical site infection in 0.8%. There was significant difference in the deviation rate between vertebral levels. The deviation rate of the T1–T4 level was high at 10.0%. There was significant difference in the deviation rate between Open-PS, CBT, PPS, and S2AIS. The PPSs had a high deviation rate of 10.3%. The deviation rates were not significantly different between patients with and without deformity. The deviation rate did not change depending on the experience of surgical cases, and the deviation rate was favorable from the onset. Although the robotic-assisted screw placement was safe, we should be extra vigilant when placing screws in the upper thoracic region (deviation rate 10.0%) and when using PPSs (deviation rate 10.3%). [ABSTRACT FROM AUTHOR]

The manipulation of compliant objects by robotic systems remains a challenging task, largely due to their variable shapes and the complex, high-dimensional nature of their interaction dynamics. Traditional robotic manipulation strategies struggle with the accurate modeling and control necessary to handle such materials, especially in the presence of visual occlusions that frequently occur in dynamic environments. Meanwhile, for most unstructured environments, robots are required to have autonomous interactions with their surroundings. To solve the shape manipulation of compliant objects in an unstructured environment, we begin by exploring the regression-based algorithm of representing the high-dimensional configuration space of deformable objects in a compressed form that enables efficient and effective manipulation. Simultaneously, we address the issue of visual occlusions by proposing the integration of an adversarial network, enabling guiding the shaping task even with partial observations of the object. Afterwards, we propose a receding-time estimator to coordinate the robot action with the computed shape features while satisfying various performance criteria. Finally, model predictive controller is utilized to compute the robot's shaping motions subject to safety constraints. Detailed experiments are presented to evaluate the proposed manipulation framework. Our MPC framework utilizes the compressed representation and occlusion-compensated information to predict the object's behavior, while the multi-objective optimizer ensures that the resulting control actions meet multiple performance criteria. Through rigorous experimental validation, our approach demonstrates superior manipulation capabilities in scenarios with visual obstructions, outperforming existing methods in terms of precision and operational reliability. The findings highlight the potential of our integrated approach to significantly enhance the manipulation of compliant objects in real-world robotic applications. • A parametric shape descriptor to efficiently characterize 3D deformations based on online curve/surface fitting. • A robust shape prediction network based on adversarial neural networks to compensate visual occlusions. • An optimization-based estimator to approximate the deformation Jacobian matrix and satisfy various performance constraints. • An MPC-based controller to guide the shaping motions while simultaneously solving saturation, workspace, and obstacle constraints. [ABSTRACT FROM AUTHOR]

Imaging systems larger than the James Webb Space Telescope will enable unprecedented astronomy in the future. Larger aperture space-based imagers will also support next-generation Earth Observation missions for national security and disaster monitoring. However, launching and operating such large telescopes in the poses several practical challenges. A key challenge is that mirrors with apertures larger than 3 m cannot be monolithically manufactured so a segmented design is utilized to achieve primary mirror apertures. Even if larger mirrors could be made, it would be impossible to stow them in fairings of most existing and future launch vehicles. Folded-wing designs to deploy segmented primary mirrors, as done with the James Webb Space Telescope, are one approach to overcoming this volumetric challenge but inappropriate for 25 m apertures considered in this study. This paper presents the concept of operations and mission architectures/analysis for on-orbit assembly of a 25 m aperture telescope operating in the visible waveband of the electromagnetic spectrum capable of 1 m spatial resolution from geostationary orbit. Further, a technology demonstration roadmap towards maturing the robotic assembly technology stack is then presented as precursors to the 25 m imaging system. • Robotic on-orbit assembled large telescopes will enable next-gen imaging. • The paper designs a 25 m aperture space telescope. • In-orbit mass of telescope is ∼ 50 tonnes. • Robotic assembly is architected in a NewSpace lens. • Precursor technology demonstrations are then presented. [ABSTRACT FROM AUTHOR]

This paper presents a study on path planning for 6-DOF free-floating space robotic manipulators using Deep Deterministic Policy Gradient-based Reinforcement Learning. The focus is the development of a novel reward function tailored to address critical requirements for efficient and effective manipulation in space. These requirements include accurate pose alignment between the end-effector and the target, collision avoidance with both the target and other links of the manipulator, smoothing of joint velocities, adaptability to strong dynamic coupling between the manipulator and its base spacecraft due to high manipulator-spacecraft mass ratio, and resilience to noise in the state observations. Uniquely, the proposed reward function employs quaternions for orientation control to reduce pose misalignments and dynamic singularities, as opposed to traditional Euler angles. Our findings demonstrate that the Reinforcement Learning algorithm, when guided by this new reward function that integrates these enhancements and constraints, not only achieves the desired path planning objectives more efficiently but also exhibits faster convergence. Furthermore, the Reinforcement Learning successfully manages significant dynamic coupling effects caused by a high mass ratio between the robotic manipulator and the base spacecraft. Even under the challenge of noisy state observations, the trained agent successfully completes the path planning task, proving the Reinforcement Learning's applicability to real-space mission designs where the noise in observation is inevitable. The study highlights the critical role of reward function design in the Reinforcement Learning training process and its consequential impact on the solution quality, providing a solid foundation for future advancements in free-floating space robotic missions. [ABSTRACT FROM AUTHOR]

The exploration of small celestial bodies has been a very active field ever since the dawn of space exploration. However, traditional approaches based on monolithic landers allow only the area in the immediate vicinity to the landing spot to be examined. Using mobile systems like rovers allow for better coverage, but at considerable cost and complexity. In this work, we propose an approach based on swarms of small nonmaneuverable agents, equipped with a sensor package, that are launched from a base station and made to land across the entire surface of the asteroid or comet. The methodology is based on multilateration using small range-finding radio frequency sensors aboard the agents; this enables position determination relative to the base station. Through dynamics simulation, we show that this approach is feasible even in highly irregular gravity fields where closed-form solutions for orbital mechanics are not available, such as the case of comet 67P/Churyumov–Gerasimenko. We show a sensitivity analysis of the absolute position error of the agents which originated from range-measurement error. The surface coverage is evaluated against the numerosity of the swarm. Finally, we show that allowing for a subset of agents to follow a long-period orbit around the object enables better localization of the landed agents, thus increasing the overall performance. • We show a methodology for the exploration of small celestial bodies based on a swarm of sensorized agents. • The swarm is aware of the position of its agents via multilateration enabled by RF range-finding. • The methodology is simulated around the comet 67P/Churyumov–Gerasimenko. • Results show that the approach can cover more than 90% of the surface of the comet. [ABSTRACT FROM AUTHOR]

While personalized, 3D printed, craniomaxillofacial (CMF) fixation plates are available from several vendors, given the short time window for reconstructive surgery in either advanced stage cancer resection or trauma, fixation plates are most often manually bent by eye. A surgeon's manual bending of fixation plates may significantly delay these time-sensitive procedures as well as introduce work hardening damage to the plate due to repetitive bending at one or more locations. Unfortunately, revision surgery can follow work hardening-induced fatigue failure, stress-shielding-induced bone loss, or stress concentration-induced fixation device failure. Our group has developed a desktop automated fixation plate bending unit for the robotic bending of CMF fixation plates. Our automated plate bending device consists of a pair of modular jaws that precisely hold a fixation plate and iteratively bend/twist it based on geometric data imported from a plate bending plan developed using 3D patient data and a VSP (Virtual Surgical Planning) environment. Our prototype plate bender offers accurate plate bending in a time frame that is much reduced versus traditional manual plate bending. The design details, and performance of this device are documented in this study. [ABSTRACT FROM AUTHOR]

Advancements in bulk metallic glass thermoplastic forming unleash the potential to fabricate microscale metallic features with unparalleled precision and durability, i.e. microspine gripping structures for mobility platforms on planetary or in-space robots. Four designs of grippers were fabricated and rigorously tested. Coefficient of friction on four test surfaces were measured and compared to SiC paper and Velcro. The hardness and elasticity of the bulk metallic glasses make them a game-changer in the field, offering a superior alternative to conventional nonmetallic grippers. • Microspine grippers were manufactured via thermoplastic forming of bulk metallic glasses using silicon-on-insulator molds. • The microspines excelled on textured surfaces, where sharp points anchor on to pores or softer regions. • Mechanical behavior of microspines were studied using nanoindentor-based micro-compression and computer simulations. [ABSTRACT FROM AUTHOR]

Automated welding with robots is one of the core processes of modern manufacturing. Industrial robots are capable of continuously performing welding tasks under different working conditions in well-defined and structured environments. However, reprogramming in the plant is still necessary to deal with uncertainties due to a positioning error of the parts to be welded on the production line. This error can be due to several reasons, including dimensional quality of the parts, mechanical play, aging of the mechanisms, etc. In this sense, an automatic decision-making system is required to compensate for the measured error and avoid stopping the production line. In this work, we propose a controller based on the Enhanced Wagner-Hagras Interval Type-3 NSFLS-1 (EWH Type-3 NSFLS-1), a vision system and a laser beam incident on the plates to be welded to determine the misalignment that is corrected by a fuzzy controller. Type-3 fuzzy logic systems (T3 FLS) make it possible to model the effects of uncertainties and to minimize them by optimizing the parameters during the learning process of the fuzzy system. They can approximate any real continuous function on a compact set to arbitrary accuracy. In this article, several fuzzy logic algorithms were evaluated, resulting in the proposed EWH IT3 TSK NSFLS-1 controller being the best option since it presented the lowest error of 1.79e-09m to successfully reposition the robot. [ABSTRACT FROM AUTHOR]

This paper presents a two-loop control framework for robotic manipulator systems subject to state constraints and input saturation, which effectively integrates planning and control strategies. Namely, a stability controller is designed in the inner loop to address uncertainties and nonlinearities; an optimization-based generator is constructed in the outer loop to ensure that state and input constraints are obeyed while concurrently minimizing the convergence time. Furthermore, to dramatically the computational burden, the optimization-based generator in the outer loop is switched to a direct model-based generator when the tracking errors are sufficiently small. In this way, both a high tracking accuracy and fast dynamic response are obtained for constrained robotic manipulator systems with considerably lower computational burden. The superiority and effectiveness of the proposed structure are illustrated through comparative simulations and experiments. • A general and systematic constrained control scheme for robotic manipulators. • Nonlinearities, uncertainties, state and input constraints are considered. • Adaptive robust controller is designed for high tracking accuracy. • Optimization-based generator is designed for fast dynamic response. • Experiments on Franka robotic manipulator reveal the effectiveness. [ABSTRACT FROM AUTHOR]

Recently, the Internet of Things (IoT) technology has played a crucial role in our daily lives. It ensures the automatic transfer and response between various smart devices. Likewise, it enhances connections within networks and provides access to different information and data, making our lives easier. The innovation of the humanoid robot, which utilizes IoT technology, positively impacts our daily activities in many ways. However, compromising these robots may raise privacy and security concerns. This work aims to increase security awareness for robot users by highlighting and conducting common possible cyberattacks on Junior Zenbo robots using open, unsecured ports and weak Wi-Fi security, leading attackers to gain unauthorized access. In addition, the study intends to provide a comprehensive forensic guide that has been illustrated, detailing major Zenbo robot artifacts that can support forensic examiners in their investigations. [ABSTRACT FROM AUTHOR]

An efficient characterization of scientifically significant locations is essential prior to the return of humans to the Moon. The highest resolution imagery acquired from orbit of south-polar shadowed regions and other relevant locations remains, at best, an order of magnitude larger than the characteristic length of most of the robotic systems to be deployed. This hinders the planning and successful implementation of prospecting missions and poses a high risk for the traverse of robots and humans, diminishing the potential overall scientific and commercial return of any mission. We herein present the design of a lightweight, compact, autonomous, and reusable lunar reconnaissance drone capable of assisting other ground-based robotic assets, and eventually humans, in the characterization and high-resolution mapping (∼ 0.1 m/px) of particularly challenging and hard-to-access locations on the lunar surface. The proposed concept consists of two main subsystems: the drone and its service station. With a total combined wet mass of 100 kg, the system is capable of 11 flights without refueling the service station, enabling almost 9 km of accumulated flight distance. The deployment of such a system could significantly impact the efficiency of upcoming exploration missions, increasing the distance covered per day of exploration and significantly reducing the need for recurrent contacts with ground stations on Earth. • Sub-meter resolution data is required for an efficient lunar exploration. • Lightweight lunar aerobots can aid in the characterization of regions of interests. • Design is mostly based on space-proven, high-TRL, off-the-shelf components. • The presented design can map a range of 11 km with a total wet mass under 100 kg. [ABSTRACT FROM AUTHOR]

One of the most challenging robotic manipulator designs is finding an appropriate balance between the shaking force and shaking moment because this reduces vibrations. Several approaches have been introduced in the last decades; nevertheless, some assumptions must be established to make such a balance. In this paper, a dynamic balancing approach is proposed. The main novelty is the no dependence on specific trajectories to be executed by the manipulator, which allows finding a design with a similar tradeoff in the balancing under robot configuration changes. Also, the proposal incorporates mass distribution and link shape in a single design procedure. The proposal is stated as a constrained nonlinear optimization problem and applied to a hybrid serial-parallel robotic manipulator. The use of different bio-inspired algorithms and one gradient one in the solution of the balancing problem reveals that differential evolution finds the most suitable design. Besides, comparative simulation results of the obtained design with other design approaches show that the obtained design presents the most suitable tradeoff between the shaking force and the shaking moment when the manipulator executes tasks with different operating velocities. [ABSTRACT FROM AUTHOR]

In this paper, a Distributed Discrete-time Exponential Sliding Mode Consensus (DDESMC) protocol is proposed for the leader-follower consensus of a Discrete Multi-Agent System (DMAS). The proposed protocol ensures not only the minimal consensus effort and reaching time for the consensus among the agents but also the minimum consensus deviation in the order of O (T 3). The consensus stability of DMAS with the proposed protocol is analyzed using Lyapunov theory and the maximum number of reaching steps required for achieving the consensus among all agents is calculated. The proposed protocol is validated in a simulation and experimental setup comprised of multiple 2-Degree of Freedom (DOF) robotic arms where one of the robotic arms is real, and others are virtual. Further, the consensus performance compared with the existing protocol in the literature, and it is inferred that the proposed protocol outperforms it in terms of time and effort required to achieve the consensus and the deviation from the consensus. • A novel DDESMC protocol to achieve the leader-follower consensus of a Discrete Multi-Agent System. • Minimal consensus effort and minimum reaching time to achieve the consensus. • Consensus deviation of the order O (T 3) after achieving the consensus. • Calculation for maximum number of steps required for achieving consensus. • Simulation and experimental validation with exhaustive performance comparison on a DMAS consist of multiple Robotic Arms. [ABSTRACT FROM AUTHOR]

The use of robotic platforms with social capabilities is becoming increasingly common to support people's daily lives. Such systems are commonly referred to as Socially Assistive Robots (SAR). Many SARs focus on a human-machine interaction based on visual and sound information, which could limit access for users with sight, hearing, or cognitive impairments, as it is usual in health environments. In this paper, we report a qualitative evaluation of the accessibility of Inrobics Rehab platform, arehabilitation platform based on the NAO robot. From the main outcomes, we propose to augment communication channels in social rehabilitation robots with haptic devices. The main aim of this proposal is to increase the accessibility of the platforms by combining multimodal information provided by visual, sound, and tactil channels. [ABSTRACT FROM AUTHOR]

The healthcare sector as a whole has a high administrative burden. Whether through doctors, nurses, laboratories, financial departments, accounting, pharmaceutical departments, among other entities, it is essential that everyone connects to monitor the patient. However, the administrative burden associated with these processes is extremely high which makes the tasks of the health sector more difficult to carry out. It is in this sense that the implementation of a technology such as Robotic Process Automation (RPA) allows all these routines but necessary tasks to be performed by capable software "robots" that respect rules that will allow to respond to all needs. It was found in the literature that there are few works related to the implementation of RPA in medical and healthcare services. The objective of this work is focused on a literature review to identify how RPA contributes to medical and health services in a context of sustainability. This work analyzes some studies available in the literature, analyzing them from the perspective of the Triple Bottom Line (TBL) related to environmental, social and economic effects. Regarding the results of this study, it appears that there is still a lot of room to improve research in this area, for example, with regard to the development of implementation models, governance and also to optimize the efficiency of RPA, in medical and healthcare administrative services, taking into account the TBL of the concept of sustainability. [ABSTRACT FROM AUTHOR]

With the ever increasing number of active satellites in space, the rising demand for larger formations of small satellites and the commercialization of the space industry (so-called New Space), the realization of manufacturing processes in orbit comes closer to reality. Reducing launch costs and risks, allowing for faster on-demand deployment of individually configured satellites as well as the prospect for possible on-orbit servicing for satellites makes the idea of realizing an in-orbit factory promising. In this paper, we present a novel approach to an in-orbit factory of small satellites covering a digital process twin, AI-based fault detection, and teleoperated robot-control, which are being researched as part of the "AI-enabled Cyber–Physical In-Orbit Factory" project. In addition to the integration of modern automation and Industry 4.0 production approaches, the question of how artificial intelligence (AI) and learning approaches can be used to make the production process more robust, fault-tolerant and autonomous is addressed. This lays the foundation for a later realization of satellite production in space in the form of an in-orbit factory. Central aspect is the development of a robotic AIT (Assembly, Integration and Testing) system where a small satellite could be assembled by a manipulator robot from modular subsystems. Approaches developed to improving this production process with AI include employing neural networks for optical and electrical fault detection of components. Force sensitive measuring and motion training helps to deal with uncertainties and tolerances during assembly. An AI-guided teleoperated control of the robot arm allows for human intervention while a Digital Process Twin represents process data and provides supervision during the whole production process. Approaches and results towards automated satellite production are presented in detail. • An automated production system for modular small satellites is proposed. • Central element is a force-sensitive robotic assembly, integration & testing system. • AI and machine learning approaches are applied to make the process more robust. • A digital process twin supervises the autonomous production. • A multi-modal shared control approach allows human teleoperation if required. [ABSTRACT FROM AUTHOR]

Antimicrobial resistance (AMR) has become a global health crisis in need of novel solutions. To this end, antibiotic combination therapies, which combine multiple antibiotics for treatment, have attracted significant attention as a potential approach for combating AMR. To facilitate advances in antibiotic combination therapies, most notably in investigating antibiotic interactions and identifying synergistic antibiotic combinations however, there remains a need for automated high-throughput platforms that can create and examine antibiotic combinations on-demand, at scale, and with minimal reagent consumption. To address these challenges, we have developed a Robotic-Printed Combinatorial Droplet (RoboDrop) platform by integrating a programmable droplet microfluidic device that generates antibiotic combinations in nanoliter droplets in automation, a robotic arm that arranges the droplets in an array, and a camera that images the array of thousands of droplets in parallel. We further implement a resazurin-based bacterial viability assay to accelerate our antibiotic combination testing. As a demonstration, we use RoboDrop to corroborate two pairs of antibiotics with known interactions and subsequently identify a new synergistic combination of cefsulodin, penicillin, and oxacillin against a model E. coli strain. We therefore envision RoboDrop becoming a useful tool to efficiently identify new synergistic antibiotic combinations toward combating AMR. We report the robotic-printed combinatorial droplet platform as a potential solution to automate and miniaturize the screening of antibiotic combinations to identify synergistic combinations for combating antimicrobial resistance. [Display omitted] [ABSTRACT FROM AUTHOR]

The development of advanced robotic systems that can be operated effortlessly by humans is crucial for expanding the capabilities of human-robot interaction and collaboration. In this work, we propose a system that enables real-time and non-invasive tracking of the human wrist's position in three-dimensional space to facilitate an intuitive control of a remotely placed robotic arm, as an improvement towards efficient control mechanism. We leverage color segmentation based human wrist tracking in the viewing plane augmented with a stereo depth estimation for synthesising the localized wrist in the Cartesian space. The centroid of the segmented image is determined to generate the precise coordinates. To remove the variance in the estimated centroid, the trajectory of the localized points is modelled by Kalman filtering approach. Finally, the coordinates are transformed using Inverse Imaging Model and transmitted to an edge device (close to the robot), which then replicates the motion at the robot end using inverse kinematics. The results show a high degree of accuracy in tracking the wrist's movements, enabling precise and natural control over the robotic arm's motion. The system's adaptability to various environments and user populations is validated through tests with diverse subjects. With its high accuracy, real-time performance, the proposed approach holds great potential to enhance the efficiency and safety of human-robot collaborative applications across multiple domains. [ABSTRACT FROM AUTHOR]

This study evaluated the accuracy of the cup alignment angles and spatial cup positioning on computed tomography (CT) images in patients with osteoarthritis secondary to developmental dysplasia of the hip (DDH) who underwent total hip arthroplasty (THA) using a minimally invasive technique via an anterolateral approach in the supine position according to whether a robotic arm-assisted system or a CT-based navigation system was used. We reviewed 60 robotic arm-assisted (RA)-THA cases and 174 navigation-assisted (NA)-THA cases. After propensity score matching, there were 52 hips in each group. Postoperative cup alignment angles and position were assessed by superimposition of a three-dimensional cup template onto the actual implanted cup using postoperative CT images with pelvic coordinates matching the preoperative planning. The mean absolute error of the inclination angle and the anteversion angle between the preoperative planning and the postoperative measurement was significantly smaller in the RA-THA group (inclination, 1.1° ± 0.9; anteversion, 1.3° ± 1.0) than in the NA-THA group (inclination, 2.2° ± 1.5; anteversion, 3.3° ± 2.5). For acetabular cup positioning, the mean discrepancy between the preoperative planning and the postoperative measurement was 1.3 ± 1.3 mm on the transverse axis, 2.0 ± 2.0 mm on the longitudinal axis, and 1.3 ± 1.7 mm on the sagittal axis in the RA-THA group and 1.6 ± 1.4 mm, 2.6 ± 2.3 mm, and 1.8 ± 1.3 mm, respectively, in the NA-THA group. High precision of cup positioning was observed in both groups with no statistically significant difference. Robotic arm-assisted THA using a minimally invasive technique via an anterolateral approach in the supine position allows accurate cup placement in patients with DDH. [ABSTRACT FROM AUTHOR]

Human desire to explore Mars has persisted throughout history, and especially, it is required to achieve precise and safe landings on the Martian surface. The atmospheric entry phase plays a crucial role in ensuring mission success, and active research on entry guidance methods continues. This paper presents a comparative study between the two primary types of entry guidance methods: (1) path planning-tracking and (2) predictor–corrector methods by applying them to solve two distinct Mars atmospheric entry guidance problems: (a) robotic entry and (b) human entry missions. Optimization approaches are adopted for the path planning-tracking method. After generating an optimal reference trajectory based on a mission concept-defined cost function, the receding horizon control method is employed to minimize tracking errors. In the predictor–corrector method, two parameterized entry guidance approaches are explored and analyzed. A well-known numerical predictor–corrector method that utilizes a parameterized bank angle profile for entry trajectory generation is tested to demonstrate its robustness against random dispersions. On the other hand, another predictor–corrector type guidance method that assumes a polynomial shape to shape an altitude profile over range-to-go is adopted to show its range control capability. Numerical simulations reveal the pros and cons of each type of entry guidance method, and a suitable guidance method for each Mars entry mission is identified depending on the mission concept and requirements. The analysis results provide future work. • A comparative study between path planning-tracking and predictor-corrector guidance methods. • Mars atmospheric entry problem for robotic and human landing missions. • Optimization approaches for reference trajectory generation and path tracking. • Numerical predictor-corrector based on parameterized bank angle and altitude profiles. [ABSTRACT FROM AUTHOR]

This paper presents a control method for trajectory tracking of a robotic manipulator, subject to practical constraints and uncertainties. The proposed method is established upon an adaptive backstepping procedure incorporating a tangent-type barrier Lyapunov function and it preserves some important metrics of trajectory tracking such as fast and user-defined settling time response and robustness against actuation faults and unknown control gain. The proposed design maintains the system trajectory within a prescribed performance bound and relaxes the assumption of the bounded initial condition. These salient features preserve the system within a safety bound and, consequently, guarantee the system stability and safety. The performance of the proposed control method is validated on a 3-DOF PUMA 560 robotic manipulator benchmark model, with different operation scenarios. The simulation results confirm the effectiveness and robustness of the proposed control method. • This paper presents a prescribed trajectory tracking control of a robotic manipulator, subject to practical constraints. • The proposed design preserves user-defined settling time and unknown control gain. • The method maintains the system trajectory within a prescribed performance bound and relaxes the bounded initial condition. • A computationally efficient core function with an unknown scalar gain is used to enable real-time implementation in practice. • The performance of the method is validated on a 3-DOF PUMA 560 robotic manipulator benchmark model. [ABSTRACT FROM AUTHOR]

In this study, a three degrees of freedom (3 DOF) rigid-link robotic manipulator (RLM) has been simulated by using the Simscape model and the mathematical model derived by Lagrange method. The robot arm has been regulated by an Optimized PID Controller to achieve better tracking performance and reasonable robustness against disturbances and payload uncertainty. To optimize the controller, a novel nature-inspired Golden Jackal Optimization (GJO) algorithm has been used due to its efficient exploration that increases the diversity of the released solutions and its exploitation schemes which enhance the best-explored solutions. The tuning process has utilized a Lyapunov stability function as the objective function (OF) and the efficacy of the proposed algorithm is evaluated through a comprehensive comparison with various state-of-the-art metaheuristic techniques such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Jellyfish Search Optimizer (JSO), Whale Optimization Algorithm (WOA), Arithmetic Optimization Algorithm (AOA) and Sine Cosine Algorithm (SCA). The assessment has been conducted on benchmark error-based functions, providing rigorous testing and validation of the algorithm's performance. Furthermore, the performance evaluation has focused on the system's robustness against disturbances, noise, and variations in the payload mass, particularly in the context of Pick and Place (PNP) industrial tasks. The results of simulation have demonstrated that the optimized system, employing the Lyapunov function, demonstrated superior performance in minimizing the objective function value compared to other benchmark functions. [Display omitted] • Efficient 3-DOF robotic model using MATLAB Simscape, enabling precise representation and efficient simulation. • Utilizing the new approach of Lyapunov function as the objective function in the optimization process. • Comparing the performance of the GJO algorithm with other recent optimization algorithms commonly used in the literature. • Comprehensive performance analysis to offer valuable insights into the controller's adaptability in real-world scenarios. [ABSTRACT FROM AUTHOR]

We propose a novel solution for generating robot tool paths to achieve uniform surface polishing of free-form poly-surfaces, addressing the limitations of imperfect surface reconstruction in real industrial settings and of abruptly varying curvatures. Existing approaches assume perfect 3D surface knowledge and overlook pieces with abrupt changes in principal curvatures, leading to unsatisfactory outcomes like non-smooth paths and incomplete coverage. Our research aims to develop an algorithm that adapts the polishing path for uniform coverage despite imperfect reconstruction or aggressive varying curvatures of the objects to be polished. Users can control the path points density, overlapping, edge proximity, and scanning direction. By segmenting the surface based on curvature similarity and connecting the separately computed trajectories, we minimize polishing time and achieve optimal polishing quality of free-form poly-surfaces in real industrial settings. [ABSTRACT FROM AUTHOR]

Moving from Industry 4.0 to Industry 5.0 marks a substantial shift in the way technology is incorporated into workplace design. In Industry 4.0, the emphasis was on automating the production process to maximize efficiency and output. However, with Industry 5.0, the focus has shifted towards placing people at the heart of the production process. This involves creating workspaces that prioritize human comfort and productivity and integrating technology that enhances human abilities. Collaborative robots, or cobots, are a critical technology in this transition as they work alongside humans to improve efficiency while enabling greater human involvement. However, to make the most of cobots, it is necessary to design workspaces that optimize human comfort and productivity, by considering the needs and preferences of both human and robotic resources. A promising strategy for achieving this goal is the implementation of a dynamic multi-objective task allocation system. The method proposed in this work uses physiological and performance data to assess the well-being of human operators and reallocates tasks dynamically to avoid overworking or fatiguing them. This represents a significant step towards creating production environments that prioritize the well-being and productivity of human workers. [ABSTRACT FROM AUTHOR]

The rapid advancement of robotics and artificial intelligence paves the way for collaborative robotics to revolutionise industrial operations. Collaborative robotics can bring significant improvements in productivity, efficiency, and safety. Recently, the concept of Symbiotic Human-Robot Collaboration (SHRC) has emerged within the field of collaborative robotics, emphasising the seamless and adaptive integration of human and robotic capabilities within a shared workspace to optimise overall production performance. However, existing collaborative systems frequently face difficulties in establishing dynamic relationships with humans and adapting to evolving conditions, ultimately restricting their overall potential. This paper investigates the essential features required for collaborative workstations to enable SHRC. Moreover, with the aim of fully harnessing the power of collaborative robotics, the paper introduces a design methodology that supports the development of symbiotic workstations for specific assembly operations. A real-world case study is provided, demonstrating the practical implementation and benefits of the proposed design approach. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study on the advancements and potential of industrial robots in the field of additive manufacturing of thermoplastics. As the manufacturing industry continues to embrace Industry 4.0 and smart manufacturing principles, the integration of robotic systems has gained significant attention due to their flexibility, precision, and automation capabilities. This article explores the utilization of industrial robots in achieving free-form fabrication, particularly focusing on the additive manufacturing process for composite materials. It looks into the hardware setup, software systems configuration, and platform development that combines FDM with robotic arm technologies. It discusses the merits, limitations, and prospective uses of freeform additive manufacturing and the interoperability between traditional additive manufacturing and robotic additive manufacturing. The experimental results show that this integration is feasible and advantageous, with better process flexibility and production capacities when compared to typical FDM systems. [ABSTRACT FROM AUTHOR]

The manufacturing landscape is rapidly evolving, being characterized by the emergence of Industry 5.0, leading to a growing recognition of the importance of a human-centered, sustainable and resilient approach. In response to these needs, collaborative robotics has emerged as a promising solution to enhance flexibility in industrial operations, while ensuring a higher level of safety. Cobots enable human-robot collaboration without the need for physical barriers, reduce the footprint of the robotic station, and foster a closer working relationship between humans and machines. This article focuses on an application of collaborative robotics in end-of-line industrial operations, specifically in the unloading of products from an automatic machine. By managing repetitive tasks, such as placing the trays near the end of the line of the automatic machine, waiting for them to be filled, and placing them on a trolley, which will then be moved by the human operator, the robot significantly improves efficiency. The proposed solution involves alternating the placement of trays in two rack trolleys, allowing the worker to replace the full trolleys with empty ones at his own timing. The paper examines the potential advantages of using a human-centric approach that emphasizes operator empowerment and human oversight: the collaborative robotic cell and the automation implemented free the workers from the low value-added operations and allow them to oversee and manage multiple machines simultaneously, thus enhancing productivity. The approach behind this proposal is to keep robotic tasks simple, easily reprogrammable and modular, while empowering the human operator to engage in supervision, decision-making, and problem-solving tasks. This collaboration allows the operator to assume a supervisory role, organizing production based on identified needs, and elevating their involvement to production line management tasks. [ABSTRACT FROM AUTHOR]

Human-robot collaboration (HRC) is one of the paradigms of the emerging Industry 5.0, aimed at supporting humans in production processes. However, the introduction of an industrial robotic system in close contact with a human opens new challenges not only for safety but also for ergonomics. This paper aims to study whether the introduction of a collaborative robot into an assembly process can support the human operator not only physically but also cognitively. To address this research question, shifts of a repetitive assembly process were implemented in both manual and HRC settings. The two settings were compared over time by analyzing the evolution of generated process failures and physiological response, revealing potential differences in process quality and operator stress. [ABSTRACT FROM AUTHOR]

As a digital environment of interconnected virtual ecosystems driven by measured and synthesized data, the Metaverse has so far been mostly considered from its gaming perspective that closely aligns with online edutainment. Although it is still in its infancy and more research as well as standardization efforts remain to be done, the Metaverse could provide considerable advantages for smart robotized applications in the industry. Workflow efficiency, collective decision enrichment even for executives, as well as a natural, resilient, and sustainable robotized assistance for the workforce are potential advantages. Hence, the Metaverse could consolidate the connection between Industry 4.0 and Industry 5.0. This paper identifies and puts forward potential advantages of the Metaverse for robotized applications and highlights how these advantages support goals pursued by the Industry 4.0 and Industry 5.0 visions. [ABSTRACT FROM AUTHOR]

Human robot collaboration is becoming the norm in the workplace, due to the benefits robots can bring to efficiency and production. However, this creates highly complex and dynamic workplaces that human operators need to adapt to. Industry 5.0 promotes the use of robotics and smart technologies in a more human-centric way. However, research on how operators are affected by those changes is needed to better understand how to move towards human-centricity. As such, an experimental study was designed and performed on human robot collaborative assembly. The main aim was to investigate the correlation between cognitive load and quality due to collaboration. Here, the preliminary results of the experimental study are presented in order to remark relevant states influencing work allocation. The results showcased the need for better training and more knowledge for the operators, as well as involving operators in process and workplace design. This study helps contribute knowledge on robot implementation and process design for human robot collaboration for both researchers and operations management, as it showcases the need to involve operators in those steps due to the feedback they can provide due to their experience. [ABSTRACT FROM AUTHOR]

This paper explores the qualification of a digital twin (DT) for a mobile manipulator (MOMA) in industrial applications. We discuss the development of different DT models based on various industrial needs and highlight the dependence of model accuracy on online sensor precision. Limitations of DTs for MOMA are examined, including challenges in respecting qualifiers due to the inability to incorporate unstructured aspects of the factory environment. Through a case study and some examples, we show the latent potential and limitations of DTs for MOMA in industrial contexts. The challenges of fidelity, real-time operation, and environment modeling are discussed. It is emphasized that creating a true digital twin of a mobile manipulator is hindered by the inability to include the complete surrounding environment. Recommendations for future research focus on addressing these limitations to enhance the effectiveness of DTs for MOMA in Industry 4.0 and smart manufacturing. [ABSTRACT FROM AUTHOR]

The convergence of cloud and edge computing, along with distributed AI and the latest 5G/6G communications, is revolutionizing collaboration, connectivity, and interaction. These digital advancements pave the way for a new era of AI-powered robots, enabling them to navigate unfamiliar scenarios and adapt in the long term by seamlessly engaging with the digital realm. Consequently, these innovative applications generate diverse, continuous, and rapidly evolving data transmission requirements that traditional network resource management struggles to satisfy in terms of Quality of Service (QoS). In this paper, we take a step beyond focusing solely on network-side traffic engineering efforts. Instead, we explore the potential of application-side traffic shaping within non-public networks to address these demanding transmission needs. Within the framework of optimizing Quality of Experience, we discuss on how the 5G-ERA project focuses on a multi-domain learning process for autonomous robotics using an intent-based networking approach for optimized resource management within the network validated by the use cases within the project enabling the transition from Industry 4.0 to Industry 5.0. Our central aim is to efficiently control and direct data traffic within the confines of the private network, ensuring it aligns with predefined objectives. While this methodology can be applied in various contexts, it is essential to establish precise intentions rooted in domain expertise. This innovative approach serves as a valuable complement to conventional network resource management methods typically employed at the network infrastructure level. [ABSTRACT FROM AUTHOR]

The development of autonomous robotic platforms for space applications such as satellites, robotic manipulators and rovers has driven the need for reliable control and navigation techniques. Simultaneous Localization and Mapping (SLAM) algorithms can be employed to determine the position and orientation of robotic platforms while generating a map of an unknown hostile environment. This article presents the successful integration of ORB-SLAM2, a localization algorithm, with Voxblox, an accurate three-dimensional mapping package. This integration aims to establish a pathway for occupancy maps to be generated from feature detection algorithms. Furthermore, virtual reality is presented as an innovative solution for testing the performance of this algorithm integration in a space environment. Providing a novel simulation environment with an opportunity to diversify applications. The effectiveness of this integration and SLAM accuracy was compared to a truth model extracted from the virtual reality environment. The accuracy is dependent on the number of observed and matched features in a given frame and are intrinsic characteristic of features in the environment. The robustness of the integration is also examined through the implementation of sensor inputs in the form of stereo and RGB-D cameras. The algorithms' integration and embedding into virtual reality allows for further developments of SLAM algorithms, to improve accuracy and robustness for autonomous navigation in any virtual environment, and extend robotic simulations and visualizations. [ABSTRACT FROM AUTHOR]

Fighter aircraft, the emblem of modern military aviation, have decisively shaped the landscape of contemporary warfare. These machines are purpose-built and equipped to carry out a variety of missions. Mission planning is a common practice, usually done before the aircraft takes off. However, the volatile nature of warfare poses a major challenge to the precise execution of preplanned missions. If the established flight path needs to be adjusted during a mission, a new path must be created and the mission completed using the newly derived flight path. In this study, we present a novel approach to path planning using a modified RRT connect algorithm. By considering the nearest nodes in two trees starting from Tinit and Tgoal, we use a constrained sampling strategy within a bounded environment. This iterative process creates a tree between the current and target locations and a path is extracted using the A* algorithm. The proposed method aims to omit samples in regions where the passage of the aircraft is infeasible or costly, resulting in a smoother trajectory. Experiments in different scenarios have shown that the method consistently delivers smooth routes. This research demonstrates the potential of our approach to improve the adaptability and performance of mission-critical aircraft in dynamic environments. [ABSTRACT FROM AUTHOR]

This paper proposes a new adaptive super-twisting global integral terminal sliding mode control algorithm for the trajectory tracking of autonomous robotic manipulators with uncertain parameters, unknown disturbances, and actuator faults. Firstly, a novel global integral terminal sliding mode surface is designed to ensure that the tracking errors of autonomous robotic manipulators converge to zero in finite time and the global robustness of the system is also enhanced. Then a new adaptive method is devised to deal with the adverse effect of nonlinear uncertainty. To suppress the chattering phenomenon, the adaptive super-twisting algorithm is used in this paper, which can ensure that the control torque is a continuous input signal. Based on the adaptive mechanism, the adaptive super-twisting global integral terminal sliding mode controller is developed to provide superior control performance. The stability analysis of the system is demonstrated by using the Lyapunov method. Ultimately, the effectiveness of the control scheme is confirmed by a simulation study. • A novel adaptive finite-time global integral terminal sliding mode control scheme is designed. • A novel global integral terminal sliding mode surface is proposed to ensure faster convergence and better global robustness. • A novel adaptive law is designed to adjust the estimation parameters according to the change of system state to avoid overestimation. • The actuator fault is considered to make the proposed control logic more suitable for practical applications. [ABSTRACT FROM AUTHOR]

This study presents the design of an adaptive event-driven controller for solving the trajectory tracking problem of a composite robotic device made up of a three-dimensional Cartesian and a parallel Delta robot. The proposed composite device has a mathematical model satisfying a standard Lagrangian structure affected by modeling uncertainties and external perturbations. The adaptive gain of the controller is considered to enforce the convergence of the tracking error while the state bounds are satisfied. The barrier Lyapunov function addresses the preconceived state constraints for both robotic devices by designing a time-varying gain that guarantees the ultimate boundedness of the tracking error under the effect of external perturbations. The event-driven approach considers that the Cartesian robot is moving into a predefined invariant zone near to the origin. In contrast, the delta robot can complete the tracking problem once the end-effector is inside the given zone. The suggested controller was evaluated using a virtual representation of the composite robotic device showing better tracking performance (while the restrictions are satisfied) than the performances obtained with the traditional linear state feedback controllers. Analyzing the mean square error and its integral led to confirming the benefits of using the adaptive barrier control. • Design of an adaptive event-driven controller for solving the trajectory-tracking problem of a composite robotic device. • The composite device has a model satisfying a standard Lagrangian affected by modeling uncertainties and external perturbations. • The adaptive gain of the controller enforces the convergence of the tracking error while the state bounds are satisfied. • The barrier Lyapunov function addresses the preconceived state constraints for the robotic devices with the time-varying gain. • The event-driven approach considers that the robot is moving into a predefined invariant zone near the origin. [ABSTRACT FROM AUTHOR]

In 2021 the Modular Mechatronics Infrastructure (MMI) was introduced as a solution to reduce weight, costs, and development time in robotic planetary missions. With standardized interfaces and multi-functional elements, this modular approach is planned to be used more often in sustainable exploration activities on the Moon and Mars. The German multi-robot research project "Autonomous Robotic Networks to Help Modern Societies (ARCHES)" has explored this concept with the use of various collaborative robotic assets which have their capabilities extended by the MMI. Different scientific payloads, engineering infrastructure modules, and specific purpose tools can be integrated to and manipulated by a robotic arm and a standardized electromechanical docking-interface. Throughout the MMI's design and implementation phase the performed preliminary tests confirmed that the different systems of the robotic cooperative team such as the Docking Interface System (DIS), the Power Management System (PMS), and the Data Communication System (DCS) functioned successfully. During the summer of 2022 a Demonstration Mission on Mount Etna (Sicily, Italy) was carried out as part of the ARCHES Project. This field scenario allowed the validation of the robotics systems in an analogue harsh environment and the confirmation of enhanced operations with the application of this modular method. Among the numerous activities performed in this volcanic terrain there are the efficient assembling of the Low Frequency Array (LOFAR) network, the energy-saving and reduced complexity of a detached Laser Induced Breakdown Spectroscopy (LIBS) module, and the uninterrupted powered operation between modules when switching between different power sources. The field data collected during this analogue campaign provided important outcomes for the modular robotics application. Modular and autonomous robots certainly benefit from their versatility, re-usability, less complex systems, reduced requirements for space qualification, and lower risks for the mission. These characteristics will ensure that long duration and complex robotic planetary endeavours are not as challenging as they used to be in the past. • Use of standardized interfaces and modular architectures in the field of robotics. • Modularity to extend the capabilities of robotic units. • Modular Mechatronics Infrastructure allows scientific tasks to be performed in a Moon Analogue environment. • Modularity in Robotics as solution for sustainable planetary exploration. [ABSTRACT FROM AUTHOR]

The flapping-wing technology has emerged recently in the application of unmanned aerial robotics for autonomous flight, control, inspection, monitoring, and manipulation. Despite the advances in applications and outdoor manual flights (open-loop control), closed-loop control is yet to be investigated. This work presents a nonlinear optimal closed-loop control design via the state-dependent Riccati equation (SDRE) for a flapping-wing flying robot (FWFR). Considering that the dynamic modeling of the flapping-wing robot is complex, a proper model for the implementation of nonlinear control methods is demanded. This work proposes an alternative approach to deliver an equivalent dynamic for the translation of the system and a simplified model for orientation, to find equivalent dynamics for the whole system. The objective is to see the effect of flapping (periodic oscillation) on behavior through a simple model in simulation. Then the SDRE controller is applied to the derived model and implemented in simulations and experiments. The robot bird is a 1.6 m wingspan flapping-wing system (six-degree-of-freedom robot) with four actuators, three in the tail, and one as the flapping input. The underactuated system has been controlled successfully in position and orientation. The control loop is closed by the motion capture system in the indoor test bed where the experiments of flight have been successfully done. [Display omitted] • Equivalent dynamic is used to build the model of the flapping-wing flying robot (FWFR). • The state-dependent Riccati equation (SDRE) is applied to the flapping-wing robot, with onboard computations. • Nonlinear optimal controller, the SDRE, is simulated and compared with experiments for the system. • Closed-loop flight control was done in a confined indoor space. • A new three-degree-of-freedom tail is presented for the FWFR. [ABSTRACT FROM AUTHOR]

In order to establish the dynamic equations of the rigid-flexible coupled robotic mechanisms with large beam-deformations over the horizontal plane, a thorough modeling technique founded on the virtual work concept has been proposed. Based on the transformed full-actuated model, the predefined-time robust sliding mode control strategy is developed to track the prescribed angular positions of the rigid-flexible coupled robotic mechanisms. In addition to compensating the time-varying external disturbances, the nonlinear disturbance observer is also integrated and resolved into predefined-time robust sliding mode schemes. By introducing the virtual input, the robust linear quadratic state feedback controller is developed to eliminate the tracking errors and vibration modes of the rigid-flexible coupled robotic mechanisms simultaneously. Furthermore, it is proved that the tracking errors and vibration modes can be uniformly stable under the developed scheme based on Lyapunov's stability theory. Finally, three numerical examples are conducted to access the effectiveness and excellence of the presented nonlinear disturbance observer-based predefined-time robust sliding mode and robust linear quadratic state feedback strategies in comparison to nonlinear disturbance observer-based backstepping control and slide mode control schemes. [ABSTRACT FROM AUTHOR]

• A generic non-linear post-capture detumble trajectory optimization formulation capable of including joint, base, and end-effector wrench limits. • A quaternion-based linearization of free-floating multibody system for trajectory tracking and stabilization. • A Lyapunov-based Region of Attraction analysis of the tracking controller. Recent increase in space debris combined with the increase in the number of satellites launched has created an increased risk of collisions. The effects of the increased risk can be seen in the form of an increased number of near misses in recent years. The use of robotic manipulators has been suggested for Active Debris Removal (ADR) to reduce the risk of potential future collisions that generate more debris in the orbits around Earth. Compared to other ADR methods, robotic manipulators provide increased versatility as they can be reused for On-Orbit Servicing as well as On-Orbit Assembly missions. A robotic ADR operation consists of three phases: Approach, Capture, and Detumble. This paper provides a method for performing feedback-based stabilization of post-capture detumble trajectories of the chaser-debris system. The approach presented here uses Time-Varying Linear Quadratic Regulator (TVLQR) for stabilization along the detumble trajectory. The contributions of this paper are as follows: A quaternion-based linearization method for multibody systems with a free-floating base, TVLQR for stabilizing the optimal detumble trajectory, and a probabilistic Region of Attraction analysis of the resulting closed-loop system. The estimated Region of Attraction could serve as the goal for the capture controller thus allowing for controller composition through ADR phases while guaranteeing stability and successful detumble. [ABSTRACT FROM AUTHOR]

The primary motivation of this paper is to present a compliant and cost-effective solution for object transfer between human and robot. The application prospect of this study is robot–human collaboration in manufacturing. To achieve above goals, a novel modular 3-axis force sensor is proposed for the grasping system to achieve interactive force sensing. Compliant object transfer control strategy, which is composed of incremental force control mode and gravity balance control mode, is proposed for object transfer between human and robot. A prototype of underactuated grasping system which is mounted on the proposed modular 3-axis force sensor is fabricated to investigate the effectiveness of the proposed interactive control strategy. Experimental results reveal that the incremental force control mode is suitable for the lighter objects with a higher interactive sensitivity. For transferring heavier objects, the gravity balance control mode is more suitable. In gravity balance control mode, the human hand could achieve a quasi-static equilibrium with the object, and achieve a compliant transfer operation. Due to the above characteristic, the proposed control strategy has the potentials to enhance the object transfer compliance and safety in the human–robot object transfer process. • Present an ultra-cost-effective modular 3-axis force sensor. • Present a compliant object transfer control strategy for interactive robotic applications. • The proposed control strategies have been validated experimentally. • Providing a new approach for developing multi-axis measurement instruments. [ABSTRACT FROM AUTHOR]

The use of modular, yet reusable and interchangeable components offers flexibility for system designers and thus leads to a higher degree of reconfigurability with cost effective, adaptable and scalable solutions. The development of a robotic system remains, however, a complex task with increasing demands depending on the targeted capabilities, functionalities and operational constraints of the robot. This requires expert knowledge in mechanical, electrical and software engineering, as well as tools that can collect, process and use relevant data. As a result, the amount of available and managed information is increasing, while its relevance, usability, and knowledge that can be extracted from it are consequently decreasing: a situation described as information overload paradox. The design of a modular system can be based on the use of standardized modules with universal interconnects, but it should be aligned with a strong formalism, e. g. the semantic representation of components, to allow for a better management of the overall complexity. An ontology-based knowledge representation, for instance, which provides domain and application specific knowledge for the design of robotic systems, can be used as a method of storing and sharing data in a uniform, machine-readable and standardized way. In this paper, we introduce the Knowledge-based Open Robot voCabulary as Utility Toolkit (korcut) as a core component to support the ontology-driven development of robotic systems as part of a reference implementation of the Q-Rock development cycle. Q-Rock gathers methods to assist users in designing robot configuration by: (a) automatically exploring robot capabilities based on robot hardware, (b) proposing robot designs to meet the user's needs, (c) refining the proposed robot designs. We use korcut to improve the robot design process in orbital and planetary robotics from the requirements definition to the development phases of complex modular robotic structures (e. g. composition of an unmanned ground vehicle with robotic arms). This is implemented by embedding semantic component descriptions in a state-of-the-art open-source 3D modeling software. Furthermore, the korcut ontology family includes various sub-ontologies to address specific space-related tasks, such as the modeling of a Standard Interconnect (SI) for On Orbit Services (OOS) and Orbital Factory. The ontology design follows criteria that have been derived from a survey conducted as part of this work. This paper also covers an evaluation of its current applications from a methodological, knowledge representation, and software tool perspectives. In the application part, we introduce the use of the ontology to model a multi-functional SI that enables mechanical connections between various (modular) robotic components, as well as transfer of power and data. Finally, we provide a critical analysis of our work and outline its future work. • The development of complex robotic systems in space domain needs expert knowledge. • Increasing information about a robotic system can lead to an information paradox. • The information paradox can be improved by standardized and inferable knowledge. • Ontology is used in a case study to facilitate the robot development workflow. • Ontology is used to provide a metric for evaluating a space interface. [ABSTRACT FROM AUTHOR]

On-Orbit-Servicing missions aim to improve the operative life of orbiting satellites. Among the studied technologies, the most promising are satellites equipped with one or more robotic arms (space robots). One robotic arm might be employed to establish a rigid connection with the target satellite, while the other performs the servicing operations. Nowadays, the control of the capture tool is managed by the computer of the space robot. However, it is possible to distribute the control tasks between the capture tool and the space robot. For this purpose, this paper presents the development of the SMArt Capture Kit (SMACK). SMACK is a standalone capture system equipped with sensors, actuators, and an integrated computer. In this fashion, the capture tool executes the guidance and navigation algorithms, and the control of the actuators; so that the space robot acts as the "positioning" system for the capture tool. The paper provides a description of all the subsystems of SMACK and their experimental validation. The tests of SMACK at the system level, confirm its capabilities to autonomously perform the capture the target interface. • Smart capture tool. • Autonomous tool with integrated sensors, actuators and computer. • Distributed control. • Use of fuzzy logic to develop the model of a custom sensor. • Use of fuzzy logic to create the approach trajectory. [ABSTRACT FROM AUTHOR]

• Most agriculture robots and automated machines are still in development. • Occupational risks can and should be assessed of these emerging technologies. • NIOSH is working to better understand and mitigate these risks. Introduction : A variety of factors are driving the development of robotics and automation in the agriculture industry including the nature of work, workforce shortages, and a variety of economic, climatic, technologic, political, and social factors. While some new robotics and automated machines are available commercially, most are still being developed. This provides occupational safety and health researchers an unprecedented opportunity to mitigate risks and benefit the health and safety of agriculture workers. Method: The NIOSH Office of Agriculture Safety and Health (OASH) is working to better understand how the advancements in automation and robotics is affecting workers. OASH is coordinating with the NIOSH Center of Occupational Robotics Research (CORR) to help to increase the understanding of human/machine interactions; improve the ability to identify injuries and fatalities involving automation/ robotics; and provide guidance on working safely with automation/ robotics. OASH also joined a small team of academics and industry to organize the SAfety For Emerging Robotics and Autonomous aGriculture or (SAFER AG) Workshop to identify gaps in knowledge and research needs that connect to issues related to risks and regulations/standards, occupational safety research, and impacts on workforce and society. This workshop was sponsored by USDA NIFA. Practical Applications: Occupational safety and health experts need to engage and collaborate with developers of technology. It is also increasingly important for occupational safety and health researchers and practitioners to not only become familiar with existing manufacturing safety standards, but also the lengthy standards development process. Joining consensus standards groups to help shape new standards for emerging technologies may help to mitigate adverse worker impacts. NIOSH's Office of Agriculture Safety and Health will continue to identify research gaps, support new research projects, education, outreach efforts and the development of best practices with our partners. [ABSTRACT FROM AUTHOR]

The current success of Reinforcement Learning algorithms for its performance in complex environments has inspired many recent theoretical approaches to cognitive science. Artistic environments are studied within the cognitive science community as rich, natural, multi-sensory, multi-cultural environments. In this work, we propose the introduction of Reinforcement Learning for improving the control of artistic robot applications. Deep Q-learning Neural Networks (DQN) is one of the most successful algorithms for the implementation of Reinforcement Learning in robotics. DQN methods generate complex control policies for the execution of complex robot applications in a wide set of environments. Current art painting robot applications use simple control laws that limits the adaptability of the frameworks to a set of simple environments. In this work, the introduction of DQN within an art painting robot application is proposed. The goal is to study how the introduction of a complex control policy impacts the performance of a basic art painting robot application. The main expected contribution of this work is to serve as a first baseline for future works introducing DQN methods for complex art painting robot frameworks. Experiments consist of real world executions of human drawn sketches using the DQN generated policy and TEO, the humanoid robot. Results are compared in terms of similarity and obtained reward with respect to the reference inputs. • A Deep Q-Learning framework to draw human-like sketches using a humanoid robot. • Adaptability and scalibiliy of DQN introduced in an art painting robot application. • DQN Agent positions are translated to a real robot. • An additional DQN classification network improves the definition of the reward. • Baseline for future works introducing complex art painting robot applications. [ABSTRACT FROM AUTHOR]