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Sidewalk Autonomous Delivery Robots (SADRs) present a promising alternative for mitigating excessive delivery traffic in smart cities. These bots operate at pedestrian speed and work in conjunction with vans to offer efficient delivery services. Existing research emphasises the development of coordinated service schedules for vans and bots to optimise customer service. In contrast, this study examines the influence of bot return policies on their travel back to designated stations after task completion. We assess three distinct return policies that dictate the station selection for bot returns and explore the relocation of bots between stations using vans. Specifically, we present a reformulation of the fleet sizing problem as a minimum cost matching problem in a bipartite graph. This reformulation allows for the efficient calculation of optimal solutions for bot fleet sizing under different return policies within polynomial time. Notably, this computational efficiency enables the analysis of large-scale cases without sacrificing the evaluation of policies with heuristic gaps. Our findings highlight the importance of carefully selecting the appropriate return policy, as the best policies have the potential to decrease the bot fleet size by up to 70%. [ABSTRACT FROM AUTHOR]

This paper addresses the control problem of complex temporal logic tasks for nonlinear systems. A new control barrier function (CBF) called time‐varying extent‐compatible control barrier function (TV‐ECCBF) is proposed, which extends the traditional CBF by considering time‐varying extent or volumes. The main aim of TV‐ECCBF is to tackle the issue of avoiding obstacles in real‐world situations that have irregular or dynamically changing shapes, which cannot be efficiently resolved through traditional CBF methods. To encode complex task specifications, a control framework is presented based on signal temporal logic (STL). Additionally, we introduce robust semantics to discuss the satisfaction of target formulas. Moreover, the control framework generates controllers automatically based on different types of tasks, exhibiting global performance in achieving complex tasks. Finally, two examples of mobile robot systems are presented to demonstrate the effectiveness of the TV‐ECCBF and the proposed design framework. [ABSTRACT FROM AUTHOR]

A distributed algorithm A solves the Point Convergence task if an arbitrarily large collection of entities, starting in an arbitrary configuration, move under the control of A to eventually form and thereafter maintain configurations in which the separation between all entities is arbitrarily small. This fundamental task in the standard OBLOT model of autonomous mobile entities has been previously studied in a variety of settings, including full visibility, exact measurements (including distances and angles), and synchronous activation of entities. Our study concerns the minimal assumptions under which entities, moving asynchronously with limited and unknown visibility range and subject to limited imprecision in measurements, can be guaranteed to converge in this way. We present an algorithm operating under these constraints that solves Point Convergence, for entities moving in two or three dimensional space, with any bounded degree of asynchrony. We also prove that under similar realistic constraints, but unbounded asynchrony, Point Convergence in the plane is not possible in general, contingent on the natural assumption that algorithms maintain the (visible) connectivity among entities present in the initial configuration. This variant, that we call Cohesive Convergence, serves to distinguish the power of bounded and unbounded asynchrony in the control of autonomous mobile entities, settling a long-standing question whether in the Euclidean plane synchronously scheduled entities are more powerful than asynchronously scheduled entities. [ABSTRACT FROM AUTHOR]

Plastering is dominated manually, exhibiting low levels of automation and inconsistent finished quality. A comprehensive review of literature indicates that extant plastering robots demonstrate a subpar performance when tasked with rectifying defects in the transition area. The limitations encompass a lack of capacity to independently evaluate the quality of work or perform remedial plastering procedures. To address this issue, this research describes the system design of the Puttybot and a paradigm of plastering to solve the stated problems. The Puttybot consists of a mobile chassis, a lift platform, and a macro/micromanipulator. The force‐controlled scraper parameters have been calibrated to dynamically modify their rigidity in response to the applied putty. This strategy utilizes convolutional neural networks to identify plastering defects and executes the plastering operation with force feedback. This paradigm's effectiveness was validated during an autonomous plastering trial wherein a large‐scale wall was processed without human involvement. [ABSTRACT FROM AUTHOR]

This paper presents the design and development of a funnel‐shaped Sparus Docking Station intended for the non‐holonomic torpedo‐shaped Sparus II Autonomous Underwater Vehicle. The Sparus Docking Station is equipped with sensors and batteries, allowing for a stand‐alone long‐term deployment of the vehicle. An inverted Ultra Short Base‐Line system is used to locate the Docking Station as well as to provide long‐term drift‐less vehicle navigation. The Sparus Docking Station is able to observe the ocean currents using a Doppler Velocity Log, being motorized to allow its self‐alignment with the current. Moreover, a docking algorithm accounting for the current is used to guide the robot during the docking maneuver. The paper reports consecutive successful experimental results of the docking maneuver in sea trials in two different countries. [ABSTRACT FROM AUTHOR]

Path planning involves finding the optimal path in a known environment, allowing robots or vehicles to reach the destination as quickly and safely as possible from the starting point. In this domain, the RRT* (Rapidly-exploring Random Tree Star) algorithm has emerged as an advanced path planning approach, in handling challenges in complex environments. However, due to the slow convergence rate of RRT*, and the significant uncertainty in the generated paths, achieving stability in generating optimal paths within a fixed number of iterations poses challenges for the RRT* algorithm. This paper develops a novel algorithm, Informed-TRRT*, which combines the Theta* algorithm with the Informed-RRT* algorithm. The algorithm proposed in this paper rapidly obtains a short initial solution using the Theta* algorithm, narrows the sampling region based on this initial solution to improve sampling efficiency, and incorporates pruning algorithms to further enhance path optimality. Through comparative simulation experiments in environments of increasing complexity, the proposed algorithm generates paths with lengths optimized by 1.23% and 3.56% compared to the Theta* algorithm and Informed-RRT* algorithm, respectively, in environments with gradually increasing complexity; 19.53% and 8.76%; 6.45% and 9.07%. [ABSTRACT FROM AUTHOR]

Autonomous delivery robots (ADRs) must share and negotiate for public and private space with pedestrians. Game theory shows that this requires making credible threats of inflicting at least small harms onto members of the public, which requires new legal justification. To this end, we argue that ADRs could be considered as pedestrians under existing law. We propose ‘robot self-defence’ and right to property as the legal basis for inflicting the required ‘small penalties.’ We examine the liability framework when an ADR actually causes a collision either deliberately through actualising a credible collision threat or accidentally via errors in controlling intended non-contact threats. We explore challenges around privacy and data protection where the ADR collects and uses data to model and predict interactions. Together, this provides a framework for legal operation of ADRs, including the ability to inflict small harm onto members of the public when necessary in negotiations for space. [ABSTRACT FROM AUTHOR]

High-repetitive features in unstructured environments and frequent signal loss of the Global Navigation Satellite System (GNSS) severely limits the development of autonomous robot localization in orchard settings. To address this issue, we propose a LiDAR-based odometry pipeline GLIO, inspired by KISS-ICP and DLIO. GLIO is based on a nonlinear observer with strong global convergence, effectively fusing sensor data from GNSS, IMU, and LiDAR. This approach allows for many potentially interfering and inaccessible relative and absolute measurements, ensuring accurate and robust 6-degree-of-freedom motion estimation in orchard environments. In this framework, GNSS measurements are treated as absolute observation constraints. These measurements are tightly coupled in the prior optimization and scan-to-map stage. During the scan-to-map stage, a novel point-to-point ICP registration with no parameter adjustment is introduced to enhance the point cloud alignment accuracy and improve the robustness of the nonlinear observer. Furthermore, a GNSS health check mechanism, based on the robot's moving distance, is employed to filter reliable GNSS measurements to prevent odometry crashed by sensor failure. Extensive experiments using multiple public benchmarks and self-collected datasets demonstrate that our approach is comparable to state-of-the-art algorithms and exhibits superior localization capabilities in unstructured environments, achieving an absolute translation error of 0.068 m and an absolute rotation error of 0.856°. [ABSTRACT FROM AUTHOR]

This paper presents an obstacle detection and tracking framework for mobile robots and autonomous vehicles equipped with LiDAR (Light Detecting and Range finding) sensors. The framework contains a detection module (DM) for clustering the point cloud and modeling the obstacles, and a tracking module (TM) for recognizing the obstacles and estimating their velocities. In order to detect the obstacles, DM segments the point cloud by finding the gaps in it. Detected obstacles are modeled by one or two line segments depending on their geometry. TM gets the line segments returned by DM as the obstacles to track. To this end, first, the obstacles are labeled and their features are stored. Thereafter, a set of equations are solved to recognize the labeled obstacles. Finally, the Kalman filter is used to calculate the translational and rotational velocities of the obstacles. The framework is evaluated in experiments on a robot platform and using the KITTI dataset. The results indicate satisfactory performance in terms of effectiveness and quickness and confirm that DM and TM are qualified enough to perform in real time as ancillary modules of mobile robots and autonomous vehicles. Especially, because of robust and accurate obstacle modeling, the velocity diagrams are smooth and coherent, a point which is not seen in similar researches. [ABSTRACT FROM AUTHOR]

To effectively assist autonomous robots in better planning and controlling their motion trajectories, enabling them to efficiently navigate to target positions and avoid obstacles and colli- sions. a new tracking algorithm was proposed using membrane computing method. Firstly, utili zing membrane computing to construct cell membrane structures with multiple step sizes increa ses the opportunity for autonomous robots to find feasible paths. Secondly, based on the operating rules of the aforementioned membrane, the workflow of the fast connection stage was designed to find feasible autonomous robot trajectory paths, so that the autonomous robot could avoid obsta cles and collisions, providing support for subsequent tracking. Finally, based on the multi-step parallel membrane structure a feasible autonomous robot trajectory path was found and a sliding mode variable structure function was used to design the control rate. The position error was ad- justed by the control rate to achieve autonomous robot trajectory tracking. The experimental re- sults show that the robot tracking is almost identical to the actual motion trajectory, with small errors(The errors on the X and Y axes are both less than 3 mm), and it has strong robustness. The convergence speed is below 1. 1 s,and it can adapt to uncertain situations. The smoothness of the tracking trajectory can be maintained at over 96%, with high stability.good tracking perform- ance, and a minimum tracking response time of 5 s. which is practical. [ABSTRACT FROM AUTHOR]

Nowadays, the use of advanced sensors, such as terrestrial, mobile 3D scanners and photogrammetric imaging, has become the prevalent practice for 3D Reality Modeling (RM) and the digitization of large-scale monuments of Cultural Heritage (CH). In practice, this process is heavily related to the expertise of the surveying team handling the laborious planning and time-consuming execution of the 3D scanning process tailored to each site's specific requirements and constraints. To minimize human intervention, this paper proposes a novel methodology for autonomous 3D Reality Modeling of CH monuments by employing autonomous robotic agents equipped with the appropriate sensors. These autonomous robotic agents are able to carry out the 3D RM process in a systematic, repeatable, and accurate approach. The outcomes of this automated process may also find applications in digital twin platforms, facilitating secure monitoring and the management of cultural heritage sites and spaces, in both indoor and outdoor environments. The main purpose of this paper is the initial release of an Industry 4.0-based methodology for reality modeling and the survey of cultural spaces in the scientific community, which will be evaluated in real-life scenarios in future research. [ABSTRACT FROM AUTHOR]

Interest in deploying deep reinforcement learning (DRL) models on low-power edge devices, such as Autonomous Mobile Robots (AMRs) and Internet of Things (IoT) devices, has seen a significant rise due to the potential of performing real-time inference by eliminating the latency and reliability issues incurred from wireless communication and the privacy benefits of processing data locally. Deploying such energy-intensive models on power-constrained devices is not always feasible, however, which has led to the development of model compression techniques that can reduce the size and computational complexity of DRL policies. Policy distillation, the most popular of these methods, can be used to first lower the number of network parameters by transferring the behavior of a large teacher network to a smaller student model before deploying these students at the edge. This works well with deterministic policies that operate using discrete actions. However, many real-world tasks that are power constrained, such as in the field of robotics, are formulated using continuous action spaces, which are not supported. In this work, we improve the policy distillation method to support the compression of DRL models designed to solve these continuous control tasks, with an emphasis on maintaining the stochastic nature of continuous DRL algorithms. Experiments show that our methods can be used effectively to compress such policies up to 750% while maintaining or even exceeding their teacher's performance by up to 41% in solving two popular continuous control tasks. [ABSTRACT FROM AUTHOR]

As robots become increasingly common in human-populated environments, they must be perceived as social beings and behave socially. People try to preserve their own space during social interactions with others, and this space depends on a variety of factors, such as individual characteristics or their age. In real-world social spaces, there are many different types of people, and robots need to be more sensitive, especially when interacting with vulnerable subjects such as children. However, the current navigation methods do not consider these differences and apply the same avoidance strategies to everyone. Thus, we propose a new navigation framework that considers different social types and defines appropriate personal spaces for each, allowing robots to respect them. To this end, the robot needs to classify people in a real environment into social types and define the personal space for each type as a Gaussian asymmetric function to respect them. The proposed framework is validated through simulations and real-world experiments, demonstrating that the robot can improve the quality of interactions with people by providing each individual with an adaptive personal space. The proposed costmap layer is available on GitHub. [ABSTRACT FROM AUTHOR]

For successful human–robot collaboration, it is crucial to establish and sustain quality interaction between humans and robots, making it essential to facilitate human–robot interaction (HRI) effectively. The evolution of robot intelligence now enables robots to take a proactive role in initiating and sustaining HRI, thereby allowing humans to concentrate more on their primary tasks. In this paper, we introduce a system known as the Robot-Facilitated Interaction System (RFIS), where mobile robots are employed to perform identification, tracking, re-identification, and gesture recognition in an integrated framework to ensure anytime readiness for HRI. We implemented the RFIS on an autonomous mobile robot used for transporting a patient, to demonstrate proactive, real-time, and user-friendly interaction with a caretaker involved in monitoring and nursing the patient. In the implementation, we focused on the efficient and robust integration of various interaction facilitation modules within a real-time HRI system that operates in an edge computing environment. Experimental results show that the RFIS, as a comprehensive system integrating caretaker recognition, tracking, re-identification, and gesture recognition, can provide an overall high quality of interaction in HRI facilitation with average accuracies exceeding 90% during real-time operations at 5 FPS. [ABSTRACT FROM AUTHOR]

Decision-making systems allow artificial agents to adapt their behaviours, depending on the information they perceive from the environment and internal processes. Human beings possess unique decision-making capabilities, adapting to current situations and anticipating future challenges. Autonomous robots with adaptive and anticipatory decision-making emulating humans can bring robots with skills that users can understand more easily. Human decisions highly depend on dopamine, a brain substance that regulates motivation and reward, acknowledging positive and negative situations. Considering recent neuroscience studies about the dopamine role in the human brain and its influence on decision-making and motivated behaviour, this paper proposes a model based on how dopamine drives human motivation and decision-making. The model allows robots to behave autonomously in dynamic environments, learning the best action selection strategy and anticipating future rewards. The results show the model's performance in five scenarios, emphasising how dopamine levels vary depending on the robot's situation and stimuli perception. Moreover, we show the model's integration into the Mini social robot to provide insights into how dopamine levels drive motivated autonomous behaviour regulating biologically inspired internal processes emulated in the robot. [ABSTRACT FROM AUTHOR]

In recent years, with the rapidly aging population, alleviating the pressure on medical staff has become a critical issue. To improve the work efficiency of medical staff and reduce the risk of infection, we consider the multi-trip autonomous mobile robot (AMR) routing problem in a stochastic environment. Our goal is to minimize the total expected operating cost and maximize the total service quality for patients, ensuring that each route violates the vehicle capacity and the time window with only a minimal probability. The travel time of AMRs is stochastically affected by the surrounding environment; the demand for each ward is unknown until the AMR reaches the ward, and the service time is linearly related to the actual demand. We developed a population-based tabu search algorithm (PTS) that combines the genetic algorithm with the tabu search algorithm to solve this problem. Extensive numerical experiments were conducted on the modified Solomon instances to demonstrate the efficiency of the PTS algorithm and reveal the impacts of the confidence level on the optimal solution, providing insights for decision-makers to devise delivery schemes that balance operating costs with patient satisfaction. [ABSTRACT FROM AUTHOR]

Effective path planning is crucial for autonomous mobile robots navigating complex environments. The "global–local" coupled path planning algorithm exhibits superior global planning capabilities and local adaptability. However, these algorithms often fail to fully realize their potential due to low efficiency and excessive constraints. To address these issues, this study introduces a simpler and more effective integration strategy. Specifically, this paper proposes using a bi-layer map and a feasible domain strategy to organically combine the D*Lite algorithm with the Dynamic Window Approach (DWA). The bi-layer map effectively reduces the number of nodes in global planning, enhancing the efficiency of the D*Lite algorithm. The feasible domain strategy decreases constraints, allowing the local algorithm DWA to utilize its local planning capabilities fully. Moreover, the cost functions of both the D*Lite algorithm and DWA have been refined, enabling the fused algorithm to cope with more complex environments. This paper conducts simulation experiments across various settings and compares our method with A_DWA, another "global–local" coupled approach, which combines A* and DWA. D_DWA significantly outperforms A_DWA in complex environments, despite a 7.43% increase in path length. It reduces the traversal of risk areas by 71.95%, accumulative risk by 80.34%, global planning time by 26.98%, and time cost by 35.61%. Additionally, D_DWA outperforms the A_Q algorithm, a coupled approach validated in real-world environments, which combines A* and Q-learning, achieving reductions of 1.34% in path length, 67.14% in traversal risk area, 78.70% in cumulative risk, 34.85% in global planning time, and 37.63% in total time cost. The results demonstrate the superiority of our proposed algorithm in complex scenarios. [ABSTRACT FROM AUTHOR]

This study empirically and comprehensively explores consumers' ethical perceptions of autonomous service robots (ASRs) in hotels. Under the triangulation approach, this study has identified eight themes of consumer perceived ethical issues (privacy, security, safety, transparency, fairness, socialization, autonomy, and responsibility). Each theme can be explained from two dimensions: ethical issues arise during the interaction (i.e., ubiquitous surveillance, excessive data, unidentified risks, service disclosure, inaccessibility, dehumanization, selection of services, and service recovery), and ethical issues can be raised by the characteristics of ASRs (i.e., privacy infringement, malicious use, malfunctions, untrustworthiness, biased features, job replacement, inflexibility, and self-identified solutions). This study is the first to propose ethical issues of ASRs from two dimensions with different intelligence levels, and to highlight ethical issues during hotel service interactions. The findings contribute to ethics studies of service robots from consumers' perspectives and offer managerial insights to reduce ethical concerns and enhance ASRs usage in hotels. [ABSTRACT FROM AUTHOR]

Autonomous navigation in dynamic environments is a significant challenge in robotics. The primary goals are to ensure smooth and safe movement. This study introduces a control strategy based on an Adaptive Neuro-Fuzzy Inference System (ANFIS). It enhances autonomous robot navigation in dynamic environments with a focus on collision-free path planning. The strategy uses a path-planning technique to develop a trajectory that allows the robot to navigate smoothly while avoiding both static and dynamic obstacles. The developed control system incorporates four ANFIS controllers: two are tasked with guiding the robot toward its end point, and the other two are activated for obstacle avoidance. The experimental setup conducted in CoppeliaSim involves a mobile robot equipped with ultrasonic sensors navigating in an environment with static and dynamic obstacles. Simulation experiments are conducted to demonstrate the model's capability in ensuring collision-free navigation, employing a path-planning algorithm to ascertain the shortest route to the target destination. The simulation results highlight the superiority of the ANFIS-based approach over conventional methods, particularly in terms of computational efficiency and navigational smoothness. [ABSTRACT FROM AUTHOR]

Obstacle avoidance is essential for the effective operation of autonomous mobile robots, enabling them to detect and navigate around obstacles in their environment. While deep learning provides significant benefits for autonomous navigation, it typically requires large, accurately labeled datasets, making the data's preparation and processing time-consuming and labor-intensive. To address this challenge, this study introduces a transfer learning (TL)-based automatic labeling segmentation (ALS) framework. This framework utilizes a pretrained attention-based network, DifferNet, to efficiently perform semantic segmentation tasks on new, unlabeled datasets. DifferNet leverages prior knowledge from the Cityscapes dataset to identify high-entropy areas as road obstacles by analyzing differences between the input and resynthesized images. The resulting road anomaly map was refined using depth information to produce a robust drivable area and map of road anomalies. Several off-the-shelf RGB-D semantic segmentation neural networks were trained using pseudo-labels generated by the ALS framework, with validation conducted on the GMRPD dataset. Experimental results demonstrated that the proposed ALS framework achieved mean precision, mean recall, and mean intersection over union (IoU) rates of 80.31%, 84.42%, and 71.99%, respectively. The ALS framework, through the use of transfer learning and the DifferNet network, offers an efficient solution for semantic segmentation of new, unlabeled datasets, underscoring its potential for improving obstacle avoidance in autonomous mobile robots. [ABSTRACT FROM AUTHOR]

This paper presents a complete system architecture for multi-robot coordination for unbalanced task assignments, where a number of robots are supposed to visit and accomplish missions at different locations. The proposed method first clusters tasks into clusters according to the number of robots, then the assignment is done in the form of one-cluster-to-one-robot, followed by solving the traveling salesman problem (TSP) to determine the visiting order of tasks within each cluster. A nonlinear model predictive controller (NMPC) is designed for robots to navigate to their assigned tasks while avoiding colliding with other robots. Several simulations are conducted to evaluate the feasibility of the proposed architecture. Video examples of the simulations can be viewed at https://youtu.be/5C7zTnv2sfo and https://youtu.be/-JtSg5V2fTI?si=7PfzZbleOOsRdzRd. Besides, we compare the cluster-based assignment with a simulated annealing (SA) algorithm, one of the typical solutions for the multiple traveling salesman problem (mTSP), and the result reveals that with a similar optimization effect, the cluster-based assignment demonstrates a notable reduction in computation time. This efficiency becomes increasingly pronounced as the task-to-agent ratio grows. [ABSTRACT FROM AUTHOR]

This article discusses the advancements in robotic surgery for dental implants. The authors conducted a search on the PubMed database and identified 46 articles related to dental implants and robotic surgery, with 25 of them published in 2023. The articles analyzed in 2023 showed that robotic surgery provides higher accuracy and precision in implant positioning compared to other techniques. The authors highlight the diverse applications of robotic surgery in implantology and anticipate that this trend will continue to grow in the future. [Extracted from the article]

The rising of e-commerce demands an increase in the performance of warehousing systems, which are being redesigned to deal with a mass volume of demands to be fulfilled as quickly as possible. The manual and robotic mobile fulfilment systems (RMFS) are the most commonly used for these activities. The former is a human-centred system that handles complex operations that current robots cannot perform. However, newer generations of autonomous robots are leading to a gradual replacement by the latter to increase productivity. Regardless of the system used, several interdependent problems must be solved for efficient storage and picking processes. Storage problems concern decisions on where to store products within the warehouse. Batching and picking problems include batching orders to be fulfilled, and the routes the pickers and robots should follow to retrieve the products demanded. This paper identifies the most relevant problems and the state-of-the-art research on these topics. We study the interactions among many of these interrelated problems and provide a critical analysis of the literature. Finally, eight research directions are identified and discussed. [ABSTRACT FROM AUTHOR]

The high growth height and substantial weight of bananas present challenges for robots to harvest autonomously. To address the issues of high labor costs and low efficiency in manual banana harvesting, a highly autonomous and integrated banana-picking robot is proposed to achieve autonomous harvesting of banana bunches. A prototype of the banana-picking robot was developed, featuring an integrated end-effector capable of clamping and cutting tasks on the banana stalks continuously. To enhance the rapid and accurate identification of banana stalks, a target detection vision system based on the YOLOv5s deep learning network was developed. Modules for detection, positioning, communication, and execution were integrated to successfully develop a banana-picking robot system, which has been tested and optimized in multiple banana plantations. Experimental results show that this robot can continuously harvest banana bunches. The average precision of detection is 99.23%, and the location accuracy is less than 6 mm. The robot picking success rate is 91.69%, and the average time from identification to harvesting completion is 33.28 s. These results lay the foundation for the future application of banana-picking robots. [ABSTRACT FROM AUTHOR]

Recent dramatic increase in e-commerce has also increased the adoption of automation technologies in warehouses. Autonomous mobile robots (AMRs) are from those technologies widely utilized in warehouse operations. It is important to design the operation of those robotic systems in such a way that, they meet the current and future system requirements correctly. In this paper, we study flexible travel of AMRs in warehouses by developing smart deadlock and collision prevention algorithms on agent-based modelling. By that, AMR agents can interact with each other and environment, so that they can make smart decisions maximizing their goals. We compare the performance of the developed flexible travel system with non-flexible designs where there is a single AMR dedicated to a specific zone so that no deadlock or collision possibility takes place. The results show that AMRs may provide up to 39% improvement in the flexible system compared to its non-flexible design. [ABSTRACT FROM AUTHOR]

Object tracking is one of the major tasks for mobile robots in many real-world applications. Also, artificial intelligence and automatic control techniques play an important role in enhancing the performance of mobile robot navigation. In contrast to previous simulation studies, this paper presents a new intelligent mobile robot for accomplishing multi-tasks by tracking red-green-blue (RGB) colored objects in a real experimental field. Moreover, a practical smart controller is developed based on adaptive fuzzy logic and custom proportional-integral-derivative (PID) schemes to achieve accurate tracking results, considering robot command delay and tolerance errors. The design of developed controllers implies some motion rules to mimic the knowledge of experienced operators. Twelve scenarios of three colored object combinations have been successfully tested and evaluated by using the developed controlled image-based robot tracker. Classical PID control failed to handle some tracking scenarios in this study. The proposed adaptive fuzzy PID control achieved the best accurate results with the minimum average final error of 13.8 cm to reach the colored targets, while our designed custom PID control is efficient in saving both average time and traveling distance of 6.6 s and 14.3 cm, respectively. These promising results demonstrate the feasibility of applying our developed image-based robotic system in a colored object-tracking environment to reduce human workloads. [ABSTRACT FROM AUTHOR]

Autonomous pollination robots have been widely discussed in recent years. However, the accurate estimation of flower poses in complex agricultural environments remains a challenge. To this end, this work proposes the implementation of a transformer-based architecture to learn the translational and rotational errors between the pollination robot's end effector and the target object with the aim of enhancing robotic pollination efficiency in cross-breeding tasks. The contributions are as follows: (1) We have developed a transformer architecture model, equipped with two feedforward neural networks that directly regress the translational and rotational errors between the robot's end effector and the pollination target. (2) Additionally, we have designed a regression loss function that is guided by the translational and rotational errors between the robot's end effector and the pollination targets. This enables the robot arm to rapidly and accurately identify the pollination target from the current position. (3) Furthermore, we have designed a strategy to readily acquire a substantial number of training samples from eye-in-hand observation, which can be utilized as inputs for the model. Meanwhile, the translational and rotational errors identified in the end-manipulator Cartesian coordinate system are designated as loss targets simultaneously. This helps to optimize the training of the model. We conducted experiments on a realistic robotic pollination system. The results demonstrate that the proposed method outperforms the state-of-the-art method, in terms of both accuracy and efficiency. [ABSTRACT FROM AUTHOR]

With the advancement in artificial intelligence technology, autonomous mobile robots have been utilized in various applications. In autonomous driving scenarios, object classification is essential for robot navigation. To perform this task, light detection and ranging (LiDAR) sensors, which can obtain depth and height information and have higher resolution than radio detection and ranging (radar) sensors, are preferred over camera sensors. The pillar-based method employs a pillar feature encoder (PFE) to encode 3D LiDAR point clouds into 2D images, enabling high-speed inference using 2D convolutional neural networks. Although the pillar-based method is employed to ensure real-time responsiveness of autonomous driving systems, research on accelerating the PFE is not actively being conducted, although the PFE consumes a significant amount of computation time within the system. Therefore, this paper proposes a PFE hardware accelerator and pillar-based object classification model for autonomous mobile robots. The proposed object classification model was trained and tested using 2971 datasets comprising eight classes, achieving a classification accuracy of 94.3%. The PFE hardware accelerator was implemented in a field-programmable gate array (FPGA) through a register-transfer level design, which achieved a 40 times speedup compared with the firmware for the ARM Cortex-A53 microprocessor unit; the object classification network was implemented in the FPGA using the FINN framework. By integrating the PFE and object classification network, we implemented a real-time pillar-based object classification acceleration system on an FPGA with a latency of 6.41 ms. [ABSTRACT FROM AUTHOR]

A recent article in Pulse International discusses the use of robotic ultrasound systems in medicine. Prof. Nassir Navab from the Technical University of Munich has developed a system that can perform routine examinations autonomously, relieving doctors of these tasks. The system attaches an ultrasound probe to a robotic arm, which can examine patients without the presence of a doctor. The system can display 3D images, visualize physiological parameters, and recognize anomalies. It can be used in medical practices and operating theaters, providing benefits such as improved visualization, comparability of data, and access to medical care for everyone. Trust-building measures are being implemented to ensure patient comfort and safety. Overall, the technology is expected to be well-received by patients and has the potential to revolutionize ultrasound examinations. [Extracted from the article]

Most real‐world systems are affected by external disturbances, which may be impossible or costly to measure. For instance, when autonomous robots move in dusty environments, the perception of their sensors is disturbed. Moreover, uneven terrains can cause ground robots to deviate from their planned trajectories. Thus, learning the external disturbances and incorporating this knowledge into the future predictions in decision‐making can significantly contribute to improved performance. Our core idea is to learn the external disturbances that vary with the states of the system, and to incorporate this knowledge into a novel formulation for robust tube model predictive control (TMPC). Robust TMPC provides robustness to bounded disturbances considering the known (fixed) upper bound of the disturbances, but it does not consider the dynamics of the disturbances. This can lead to highly conservative solutions. We propose a new dynamic version of robust TMPC (with proven robust stability), called state‐dependent dynamic TMPC (SDD‐TMPC), which incorporates the dynamics of the disturbances into the decision‐making of TMPC. In order to learn the dynamics of the disturbances as a function of the system states, a fuzzy model is proposed. We compare the performance of SDD‐TMPC, MPC, and TMPC via simulations, in designed search‐and‐rescue scenarios. The results show that, while remaining robust to bounded external disturbances, SDD‐TMPC generates less conservative solutions and remains feasible in more cases, compared to TMPC. [ABSTRACT FROM AUTHOR]

Autonomous robots are already present in a variety of domains performing complex tasks. Their deployment in open-ended environments offers endless possibilities. However, there are still risks due to unresolved issues in dependability and trust. Knowledge representation and reasoning provide tools for handling explicit information, endowing systems with a deeper understanding of the situations they face. This article explores the use of declarative knowledge for autonomous robots to represent and reason about their environment, their designs, and the complex missions they accomplish. This information can be exploited at runtime by the robots themselves to adapt their structure or re-plan their actions to finish their mission goals, even in the presence of unexpected events. The primary focus of this article is to provide an overview of popular and recent research that uses knowledge-based approaches to increase robot autonomy. Specifically, the ontologies surveyed are related to the selection and arrangement of actions, representing concepts such as autonomy, planning, or behavior. Additionally, they may be related to overcoming contingencies with concepts such as fault or adapt. A systematic exploration is carried out to analyze the use of ontologies in autonomous robots, with the objective of facilitating the development of complex missions. Special attention is dedicated to examining how ontologies are leveraged in real time to ensure the successful completion of missions while aligning with user and owner expectations. The motivation of this analysis is to examine the potential of knowledge-driven approaches as a means to improve flexibility, explainability, and efficacy in autonomous robotic systems. [ABSTRACT FROM AUTHOR]

In robotics, achieving adaptivity in complex environments is challenging. Traditional robotic systems use stiff materials and computationally expensive centralized controllers, while nature often favors soft materials and embodied intelligence. Inspired by nature's distributed intelligence, this study explores a decentralized approach for robust behavior in soft robotic systems without knowledge of their shape or environment. It is demonstrated that only a few basic rules implemented in identical modules that shape the soft robotic system can enable whole‐body phototaxis, navigating on a surface toward a light source, without explicit communication between modules or prior system knowledge. The results reveal the method's effectiveness in generating robust and adaptive behavior in dynamic and challenging environments. Moreover, the approach's simplicity makes it possible to illustrate and understand the underlying mechanism of the observed behavior, paying particular attention to the geometry of the assembled system and the effect of learning parameters. Consequently, the findings offer insights into the development of adaptive, autonomous robotic systems with minimal computational power, paving the way for robust and useful behavior in soft and microscale robots, as well as robotic matter, that operate in real‐world environments. [ABSTRACT FROM AUTHOR]

A recent article in Pulse International discusses the use of robotic ultrasound systems in medicine. Prof. Nassir Navab from the Technical University of Munich has developed a system that can perform routine examinations autonomously, relieving doctors of these tasks. The system attaches an ultrasound probe to a robotic arm, which can examine patients without the presence of a doctor. It can display 3D images of vessels and physiological parameters, as well as recognize anomalies. The system can be used in operating theaters, allowing surgeons to have their hands free while still receiving ultrasound images. The benefits of these autonomous systems include 3D imaging, comparability of data, and the potential for health scans without medical experts. Trust-building measures are being implemented to ensure patient comfort and acceptance of the technology. Overall, the use of robotic ultrasound systems has the potential to relieve medical staff, improve patient care, and democratize access to medical care. [Extracted from the article]

Sidewalk autonomous delivery robots (SADRs) share sidewalks with pedestrians and may affect their perceived safety. In outdoor nighttime environments, the color of the lights on SADRs serves as a noticeable form of communication that can influence human emotions. Therefore, this study investigated whether the perceived safety of SADRs varied with the colors of their lighting. In this study, an experiment (n = 30) was conducted where participants walked towards a robot from opposite directions to interact. The perceived safety of five different lighting colors (red, yellow, blue, green, and white) on SADRs was measured before and after the interaction using two perceived safety scales. The results showed significant differences in participants' perceived safety for the robot's red, green, blue, and yellow lighting before and after the interaction. Red lighting was rated the least perceived safe, while white and yellow lighting were rated the most perceived safe. Additionally, gender significantly influenced the perceived safety assessments, with females reporting lower perceived safety than males. These findings are valuable when designing SADRs that enhance pedestrians' perceived safety, thereby facilitating their integration into broader environments in the future. [ABSTRACT FROM AUTHOR]

Traditional industrial robots come with prime movers, i.e. electric motors (EMs), which range from a few hundred to just a few kilo watts of power ratings. However, for autonomous robotic navigation systems, we require motors which are lightweight with the aspect of high torque and power density. This aspect is very critical when the EMs in robotic navigations are subjected to harsh high temperature survival conditions, where the sustainability of the performance metrics of the electromagnetic system of the EMs degrades with the prevailing high temperature conditions. Hence, this research work addresses and formulates the design methodology to develop a 630 W high temperature PMSM (HTPMSM) in the aspect of high torque and power density, which can be used for the autonomous robotic navigation systems under high temperature survival conditions of 200°C. Two types of rotor configurations i.e. the surface permanent magnet type (SPM) and the interior permanent magnet type (IPM) of HTPMSM are examined for its optimal electromagnetic metrics under the temperature conditions of 200°C. The 630 W HTPMSM is designed to deliver the rated torque of 2 Nm within the volumetric & diametric constraints of DxL, which comes at 80 × 70 mm at the rated speed of 3 000 rpm with the survival temperature of 200°C and target efficiency greater than 90%. The FEM based results are validated through the hardware prototypes for both SPM and IPM types, and the results confirm the effectiveness of the proposed design methodology of HTPMSM for sustainable autonomous robotic navigation applications. [ABSTRACT FROM AUTHOR]

Prior research has demonstrated that trust in robots and performance of robots are two important factors that influence human–autonomy teaming. However, other factors may influence users' perceptions and use of autonomous systems, such as perceived intent of robots and decision authority of the robots. The current study experimentally examined participants' trust in an autonomous security robot (ASR), perceived trustworthiness of the ASR, and desire to use an ASR that varied in levels of decision authority and benevolence. Participants (N = 340) were recruited from Amazon Mechanical Turk. Results revealed the participants had increased trust in the ASR when the robot was described as having benevolent intent compared to self‐protective intent. There were several interactions between decision authority and intent when predicting the trust process, showing that intent may matter the most when the robot has discretion on executing that intent. Participants stated a desire to use the ASR in a military context compared to a public context. Implications for this research demonstrate that as robots become more prevalent in jobs paired with humans, factors such as transparency provided for the robot's intent and its decision authority will influence users' trust and trustworthiness. The role of decision authority and stated social intent as predictors of trust in autonomous robots Signaling intent to a human partner is an important capability of a machine partner for effective human‐autonomy teams. The current study demonstrated that the benefits of intent signaling vary based on the degree of decision authority a machine partner has to enact behaviors in accordance with that intent. [ABSTRACT FROM AUTHOR]

Fractional calculus, a branch of mathematical analysis, extends traditional calculus by allowing for integrals and derivatives of non-integer orders. This concept has found applications in various scientific disciplines, including robotics. Fractional calculus offers a more precise and flexible approach to modeling complex robotic systems, particularly in areas such as control, optimization, and path planning. The articles in this special issue explore different applications of fractional calculus in robotics, including active disturbance rejection control for remotely operated vehicles, path planning for car-like mobile robots, wind power forecasting, control system development for pneumatic positioning systems, trajectory tracking control for robotic manipulators, real-time optimization for industrial robots, controller design for mechatronic systems, modeling of dielectric elastomer actuators, and impedance control for robot manipulators. These articles demonstrate the potential of fractional calculus to revolutionize various aspects of robotic technology and encourage further research in this field. [Extracted from the article]

Abstract\nIMPLICATIONS FOR REHABILITATIONPurpose: Visual impairment poses significant challenges in daily life, especially when navigating unfamiliar environments, resulting in inequalities and reduced quality of life. This study aimed to gain an in-depth understanding of the needs and perspectives of visually impaired people in sports-related contexts through surveys and focus groups, and to understand whether their needs are being met by current technological solutions.Materials and methods: To accomplish this, opinions gathered from focus groups and interviews were compared to the technological solutions found in the literature. Since many unmet needs were identified, participants from associations and organizations were asked to identify key characteristics for the development of a robot guide. The results underscored the paramount importance of an easy-to-use guide that offers accurate and personalized assistance. Participants expressed a strong desire for advanced features such as object recognition and navigation in complex environments, as well as adaptability to the user’s speed while providing the necessary safety features to ensure a high level of autonomy.Results: This research serves as a bridge between technological advances and the needs of the visually impaired, contributing to a more accessible and inclusive society. By addressing the unique challenges faced by the visually impaired individuals and tailoring technology to meet their needs, this study takes a significant step toward reducing disparities and improving the independence and quality of life for this community.Conclusions: As technology continues to advance, it has the potential to be a powerful tool in breaking down barriers and fostering a world where everyone, regardless of their visual ability, can navigate the world with confidence and ease.Inclusive design: Recognizing the importance of incorporating the unique requirements and perspectives of visually impaired individuals can guide the development of rehabilitation technology and services, ensuring they effectively support daily activities and active participation in sports and physical pursuits.Tailored-assistive technology: Understanding the specific needs of visually impaired individuals with regards to assistive technology, such as dependable robotic guides and essential features, can inform the design and customization of rehabilitation aids to enhance mobility and independence.Promising technologies: Exploring promising technologies like Aira, Be My Eyes, RoboCart, and Wayband can inspire the integration of these innovations into rehabilitation programs, facilitating better orientation, mobility, and accessibility for individuals with visual impairments.Continued research and development: Emphasizing the necessity for ongoing research and development efforts underscores the importance of advancing rehabilitation solutions that effectively address the distinct needs of visually impaired individuals, particularly in navigating unfamiliar environments.Inclusive design: Recognizing the importance of incorporating the unique requirements and perspectives of visually impaired individuals can guide the development of rehabilitation technology and services, ensuring they effectively support daily activities and active participation in sports and physical pursuits.Tailored-assistive technology: Understanding the specific needs of visually impaired individuals with regards to assistive technology, such as dependable robotic guides and essential features, can inform the design and customization of rehabilitation aids to enhance mobility and independence.Promising technologies: Exploring promising technologies like Aira, Be My Eyes, RoboCart, and Wayband can inspire the integration of these innovations into rehabilitation programs, facilitating better orientation, mobility, and accessibility for individuals with visual impairments.Continued research and development: Emphasizing the necessity for ongoing research and development efforts underscores the importance of advancing rehabilitation solutions that effectively address the distinct needs of visually impaired individuals, particularly in navigating unfamiliar environments. [ABSTRACT FROM AUTHOR]

The present technology, based on laser and visual SLAM navigation and positioning, does not apply to the event of a fire in a room where there is a large amount of smoke, for its increasingly obvious defects. In addition, traditional track deduction technology based on photoelectric encoder has accumulated error, and noise disturbance exists in the INS inertial navigation measurement technology, and the UWB positioning technology is vulnerable to NLOS disturbance caused by site occlusion. To solve the problem of accurate positioning of Autonomous Mobile fire-fighting robot in smoke scenes, the system design is optimized by adopting the following methods: using IMU-assisted residual chi-square criterion to detect whether there is NLOS in UWB, introducing IMU instantaneous compensation positioning data and adopting Chan algorithm fitting of the second multiplication to ensure the stability and accuracy of UWB data; meanwhile, a tight combination model of navigation and positioning is designed: via the improved Kalman filter algorithm, fused with the magnetic encoder track estimation pose, UWB absolute and IMU heading angle pose to realize the accurate positioning of the fire robot in the smoke scene. Finally, the fusion simulation model and algorithm are verified by MATLAB, as it shows, the method has an average positioning accuracy of 98.63% in the X-axis direction, 99.52% in the Y-axis direction, and 97.24% in the heading angle, which solves the inherent physical defects of a single positioning sensor and serves as a reliable and accurate solution for indoor robot positioning. [ABSTRACT FROM AUTHOR]

The concept of affordance is commonly described as the set of possibilities an environment offers an animal to select and execute actions. In the context of autonomous robot navigation, determining which affordances are available to a mobile agent provides crucial information to perform navigation tasks such as executing navigation plans or exploring new environments. In this work, we provide a robot with the capability of detecting changes in the set of navigation affordances that it has available as it moves through indoor spaces. Specifically, given a navigation plan represented as a sequence of high-level behaviors such as 'turn left' or 'follow a corridor', we propose the Affordance-Based Plan Executor (APEX) as a new learning-based method to execute this plan based on the identification of navigation affordances. As a relevant fact, the proposed method does not require a map of the environment at execution time. To the best of our knowledge, our work is one of the first to explicitly consider the identification of navigation affordances as a central element to implement the execution of a behavior-based navigation plan in an indoor environment. Our experiments using the Gibson simulator and environments from the Stanford 2D-3D-S dataset suggest that our approach performs well in previously seen environments in comparison to baselines, exceeding alternative approaches' success rate by up to 43%. Additionally, our approach shows promise at generalizing to previously unseen environments, exceeding the success rate of alternative approaches by up to 26.3%. Finally, our experiments confirm that explicit reasoning about affordances is key to APEX's performance. [ABSTRACT FROM AUTHOR]

Due to the excellent maneuverability and obstacle crossing of legged robots, it is possible for an autonomous legged wall-climbing robots to replace manual inspection of ship exterior panels. However, when the magnetic adsorption legged wall-climbing robot steps on the convex point or convex line of the wall, or even when the robot missteps, the robot is likely to detach from the ferromagnetic wall. Therefore, this paper proposes a tactile sensor for the legged magnetic adsorption wall-climbing robot to detect the magnetic adsorption state and improve the safety of the autonomous crawling of the robot. The tactile sensor mainly comprises a three-dimensional (3D)-printed shell, a tactile slider, and three isometric sensing units, with an optimized geometry. The experiment shows that the triboelectric tactile sensor can monitor the sliding depth of the tactile slider and control the light-emitting device (LED) signal light. In addition, in the demonstration experiment of detecting the adsorption state of the robot's foot, the triboelectric tactile sensor has strong adaptability to various ferromagnetic wall surfaces. Finally, this study establishes a robot gait control system to verify the feedback control ability of the triboelectric tactile sensor. The results show that the robot equipped with the triboelectric tactile sensor can recognize the dangerous area on the crawling wall and autonomously avoid the risk. Therefore, the proposed triboelectric tactile sensor has great potential in realizing the tactile sensing ability of robots and enhancing the safety and intelligent inspection of ultra-large vessels. [ABSTRACT FROM AUTHOR]

Current operations involving Dismantling and Decommissioning (D&D) in nuclear and other harsh environments rely on manual inspection and assessment of the sites, exposing human operators to potentially dangerous situations. This work presents a reconfigurable Autonomous Mobile Robot (AMR) able to mount a wide range of nuclear sensors for flexible and modular inspection tasks in these operations. This AMR is part of the CLEANDEM solution, which uses Unmanned Ground Vehicles (UGVs), nuclear sensors, and a Digital Twin to facilitate a tool for improving D&D operations in nuclear sites. Both the AMR used as a UGV and the system have been successfully tested in real nuclear sites, showing that these tools can greatly aid in operations management and hazard reduction. [ABSTRACT FROM AUTHOR]

The appearance of construction defects in buildings can arise from a variety of factors, ranging from issues during the design and construction phases to problems that develop over time with the lifecycle of a building. These defects require repairs, often in the context of a significant shortage of skilled labor. In addition, such work is often physically demanding and carried out in hazardous environments. Consequently, adopting autonomous robotic systems in the construction industry becomes essential, as they can relieve labor shortages, promote safety, and enhance the quality and efficiency of repair and maintenance tasks. Hereupon, the present study introduces an end-to-end framework towards the automation of shotcreting tasks in cases where construction or repair actions are required. The proposed system can scan a construction scene using a stereo-vision camera mounted on a robotic platform, identify regions of defects, and reconstruct a 3D model of these areas. Furthermore, it automatically calculates the required 3D volumes to be constructed to treat a detected defect. To achieve all of the above-mentioned technological tools, the developed software framework employs semantic segmentation and 3D reconstruction modules based on YOLOv8m-seg, SiamMask, InfiniTAM, and RTAB-Map, respectively. In addition, the segmented 3D regions are processed by the volumetric modeling component, which determines the amount of concrete needed to fill the defects. It generates the exact 3D model that can repair the investigated defect. Finally, the precision and effectiveness of the proposed pipeline are evaluated in actual construction site scenarios, featuring reinforcement bars as defective areas. [ABSTRACT FROM AUTHOR]

Autonomous mobile robots (AMRs) combine a remarkable combination of mobility, adaptability, and an innate capacity for obstacle avoidance. They are exceptionally well-suited for a wide range of applications but usually operate in uncontrolled, non-deterministic environments, so the analysis and classification of security events are very important for their safe operation. In this regard, we considered the influence of different types of attacks on AMR navigation systems to subdivide them into classes and unified the effect of attacks on the system through their level of consequences and impact. Then, we built a model of an attack on a system, taking into account five methods of attack implementation and identified the unified response thresholds valid for any type of parameter, which allows for creating universal correlation rules and simplifies this process, as the trigger threshold is related to the degree of impact that the attack has on the finite subsystem. Also, we developed a methodology for classifying incidents and identifying key components of the system based on ontological models, which makes it possible to predict risks and select the optimal system configuration. The obtained results are important in the context of separating different types of destructive effects based on attack classes. Our study showed that it is sometimes difficult to divide spoofing attacks into classes by assessing only one parameter since the attacker can use a complex attack scenario, mixing the stages of the scenarios. We then showed how adding an attack intensity factor can make classification more flexible. The connections between subsystems and parameters, as well as the attack impact patterns, were determined. Finally, a set of unique rules was developed to classify destructive effects with uniform response thresholds for each parameter. In this case, we can increase the number of parameters as well as the type of parameter value. [ABSTRACT FROM AUTHOR]