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Space telescopes have been instrumental in enlightening our understanding of the universe, from the iconic Hubble Space Telescope to specialized instruments like Chandra and Kepler. Pushing the frontiers of cosmic exploration, the future of space exploration hinges on modular Large Aperture Space Telescopes (LAST), much larger than the recently launched 6.5 m James Webb Space Telescope, necessitating robotic assembly in orbit. This paper introduces a paradigm shift in astronomical observation by featuring robotic in-orbit assembly of a large aperture space telescope. This review paper starts by tracing the evolution of telescopes and presents a comprehensive overview of the state-of-the-art space telescopes. This paper then reinforces the need for LAST to address the constant clamour for higher-resolution astronomy. While current semi-autonomous robotic manipulators operate from the International Space Station, their limited walking capabilities constrain their workspace, making them unsuitable for the LAST mission. This paper presents a detailed trade-off analysis of the challenges associated with the in-orbit assembly of LAST using candidate robots to understand the technological gaps. Further, the evolution of space robotic manipulators is presented, highlighting design features, advantages, and drawbacks for in-orbit spacecraft servicing and assembly missions. To tackle design and modelling challenges for robotic systems in space, amidst the various perturbations in the extreme space environment, linear and non-linear control systems necessary for achieving ultra-high precision performance are also discussed. This paper advances the walking space manipulator technology by introducing the next generation of walking space manipulators – the End-Over-End Walking Robot (E-Walker). The dexterous and modular design of the E-Walker makes it an ideal candidate for missions involving assembly, manufacturing, servicing, and maintenance. [ABSTRACT FROM AUTHOR]

This paper addresses the limitations of camera-based random bin picking (RBP) strategies by proposing an innovative strategy that relies solely on a 6-axis load cell instead of cameras and image processing algorithms. Specific applications of RBP include loading and unloading industrial assembly machines, transferring objects between industrial workstations, and picking objects to be assembled directly. Camera-based RBP systems are limited in industrial settings due to challenges related to image processing robustness, high costs of hardware and software, the need for 3D models and extensive training datasets. These limitations are inherently tied to the reliance on camera-based technology and image processing AI. This paper presents a novel camera-less RBP strategy, eliminating the need for visual information processing altogether and circumventing these limitations. The strategy is largely agnostic to factors such as bin size, object material and gripper size, and as such can be modularly configured to the user's needs. This strategy is demonstrated using a collaborative robot, highlighting the advantages in a collaborative environment. The proposed strategy is evaluated through feasibility case studies on several objects of different geometries, namely navel oranges, cardboard boxes and resin bottles, and performance metrics are benchmarked. The strategy has an average success rate of 99%, average cycle time of 26.08 seconds per object and an average grip success rate of 47%. [ABSTRACT FROM AUTHOR]

A bin picking task involves multiple steps, including object detection, 3D pose estimation, and path planning. Dealing with unstructured and cluttered bins presents challenges due to potential overlaps and misidentification of parts. We propose a purely mathematical approach for achieving highly precise 3D pose estimation at the millimeter level. Our method involves capturing a point-cloud image of the parts within the bin and applying a best fit algorithm to smooth the surface. Subsequently, we employ the Iterative Closest Point Method (ICP) to match a CAD model of the desired part with the smoothed surface point-cloud. Finally, we identify gripping points to enable the robot to pick up the part. The concept is validated in a use-case involving Lego sorting. It is compared with an industrial tool for performance evaluation purposes. [ABSTRACT FROM AUTHOR]

In response to the shortage of skilled workers and the increasing diversity of products and variants, integrating adaptive and flexible mobile manipulators into line-less mobile assembly systems (LMAS) stands out as a promising solution. To fully benefit from these robotic systems in LMAS, offloading computationally intensive tasks from mobile manipulators to edge computing systems is essential. This offloading of computational tasks not only lightens the processing load on individual robots but also enhances AI based perception, improving the overall system capabilities in dynamic manufacturing environments. Achieving these goals requires a robotic edge computing framework that serves as the key to flexible and adaptive mobile manipulation in LMAS. To construct such an edge computing framework, a comprehensive set of building blocks is required. These building blocks include a powerful edge system with high processing power, sensor-equipped mobile manipulators with the ability to perceive the environment, and a robust edge-to-robot connectivity. A comprehensive software stack, including software modules for localization and navigation in dynamic assembly environments, AI perception for precise pose estimation of assembly components, and motion planning for interaction between manipulators and assembly components, is crucial. Furthermore, virtualization techniques, a comprehensive deployment strategy, and a detailed description of robot hardware, site, and resources are essential. The proposed edge computing framework presents a solution that addresses mobile manipulators in LMAS and paves the way towards advanced industrial automation. Through implementation in a research assembly environment, the realized proof-of-concept showcased the feasibility of the proposed edge computing framework in a real-world pick-and-place scenario, highlighting its potential to enhance adaptivity and flexibility. There is a potential to refine and expand the framework to accommodate a broader range of industrial applications and streamline diverse manufacturing processes through the integration of mobile manipulators and edge computing within industrial settings. [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]

A rigid-flexible dual-arm space robot offers promising potential for on-orbit operation due to complementary strengths of its rigid and flexible manipulators. The rigid manipulator has the advantage of large payload capacity and high motion accuracy. The segmented linkage flexible manipulator is ideal for maneuvering in narrow, unstructured environments like internal satellite inspections and maintenance, due to its flexibility and slender body. However, there are inevitable tracking errors due to the multiple cable-driven mechanisms in the flexible manipulator. It's difficult to get the configuration and end pose of flexible manipulator without adding external sensors. To address these issues, this paper presents a method for configuration reconstruction and all-joint synchronous measurement of the flexible manipulator. The flexible manipulator is captured by the stereovision system mounted on the rigid manipulator. The links' equivalent center points are recognized by central moment method and edge line extraction method based on the natural characteristics. Joint - to - link kinematic model of flexible manipulator is established to measure joint angles and reconstruct configuration. Several simulations and experiments are carried out to validate the proposed method. The experimental results showed links' average measurement position errors were less than 13 mm and joint angles reconstructed using the proposed method had an error of approximately 0.35 °. These results demonstrate the accuracy and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

This study aims to highlight the trends in the minimally invasive spine surgery (MISS) research field from the bibliometrics perspective. The articles and reviews from 2002 to 2022 were manually retrieved from Scopus based on predefined inclusion criteria. A total of 296 articles for robotics and 13 articles for AI were included in the final analysis. All publication records were imported and analyzed in Microsoft Excel and VOSviewer. An increase in the number of publications per year was observed in the last five years. For robotics, the United States published the largest number of articles (161), but the Netherlands had the highest total citations (1216). Beijing Jishuitan Hospital, China, was the most prolific institution. For journals, World Neurosurgery had the most publications (31), while Spine journal was the most impactful (average citation index = 86.6). Wang T.Y was the author with the most published articles (5). For AI, the United States had the greatest number of publications (10) and the highest citations (229). Global Spine Journal had the most publications (3), while Spine had the most citations (112). Kim J.S. was the most cited author (102). Recent keywords mainly focused on techniques and prognoses using these modalities in MISS. There were relatively fewer collaborations among countries. An increasing trend in publications regarding robotics and AI use reflects the recent MISS technique advancements. Our findings can provide useful information to identify potential research fronts in the coming years. Enhanced collaboration on an international level should be pursued. [ABSTRACT FROM AUTHOR]

Anterior column realignment via anterior, oblique, or lateral lumbar interbody fusion is increasingly recognized as a powerful mechanism for indirect decompression and sagittal realignment in flexible deformity. Single-position lateral surgery is a popular variation that places patients in the lateral decubitus position, allowing concomitant placement of lateral interbodies and posterior segmental instrumentation without the need for repositioning the patient. The addition of robotics to this technique can help to overcome ergonomic limitations of the placement of pedicle screws in the lateral decubitus position; however, its description in the literature is relatively lacking. In this review we aim to discuss the indications, advantages, and pitfalls of this approach. [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]

This work presents AstrobeeCD, a system for 3D scene change detection toward near-real-time environmental awareness of space outposts using the Astrobee free-flying robot in microgravity. Assistive free-flyer robots autonomously caring for future crewed space habitats must be able to detect day-to-day interior changes to track inventory, detect and diagnose faults, and monitor the outpost status. A set of image and depth data from one time step is used to reconstruct a 3D model of the environment. The 3D model is used as the basis for comparison for free-flyer environment surveys at future time steps, where an image-based change detection algorithm identifies inconsistencies against the 3D model. Change detection is demonstrated using real image and pose data collected by an Astrobee robot in a test environment on Earth at NASA Ames Research Center and from microgravity aboard the International Space Station. Change detection computation time and performance are quantitatively evaluated on the test data captured on Earth, and it identifies scene changes more quickly than a point cloud clustering-based algorithm applied to data from the same surveys. [Display omitted] • Free-flying robotic assistants in space need to efficiently detect scene changes. • AstrobeeCD demonstrates near-real-time 3D scene change detection for space habitats. • AstrobeeCD processes Astrobee's images and poses for image-based change detection. • AstrobeeCD is validated on Astrobee data collected on Earth and in space on the ISS. • Data and code are publicly released to promote further development. [ABSTRACT FROM AUTHOR]

• A dynamic theory of the HMS curved rod under 3D large deformation is presented. • The "tension-bending" and "shear-torsion" coupling effects of curved rod emerge in our model. • The finite element formulation and exact linearization of the dynamical problem are obtained. • An implementation based on Newmark algorithm and some numerical examples are presented. • Several experiments about a quarter arc HMS robotic arm are presented. Magnetorheological elastomers are active materials which can be actuated by the applied magnetic field. Hard magnetic soft (HMS) materials, a type of magnetorheological elastomers, show great potential in the fields of biomedical engineering and soft robotics, due to their short response time, remote operation, and shape programmability. To exploit its potential, a series of theoretical frameworks of HMS rods have been developed, but they are mainly limited to the static rod models or classical curved rod models that fail to consider the effect of the "initial curvature" on the distribution of the stress. In this work, we develop a curved rod theory to predict the 3D dynamic motion of the rod-like HMS robotics under large deformation. Based on the geometrically exact rod theory, we include the heterogeneous initial length of the longitudinal fiber caused by "initial curvature" into our model and obtain the reduced balance equations of the HMS robotics. As a result, the "tension-bending" and "shear-torsion" coupling effects of curved rods emerge in the present model. A numerical implementation of our model based on the classical Newmark algorithm is presented. To validate our model, three numerical examples, including the dynamic snap-through behavior of a bistable arch, are performed and compared with the simulation or experiment results reported in literatures, which show a good agreement. Finally, we experimentally study the 2D and 3D static and dynamic motion of a quarter arc HMS robotic arm under an applied magnetic field of 10 mT, and our model gives a satisfactory prediction, especially for static deformation. [ABSTRACT FROM AUTHOR]

The coronal plane alignment of the knee (CPAK) classification system divides coronal knee anatomy into 9 phenotypes, suggesting different soft tissue balancing is needed for optimal outcomes. We investigated the interplay between CPAK phenotypes and gap stress curves throughout the knee's range of motion, aiming to understand their impact on total knee arthroplasty balancing. There were 1,112 total knee arthroplasty cases from 2 imageless robotic assisted navigation systems using posterior stabilized implants that were classified into CPAK phenotypes. Medial and lateral initial gap values were measured throughout the knee flexion-extension arc, gap curve morphologies were generated, and mediolateral (ML) gap balance was calculated for each phenotype. The most common phenotypes were included in this study, CPAK I to VI. Each phenotype exhibited a distinct gap curve morphology. Type I maintained the largest ML gap difference (−3.6 to −2.1), with the medial compartment tightest in extension. Type II showed relative laxity in the lateral compartment compared to the medial gap (−1.0 to −1.9), with the medial compartment tightening through flexion. Type III had a looser medial and tighter lateral compartment in extension that inverts to a tighter medial compartment in deep flexion (2.1 to −0.8), while Type IV showed a decreasing compartment difference with increased flexion (−3.7 to 0.6). Type V had fluctuating tightness (−0.6 to 1.8), and Type VI had the medial compartment tightening more with flexion (0.6 to 1.8). The distinct stress curves and ML gap behavior provide a "fingerprint" for each corresponding CPAK phenotype. Investigating these morphologies can help determine the best phenotype-specific treatments, including alignment strategy, implant selection, and gap balance, for optimal functional and patient outcomes. [ABSTRACT FROM AUTHOR]

In this work, the design and the experimental characterization of a soft gripper which takes inspiration from the octopus tentacle is presented. The system is a single silicone–based pneumatic soft actuator which is able to perform bending motion thanks to its particular geometry and, due to the presence of an embedded suction cup, it can provide enhanced adhesion to the object surface of the goods to be grasped. Firstly, the design aspects as well as the fabrication process of the single-tentacle soft gripper are described. Then, its performances in terms of bending behavior, static deflection, shape variation under applied load and detachment force of the suction cup are evaluated. The experiments include grasping tests of spherical payloads with di ff erent weights. This gripper might have impact in the grasping and manipulation of delicate products like mozzarella in the agrifood field. [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]

Soft and stretchable electronics have garnered significant attention in various fields, such as wearable electronics, electronic skins, and soft robotics. However, current wearable electronics made from materials like conductive elastomers, hydrogels, and liquid metals face limitations, including low permeability, poor adhesion, inadequate conductivity, and limited stretchability. These issues hinder their effectiveness in long-term healthcare monitoring and exercise monitoring. To address these challenges, we introduce a novel design of web-droplet-like electronics featuring a semi-liquid metal coating for wearable applications. This innovative design offers high permeability, excellent stretchability, strong adhesion, and good conductivity for the electronic skin. The unique structure, inspired by the architecture of a spider web, significantly enhances air permeability compared to commercial breathable patches. Furthermore, the distribution of polyborosiloxane mimics the adhesive properties of spider web mucus, while the use of semi-liquid metals in this design results in remarkable conductivity (9 × 106 S/m) and tensile performance (up to 850% strain). This advanced electronic skin technology enables long-term monitoring of various physiological parameters and supports machine learning recognition functions with unparalleled advantages. This web-droplet structure design strategy holds great promise for commercial applications in medical health monitoring and disease diagnosis. [ABSTRACT FROM AUTHOR]

Over the past few decades, instrumentation and techniques for total knee arthroplasty have evolved from conventional manual tools to a wide range of technologies, including calibrated guides for accurate bone cuts and alignment, smart tools, dynamic intraoperative sensors for soft tissue balancing, patient-specific guides, computer navigation, and robotics. This review is intended to provide an overview of the latest advancements in total knee arthroplasty technology, address potential challenges and solutions related to the application of these technologies, and explore their limitations. [ABSTRACT FROM AUTHOR]

Computer navigation and robotic assistance may reduce total hip arthroplasty (THA) dislocations by improving the accuracy and precision of component positioning. We investigated dislocation rates for THAs using conventional techniques, robotic assistance, and computer navigation, while controlling for surgical approach, dual mobility (DM) use, and fluoroscopic guidance. We reviewed 11,740 primary THAs performed between June 2016 and December 2022, including 5,873 conventional, 1,293 with robotic-arm assistance, and 4,574 with navigation. The approach was posterior in 6,580 (56.0%), anterior in 4,342 (37.0%), and lateral in 818 (7.0%). A DM was used in 10.4%. Fluoroscopy was used in 3,653 cases and only with the anterior approach. Multivariate analyses yielded odds ratios (OR) for dislocation and revision. Additional regression analyses for dislocation were performed for approach and DM. Raw dislocation rates were as follows: conventional 1.2%, robotic 0.4%, navigation 0.9%, anterior with fluoroscopy 0.4%, anterior without fluoroscopy 2.3%, posterior 1.3%, and lateral 0.5%. Upon multivariate analysis, use of robotics was found to be associated with significantly reduced dislocation risk compared to conventional (OR: 0.3), as did anterior (OR: 0.6) compared to posterior approach; navigation and lateral approach were not found to be associated with a significant reduction in risk. For the anterior approach, multivariate analysis demonstrated that fluoroscopy significantly reduced dislocation risk (OR: 0.1), while DM, robotics, and navigation were not significant. For the posterior approach, the dislocation risk was lower with robotics than with conventional (OR: 0.2); the use of navigation or DM did not demonstrate a significant reduction in risk. The use of robotics was associated with a reduction in dislocations for this cohort overall. Further, fluoroscopy in the anterior approach and robotic assistance in the posterior approach were both associated with decreased dislocation risk. The role of imageless computer navigation and DM implants requires further study. [ABSTRACT FROM AUTHOR]

We describe our protocol and outcomes of awake robotic minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) under spinal anesthesia. We conducted a prospective study of 10 consecutive patients undergoing awake robotic single-level MIS-TLIF with the Mazor X robot. We prospectively collected patient-reported outcomes (back and leg pain visual analog scale and Oswestry Disability Index) preoperatively at 1-month and 1-year follow-ups and assessed fusion and screw placement accuracy with a 1-year computed tomography (CT) scan. Median age was 61 years (interquartile range [IQR] = 57.7–66). Median body mass index was 27 kg/m2. No intraoperative complications were reported. Most (9/10) patients were discharged home, and 50% discharged on the day of surgery. Median length of stay was 16.5 hours (IQR = 5–35.5). Median follow-up was 12.5 months (IQR = 12–13.5), with 9 patients having at least 12-month follow-up, with CT scans documenting good screw placement (Gertzbein-Robbins grade A) and solid bony fusion. Median preoperative back pain visual analog scale score was 7.8 (IQR = 6.9–8) versus 1.5 (IQR = 0–3.2) at 1-month post operation, P < 0.01, and 0 (IQR = 0–1) at 1-year follow-up, P < 0.01; median preoperative leg pain 8 (IQR = 7.4–8) versus 0 (IQR = 0–1.2) at 1-month post operation, P < 0.01, and 0 (IQR = 0–2) at 1-year follow-up, P < 0.01; median preoperative Oswestry Disability Index 47.5 (IQR = 27.8–57.5) versus 4 (IQR = 0–16) at 1-month postoperation, P < 0.01, and 0 (IQR = 0–7) at 1-year follow-up, P < 0.01. Median preoperative disk height of the index level was 8 mm (IQR = 2.4–9.5) versus 11.4 mm (IQR = 9.2–11.2) postoperatively, P < 0.01. Median preoperative lordosis of the index level was 5 degrees (IQR = 3.4–8.5) versus 10.1 degrees (7.3–12.2) postoperatively, P < 0.01. Our study showed significant improvement in patient-reported outcomes at 1-month and 1-year follow-ups after awake robotic MIS-TLIF, as well as solid bony fusion on CT scans. [ABSTRACT FROM AUTHOR]

[Display omitted] Replicating the tactile sensing mechanisms, conformity, and feel of real skin is essential for next-generation human–machine interfaces. However, producing tissue-like multilayered geometries and integrating them as e-skin systems requires simplifying and standardizing their manufacture. Here, we present a scalable and cost-effective cure-on-demand strategy for 3D printing nanocomposite silicone rubbers and integrating them into complex soft structures with 1200 % enhanced pressure-strain sensitivity. By utilizing a controlled in-situ mixing of catalyst-cured silicones and shear-driven alignment of carbon nanofibers (CNF), we construct percolated networks with conductivities up to 130 S m−1 layer-by-layer. We investigate the influence of ink composition, printing parameters, geometrical design, and material density on the mechanical properties, stretchability, sensitivity, and antimicrobial activity of 3D printed piezoresistive sensors and build skin-like interfaces that detect minimal deformations like human physiological signs. This customizable, biocompatible, and robust e-skin holds promise for cost-effective integration in rehabilitation medicine, smart robotics applications, and extended reality (XR) interactive experiences. [ABSTRACT FROM AUTHOR]

Space robots are inseparable components of many space missions. However, capabilities of space robots with rigid manipulators are restricted in confronting with unknown and confined environments and situations require high safety. To overcome these defects, the potential of bio-inspired soft robots for space applications has recently been addressed in the literature. Dynamic modeling and control of soft manipulators are challenging due to their infinite degrees of freedom. In this paper, a general comprehensive three-dimensional dynamic modeling framework is developed for a floating soft manipulator-spacecraft system employing the Euler-Lagrange method. This framework derives the coupled equations of motion adapting the generalized Jacobian approach. Consequently, the opportunity of exploiting model-based control methods will be provided. In addition, the proposed model avoids numerical singularities once the bending angles are near zero. Furthermore, the generalized Jacobian matrix is defined for any arbitrary point of the soft manipulator essential for the contact dynamics. Eventually, to verify the proposed dynamic modeling procedure, the dynamic response of a floating soft manipulator-spacecraft system released from an initial configuration is investigated. Then, a sliding mode controller is designed for the floating soft manipulator to track the desired shape and compared to the proportional-derivative control scheme. The obtained results follow the physical principles and fulfill the control objective. • To overcome defects of usual rigid manipulators, soft space robots are concentrated. • A thorough 3D modeling frame is presented for soft manipulator-spacecraft systems. • Thorough dynamic modeling process is established using the Euler-Lagrange approach. • The generalized Jacobian matrix is derived for any optional point of the soft arm. • Application of the sliding mode control is proven for the soft space manipulator. [ABSTRACT FROM AUTHOR]

• A data-driven optimal control framework is proposed for TSR deployment. • A linear representation of TSR's dynamics is derived with Koopman operator. • An enhanced deep learning method is proposed for finding embedding functions. To avoid complex constraints of the traditional nonlinear method for tethered space robot (TSR) deployment, a data-driven optimal control framework with an improve deep learning based Koopman operator is proposed in this work. In consideration of nonlinearity of tethered space robot dynamics, its finite dimensional global linear representation called lifted linear system is derived with the Koopman operator theory. A deep learning scheme is adopted to find the embedding functions associate with Koopman operator. And an auxiliary neural network is developed to encode the nonlinear control term of finite dimensional lifted system. Then a controllability constraint is considered for learning a controllable lifted linear system. Besides two loss functions that relate to reconstruction and prediction ability of lifted linear system are designed for training the deep neural network. With the learned lifted linear dynamics, Linear Quadratic Regulator (LQR) is applied to derive the optimal control policy for the tethered space robot deployment. Finally, simulation results verify the effectiveness of proposed framework and show that it could deploy tethered space robot more quickly with less swing of in-plane angle. [ABSTRACT FROM AUTHOR]

Although minimally invasive distal pancreatectomy (MIDP) is considered a standard approach it still presents a non-negligible rate of conversion to open that is mainly related to some difficulty factors, as obesity. The aim of this study is to analyze the preoperative factors associated with conversion in obese patients with MIDP. In this multicenter study, all obese patients who underwent MIDP at 18 international expert centers were included. The preoperative factors associated with conversion to open surgery were analyzed. Out of 436 patients, 91 (20.9%) underwent conversion to open, presenting higher blood loss, longer operative time and similar rate of major complications. Twenty (22%) patients received emergent conversion. At univariate analysis, the type of approach, radiological invasion of adjacent organs, preoperative enlarged lymphnodes and ASA ≥ III were significantly associated with conversion to open. At multivariate analysis, robotic approach showed a significantly lower conversion rate (14.6 % vs 27.3%, OR = 2.380, p = 0.001). ASA ≥ III (OR = 2.391, p = 0.002) and preoperative enlarged lymphnodes (OR = 3.836, p = 0.003) were also independently associated with conversion. Conversion rate is significantly lower in patients undergoing robotic approach. Radiological enlarged lymphnodes and ASA ≥ III are also associated with conversion to open. Conversion is associated with poorer perioperative outcomes, especially in case of intraoperative hemorrhage. [ABSTRACT FROM AUTHOR]

[Display omitted] • We proposed an SPA based on a Kresling-like pattern with a rigid skeleton by observing the cloning and moving behaviors from salps. • An extensible inserting structure is innovatively designed to replace the creases in traditional Kresling patterns. • The SPA can perform an axial contraction motion without twisting and a controllable bending motion. • The number of layers of Kresling-like patterns is changeable by adding or reducing skeleton components. • The elongation can reach to above 162% and the output force can reach to above 6.36 N. Biomimetic soft pneumatic actuators (SPA) with Kresling origami patterns have unique advantages over conventional rigid robots, owing to their adaptability and safety. Inspired by cloning and moving behaviors observed from salps, we proposed an SPA based on a Kresling-like pattern with a rigid skeleton. The elongation and output force were tested, and the effectiveness of the applications with the SPA was evaluated. The proposed SPA consists of rigid skeletons and a soft skin. The rigid skeletons are constructed using layers of Kresling-like patterns, while a novel extensible inserting structure is devised to replace the folds found in conventional Kresling patterns. This innovative approach ensures that the SPA exhibits axial contraction/expansion motion without any twisting movement. To mimic the bionic characteristics of swimming and ingesting progress of salps, the proposed SPA can perform an axial contraction motion without twisting and a controllable bending motion based on multi-layered Kresling-like patterns; to mimic the cloning and releasing life phenomena of salps, the number of layers of Kresling-like patterns is changeable by adding or reducing skeleton components according to the practical needs. The experimental elongation results on the SPA with multiple layers of Kresling-like patterns show that the elongation can increase to above 162% by adding layers; the experimental output force results show that the three-layer SPA can provide 6.36 N output force at an air flow rate of 10 L/min, and the output force will continue to increase as the number of layers of Kresling-like pattern increases or the air flow rate increases. Further, we demonstrate the applications of the SPA in soft grippers, scissor grippers, claw grippers and pipe crawlers. Our proposed SPA can avoid twisting in the radial contraction motion with high elongation and output force, and provide the practical guidance for bio-inspired soft robotic applications. [ABSTRACT FROM AUTHOR]

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]

[Display omitted] 4D printing combines the typical 3D printing with "smart materials", allowing 3D printed materials to undergo a structural change over time. Since its original concept was first introduced in 2013, 4D printing became an innovative research that has received more attention from scientists in different fields. This review summarizes the progress achieved in 4D printing technologies and their associated materials. First, the technology and process of 4D printing are overviewed, and then the structure and properties of smart materials utilized in 4D printing are analyzed in depth, including metamaterials, shape memory materials, hydrogels, and self-healing polymers. We systematically illustrate the morphing mechanisms of the 4D printed smart materials, and then critically discuss the stimuli that can trigger transformation in the 4D printed smart materials, including heat, light, moisture, pH, electric current, and magnetic field. For 4D printed smart materials, all the changes programmed in the materials follow a mathematical model that allows scientists to predict and design the desired behaviors of the structures, using parameters such as the material distribution and the spatial gradients of the metric tensor. We finally conclude with the discussion of future challenges and opportunities for this ever-growing technology. Overall, 4D printing can create dynamic structures programmed to be responsive to external stimuli in the environment, widening its use in a myriad of applications such as rapid prototyping, electronics, biomedicine, soft robotics, self-assembly structures, smart sensors, and dynamic actuators. [ABSTRACT FROM AUTHOR]

To study the development and clinical validation of the ART Pipetting Robot for the IVF Laboratory (APRIL), a liquid-handling robot customized for the precise preparation of microdroplet culture dishes in the field of in vitro fertilization (IVF). A prospective randomized study conducted at an academic IVF center comparing mouse and human embryo outcomes and quantitative measures of accuracy in embryo dishes prepared using APRIL compared with standard manual preparation. Academic IVF center. The study involved the assessment of the automated culture dish preparation system, APRIL, compared with manual preparation methods in the context of IVF treatment. ART Pipetting Robot for the IVF Laboratory is an enclosed liquid-handling robot equipped with custom three-dimensional–printed adapters and designed to dispense embryo culture media and mineral oil into microdroplet culture dishes. The study evaluated the precision and consistency of APRIL in culture dish preparation by looking at droplet mass, pH of prepared media droplets, and mouse and human embryo development rates. Clinical implementation was assessed by comparing embryo development and outcomes in dishes prepared by APRIL and human embryologists. Compared with embryo culture dishes prepared using standard manual procedures, embryo culture dishes prepared using APRIL demonstrated a greater than 10-fold improvement in consistency (coefficient of variation, 0.46% vs. 6%–7%), maintained optimal pH levels (pH range, 7.281–7.33 vs. 7.275–7.311), and had a greater mouse embryo blastocyst rate (100% vs. 90%–91%). Human embryos cultured in dishes prepared by APRIL had a higher rate of development on days 3 (92.4% vs. 82.6%) and 5 (19.75% vs. 15.57%), and a total number of usable embryos (50.3% vs. 46.1%) compared with manually prepared dishes, although the last two outcomes did not reach statistical significance. The results suggest that the use of an automated robotic system for preparation of embryo culture dishes may improve accuracy and outcome measures while reducing the need for trained laboratory personnel to prepare the dishes manually. [ABSTRACT FROM AUTHOR]

Aiming to endow natural minerals with fast, high-sensitivity, and controllable smart light, humidity, solvent and temperature responsiveness, a novel ceramic clay and cucurbituril (CB) guest-host system (MVCB) inspired by nature was self-assembled and rearranged via an ingenious and simple design successfully. For the first time, the multi-functional MVCB not only displayed a high intelligent multi-stimuli responsiveness as light, humidity, solvent, and temperature switches, but also had multiple selective molecular recognition responses because of different organic vapor with CB and asymmetrical bilayer clay. For instance, due to the factors of guest-host and slow release, the average response speed of self-assembled shuffling the MVCB in methanol was as high as 108°/s compared with bilayer film without CB (20°/s). Besides, the MVCB exhibited controllable swift movement and was bending for grabbing three times its own weight. Moreover, the ultra-wide operating temperature of the MVCB from was ranging −196 to 300 °C under extreme condition. More interestingly, the MVCB presented a unique and compliable self-driven, smart switch, shape memory, environmental awareness and soft robotic gripper owing to the asymmetrical swelling and supramolecular of the CB. This work not only brings a simple, cost-effective, large-scale, and efficient strategy for multi-functionalized natural minerals materials, but also opens potential applications for intelligent response, extremely resistant substances, energy conversion and soft robotics. [ABSTRACT FROM AUTHOR]

Simulation-based training curricula involving objective assessment of proficiency and progressive improvements in competence have been introduced to standardize trainee outcomes in urology. Although their educational impact has been demonstrated, clinical validation is still needed. Different training programs have been developed to improve trainee outcomes in urology. However, evidence on the optimal training methodology is sparse. Our aim was to provide a comprehensive description of the training programs available for urological robotic surgery and endourology, assess their validity, and highlight the fundamental elements of future training pathways. We systematically reviewed the literature using PubMed/Medline, Embase, and Web of Science databases. The validity of each training model was assessed. The methodological quality of studies on metrics and curricula was graded using the MERSQI scale. The level of evidence (LoE) and level of recommendation for surgical curricula were awarded using the educational Oxford Centre for Evidence-Based Medicine classification. A total of 75 studies were identified. Many simulators have been developed to aid trainees in mastering skills required for both robotic and endourology procedures, but only four demonstrated predictive validity. For assessment of trainee proficiency, we identified 18 in robotics training and six in endourology training; however, the majority are Likert-type scales. Although proficiency-based progression (PBP) curricula demonstrated superior outcomes to traditional training in preclinical settings, only four of six (67%) in robotics and three of nine (33%) in endourology are PBP-based. Among these, the Fundamentals of Robotic Surgery and the SIMULATE curricula have the highest LoE (level 1b). The lack of a quantitative synthesis is the main limitation of our study. Training curricula that integrate simulators and PBP methodology have been introduced to standardize trainee outcomes in robotics and endourology. However, evidence regarding their educational impact remains restricted to preclinical studies. Efforts should be made to expand these training programs to different surgical procedures and assess their clinical impact. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of trajectory planning for a free-floating dual-arm space robot, considering the constraints of collision avoidance while simultaneously minimizing the base position and attitude disturbance during on-orbit tasks. One arm is designated as the mission arm, tracking a prescribed path to complete specific goals, while the other serves as the balance arm, compensating for any base attitude disturbance. A collision-free path planning approach is presented for the mission arm, which utilizes a hybrid map, improved artificial potential field (IAPF) and adaptive coupling guidance method. The hybrid map in the mission arm joint space contains the coupling relationship between the deviation of the base position and the motion of the dual arms, along with obstacles transformed from Cartesian space. Then, to address the problem of the goals nonreachable with obstacles nearby (GNRON) of the artificial potential field (APF), a novel repulsive potential field is incorporated into the IAPF. To minimize the base position disturbance, a coupling guidance method is presented that enables the mission arm to move in areas with lower coupling factors on the hybrid map. Finally, simulations are conducted to demonstrate the effectiveness and superiority of the proposed hybrid map and coordinated trajectory planning approach. [ABSTRACT FROM AUTHOR]

Robotic-assisted total knee arthroplasty is increasingly used in revision total knee arthroplasty (rTKA), with imageless systems recently receiving Food and Drug Administration (FDA) approval. However, there remains a paucity of literature on the use of robotic assistance in revision total knee arthroplasty (TKA). This paper describes the imageless surgical technique for robotic revision TKA using a second-generation robotic system and details both intraoperative and 90-day outcomes. This was a retrospective review of 115 robotic revision TKAs from March 2021 to May 2023 at 3 tertiary academic centers. Patient demographics, perioperative surgical data, and 90-day outcomes were collected. Pain and Patient-Reported Outcomes Measurement Information System scores preoperatively and postoperatively were recorded. All-cause reoperations at the final follow-up were detailed. The mean patient age was 65 years (range, 43 to 88), and 58% were women. The mean follow-up time was 13 months (range, 3 to 51). The most common indications for rTKA were instability (n = 37, 32%) and aseptic loosening (n = 42, 37%). There were 83 rTKAs to a posterior-stabilized liner, 22 to a varus-valgus constrained liner, and 5 to a hinged construct. The median polyethylene size was 11 (interquartile range, 10 to 13), and 93% of patients had their joint line restored within 5 millimeters of the native contralateral knee. Within the 90-day postoperative window, there were 8 emergency department visits and 2 readmissions. At the final follow-up, there were 5 reoperations and 2 manipulations under anesthesia. There were 4 patients who required irrigation and debridement after superficial wound dehiscence, and one had an arthrotomy disruption after a fall. This review demonstrates favorable intraoperative and 90-day outcomes and suggests that imageless robotic surgery is a promising modality in rTKA. Further studies comparing the longitudinal outcomes after robotic and conventional rTKA are warranted. [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]

Tactile sensing provides robots the ability of object recognition, fine operation, natural interaction, etc. However, in the actual scenario, robotic tactile recognition of similar objects still faces difficulties such as low efficiency and accuracy, resulting from a lack of high-performance sensors and intelligent recognition algorithms. In this paper, a flexible sensor combining a pyramidal microstructure with a gradient conformal ionic gel coating was demonstrated, exhibiting excellent signal-to-noise ratio (48 dB), low detection limit (1 Pa), high sensitivity (92.96 kPa−1), fast response time (55 ms), and outstanding stability over 15,000 compression-release cycles. Furthermore, a Pressure-Slip Dual-Branch Convolutional Neural Network (PSNet) architecture was proposed to separately extract hardness and texture features and perform feature fusion. In tactile experiments on different kinds of leaves, a recognition rate of 97.16% was achieved, and surpassed that of human hands recognition (72.5%). These researches showed the great potential in a broad application in bionic robots, intelligent prostheses, and precise human–computer interaction. [ABSTRACT FROM AUTHOR]

Cancer engineering is an interdisciplinary approach that promises to confront the complexities of cancer and accelerate transformative discoveries by integrating innovative fields across engineering and the physical sciences with a focus on cancer. We offer a conceptual framework for the hallmarks of cancer engineering, integrating 12 fields: system dynamics; imaging, radiation, and spectroscopy; robotics and controls; solid mechanics; fluid mechanics; chemistry and nanomaterials; mathematics and simulation; cellular and protein engineering; kinetics and thermodynamics; materials science; manufacturing and biofabrication; and microsystems. [ABSTRACT FROM AUTHOR]

In this paper, a novel post-capture takeover control strategy of non-cooperative target with model uncertainties by the dual-arm space manipulator is proposed. Two mission objectives of target detumbling and pose adjusting are comprehensively considered. First, the motion trajectory of the target is parameterized with normalized time by a Bezier shape-based function. On this basis, by considering the resultant external force and moment constraint and the model uncertainties of the target, the mission duration is optimized to obtain the time optimal detumbling and pose adjusting the trajectory of the target. Furthermore, the desired trajectory in manipulator joint space is obtained by introducing the arm angle inverse kinematics method, and a joint trajectory tracking controller is designed based on the feedback linearization theory to track the desired joint trajectory. Simulation results show that detumbling and pose adjusting can be simultaneously achieved by one control action, which verifies the effectiveness of the proposed integrated control strategy. • A time-optimal control strategy is proposed to simultaneously achieve target detumbling and pose adjusting. • The model uncertainty of non-cooperative target is considered in post-capture takeover control. • The arm angle parameterized inverse kinetics is introduced to avoid singularity of the inverse Jacobian matrix. [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]

On-orbit servicing and on-orbit assembly represent very appealing mission concepts that could facilitate the exploitation of the space environment. Autonomy is a critical requirement for systems aimed at the execution of these operations. The development of enabling technologies for this type of missions is a focal research topic for the global space community. The AUTOMA project (University of Padua, Italy) aims at the development of technologies to enable the on-orbit assembly of a standardized modular unit by means of a robotic arm. The goal is the development of a capture interface, composed of a gripper mechanism and a suite of close-range navigation sensors (a navcam, four time-of-flight sensors, two custom matrix sensors based on a set of phototransistors), and the mock-up of a modular assembly unit. The paper prevalently focuses on the conspicuous series of tests that has been performed at subsystem level for the characterization of the two custom matrix sensors, both in terms of their resolution and range of application. In particular, the test campaign has proven how the in-plane matrix sensor provides information about lateral misalignment and relative distance with a resolution of a few millimetres while the roll matrix sensor provides information about the angular roll misalignment with a resolution dependent on the distance between the two bodies. In addition, an analytical relation for the computation of the relative distance through the information provided by the in-plane matrix has been investigated. • Capture interface composed of a gripper mechanism and a suite of close-range navigation sensors. • Design of a standardized unit mock-up for modular assembly. • Custom sensors based on a matrix of phototransistors activated by an infrared LED beacon counterpart. • Testing campaign aimed at the characterization of the custom matrix sensors. • Analysis of a relation for the retrieval of the relative distance based on the number of phototransistors activated. [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]

Space robots have proven their ability to accomplish many space missions impossible or dangerous for the human. However, the conventional space robots suffer from the low adaptability, heavy structures, and the low safety. In this regard, the bio-inspired soft robots, composed of the soft material, have been emerged to overcome these shortcomings and to expand the space missions' diversity. The potential of soft robots for space applications has recently been investigated in the literature. Although the dynamic modeling and control of soft robotic arms are complicated due to their infinite degrees of freedom, soft manipulators are anticipated to provide the adaptive capture of targets, confined space operations, and the long-distance handling of space facilities. In this regard, a general novel dynamic modeling algorithm is presented here for soft space robotic manipulators (SSRM) in a specified orientation based on the generalized Jacobian method. The proposed algorithm could be employed for different numbers of soft manipulator constant-curvature elements and avoids numerical singularities using the Taylor expansion as well. In addition, the generalized Jacobian matrix is defined to compute the velocity of any arbitrary point on the soft manipulator for contact dynamics objectives. Accuracy of the proposed dynamic model has been verified via comparison to the simulation results of the existing fixed-base soft manipulators. Moreover, performance of the proposed model has been more investigated through two numerical simulation case studies. The first case verifies that the dynamic responses of the floating SSRM follows the physical principles. While the second one addresses the configuration tracking problem of the floating SSRM exposing a computed torque control scheme. The obtained results indicate the effectiveness of the presented dynamic modeling algorithm and the proposed control scheme. [ABSTRACT FROM AUTHOR]

On-orbit capturing is a crucial step in the application of dual-arm space robots, aiming to accurately grasp a moving target and avoid damage to the arms terminal parts (i.e. the end effectors) due to contact force saturation. This paper presents a synthetic control method with target sticking for on-orbit capturing using a dual-arm space robot system from the approaching phase to pre-grasping phase. In the approaching phase, an online trajectory planning method based on visual servoing is proposed to obtain the desired trajectories for end effectors in work space. Then the joint torques are designed by the inverse kinematics and inverse dynamics to track the desired trajectories. Different from most of the current research, a new sticking phase is proposed after the approaching phase and before the pre-grasping phase. In the sticking phase, a force and motion control method is designed to maintain a close relative distance between end effectors and the target, which can avoid saturation of contact forces and avoid damage to the end effectors. In what follows, an impedance control method is proposed in the pre-grasping phase to absorb the impact energy and grasp the target accurately. The synthetic control strategy integrates the controllers of all the phases mentioned above. Automatic switching among the phases is realized by using logic judgment according to the system real states, which is more consistent with the real capturing environment. Finally, numerical simulation results demonstrate the effectiveness of the proposed synthetic control scheme. The results can be applied to the trajectory planning and control of on-orbit capturing, avoiding contact force saturation and ensuring the safety of the system. [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]

The current research background of digital robot visual defect detection focuses on the application of virtual artificial intelligence algorithms. Convolutional neural networks (CNNS) perform well in the field of image processing and can learn and extract image features, which provides a more refined analysis ability for the visual defect detection of digital robots. Therefore, this paper focuses on the application of convolutional neural networks in digital robot vision defect detection, and explores the main processes of data collection and preprocessing, feature extraction, model training and defect detection in detail. Finally, the results of simulation experiments are as follows: Compared with traditional methods that rely on edge detection or threshold segmentation, the optimized defect detection accuracy rate is increased by 11.4%, and the image detection speed is increased by about 33.9%. The research results have important practical significance for improving the quality control of industrial manufacturing, which can not only improve the quality of products, but also adapt to the constant changes of the production line. [ABSTRACT FROM AUTHOR]

This study addresses the challenge of formation control and navigation in swarms of mobile robots, presenting an innovative algorithm that integrates behavioral approaches with fuzzy logic to calculate behavior weights for Efficient, adaptive coordination dynamically. Using a leader-follower mechanism enhanced by a virtual leader, the algorithm enables coordinated swarm movement in complex environments. The methodology combines fuzzy logic with a behavioral strategy, allowing mobile robots to adjust their actions based on environmental cues and swarm dynamics without predefned paths. Simulated experiments show the algorithm's capability to maintain formation integrity and navigate obstacles, significantly improving swarm adaptability and operational efficiency. The findings suggest a promising direction for developing intelligent mobile robotic systems capable of complex, autonomous tasks, contributing to the field of swarm robotics with a novel approach to formation control and navigation that balances individual autonomy with collective behavior. [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]

Cobots play an essential role in the fourth industrial revolution and the automation of complex manufacturing processes. However, cobots still face challenges in achieving high precision, which obstructs their usage in precise applications such as the aerospace industry. Nonetheless, advances in perception systems unlock new cobot manufacturing capabilities. This paper presents a novel multi-functional sensor that combines visual and tactile feedback using a single optical sensor, featuring a moving gate mechanism. This work also marks the first integration of Vision-Based Tactile Sensing (VBTS) into a robotic machining end-effector. The sensor provides vision-based tactile perception capabilities for precise normality control and exteroceptive perception for robot localization and positioning. Its performance is experimentally demonstrated in a precise robotic deburring application, where the sensor achieves the high-precision requirements of the aerospace industry with a mean normality error of 0.13° and a mean positioning error of 0.2 mm. These results open a new paradigm for using vision-based sensing for precise robotic manufacturing, which surpasses conventional approaches in terms of precision, weight, size, and cost-effectiveness. • We develop a novel multi-functional sensor for robotic manufacturing that combines proprioceptive and exteroceptive sensing using only a single camera. This proposed design offers improvements in cost, weight, and design simplicity over prior work in the literature, pioneering the application of tactile sensing in robotic manufacturing tasks. • For the first time, we propose vision-based tactile sensing for normality adjustment in precise robotic machining. We develop the corresponding perception and control algorithms by training a CNN architecture. This approach outperforms State-of-The-Art, achieving a mean absolute error of 0.13° and significantly enhancing robotic machining precision. • We formulate and validate the visual guidance and control laws that utilize the camera's exteroceptive feedback to precisely position the robotic tool relative to a target workpiece with 0.2 mm accuracy. • We perform experimental validation and testing of the proposed sensor in a robotic deburring setup and present a detailed qualitative and quantitative analysis of the obtained results. Results show that the proposed sensor and perception algorithms provide the positioning and normality requirements of automated manufacturing in the aerospace industry. [ABSTRACT FROM AUTHOR]

The demands for efficiency and stability in space missions have necessitated the development of sequence planning schemes for on-orbit robotic assembly of space structures. A sequence planning method is proposed in this work based on robotic kinematic analysis and Symbiotic Organisms Search (SOS) algorithm with a diversification strategy. With feasibility as the constraint and assembly cost as the objective function, the assembly sequence planning is regarded as an optimization problem. The assembly relationship constraint is depicted as the dependency between assembly steps represented by a directed acyclic graph. An abstract modeling method based on robotic kinematics is proposed to construct geometric feasibility constraint, accommodating the interference stemming from the multi-degree-of-freedom motion of the robotic system. A diversification strategy is exploited to address the issue of local optima arising from solving the optimal sequence based on SOS. A cost prediction method using previously calculated populations as samples is proposed to reduce the reliance on robotic dynamic analysis for cost calculation. Finally, simulations are carried out to validate the practicability of the proposed method. • The on-orbit robotic assembly sequence planning is modeled as an optimization problem with feasibility constraints. • The dependencies and priorities between steps are considered in the feasibility constraints of multi-DOF robotic motions. • The optimal sequence determination based on Symbiotic Organisms Search is speeded up by a cost prediction method. [ABSTRACT FROM AUTHOR]