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• The calibration problem of the general acupuncture robot is analyzed. • A simultaneous offline calibration method for problems defined with multiple closed kinematic chains is proposed. • A multi-objective optimization model covering all coupling components and constraints is constructed and solved. • A real-world platform with a customized calibration board is built. • The proposed method has higher calibration accuracy and convergence speed. Acupuncture robot is a new-era product combining traditional acupuncture and cutting-edge technology. The calibration of the vision system and the acupuncture mechanism is a crucial prerequisite for humanoid acupuncture control, which has not yet been explored. In this paper, a simultaneous offline calibration method is proposed for acupuncture robots. Analysis reveals that its calibration problem is defined with three closed kinematic chains, while the typical problems cover only a single chain. Decoupling them, a Kronecker product-based method is deduced to access the closed-form rotation component. As opposed to quaternion-based methods, it only experiences linear complexity in sign ambiguity, which can produce a more precise solution in finite time. Further, a simultaneous optimization model that encompasses all components is established, which can be solved with stochastic gradient descent-based methods. It is free of truncation errors and thus has higher calibration accuracy and convergence speed. Besides, the impairment in error propagation between different closed kinematic chains is mitigated compared to step-by-step methods. Finally, simulations and experiments are carried out. Notably, the proposed method can be easily extended to other robot calibration problems with multiple closed kinematic chains. [ABSTRACT FROM AUTHOR]

Robotic technologies have shown their potential to improve efficiency, precision, and safety for construction tasks. In this paper, the concept of design for robotic construction (DfRC) is introduced, and robotic collaborative systems are developed for the construction of load‐carrying structures. An automated structural assembly was achieved and demonstrated through robotics with a preference for reciprocal frame (RF) structures. Key innovations include the use of magnetic temporary connections to minimize offsets of mobile robots and linear actuators as temporary supports during construction. Furthermore, specific procedures are formulated to navigate the robots and to target and install the components using a fiducial marker system and simultaneous localization and mapping packages, with consideration of structural deformation during construction due to self‐weight. The successful assembly of a 4.5‐m span RF structure demonstrates the potential of DfRC and the proposed robotic collaborative system in the automated construction of load‐carrying structures. [ABSTRACT FROM AUTHOR]

In this paper, we propose a distributed controller for holonomic and nonholonomic multi-robot systems to patrol multiple targets. Distributed control is a control method that uses only information observed by the robot itself and gathered from its surrounding without relying on centralized control. In order for the robots to accomplish their patrols, we propose an objective function that changes switching weights depending on the time when the targets are not observed. As a result, each robot determines which targets to observe based on the time weights and the information from surrounding robots to achieve efficient patrols. We design a distributed controller based on the gradient-flow method for both holonomic and nonholonomic systems. Finally, simulation and experimental results show that the proposed controller is useful for the robots to realize patrolling. [ABSTRACT FROM AUTHOR]

The current robot path planning methods only use global or local methods, which is difficult to meet the real-time and integrity requirements, and can not avoid dynamic obstacles. Based on this, this study will use the improved A-star global planning algorithm to design a hybrid robot obstacle avoidance path planning algorithm that integrates sliding window local planning methods to solve related problems. Specifically, A-star is optimized by evaluation function, sub node selection mode and path smoothness, and fuzzy control is introduced to optimize the sliding window algorithm. The study conducted algorithm validation on the TurtleBot3 mobile robot, with data sourced from experimental data from a certain college. The results showed that hybrid algorithm enabled the planned path to effectively navigate around dynamic obstacles and reach the target point accurately. When compared with traditional methods, path length reduced by 9.6%, path planning time decreased by 29% with an approximate 26.7% increase in the average speed of the robot. Compared with the traditional methods, the research algorithm has greatly improved in avoiding dynamic obstacles, path planning efficiency, model adaptability and so on, which has important value for relevant research. It can be seen that the algorithm proposed in the study has performance advantages, demonstrating the effectiveness and advantages of robot path planning, and can provide reference for robot obstacle avoidance optimization. Research can complete tasks for robots in practical environments, which has certain reference value for the research of robots in path planning and the development of path obstacle avoidance planning. [ABSTRACT FROM AUTHOR]

The precision of workpiece machining is critically influenced by the geometric errors in the kinematics of grind robots, which directly affect their absolute positioning accuracy. To tackle this challenge, this paper introduces a logistic-tent chaotic mapping Levenberg Marquardt algorithm designed to accurately identify and compensate for this geometric error. the approach begins with the construction of a forward kinematic model and an error model specific to the robot. Then the algorithm is adopted to identify and compensate for the geometric error. The method establishes a mapping interval around the initial candidate solutions derived from iterative applications of the Levenberg Marquardt algorithm. Within this interval, the logistic-tent chaotic mapping method generates a diverse set of candidate solutions. These candidates are evaluated based on their fitness values, with the optimal solution selected for subsequent iterations. Empirical compensation experiments have validated the proposed method's precision and effectiveness, demonstrating a 6% increase in compensation accuracy and a 47.68% improvement in efficiency compared to existing state-of-the-art approaches. This process not only minimizes the truncation error inherent in the Levenberg Marquardt algorithm but also significantly enhances solution efficiency. Moreover, simulation experiments on grind processes further validate the method's ability to significantly improve the quality of workpiece machining. [ABSTRACT FROM AUTHOR]

The swift advancements in robotics have rendered navigation an essential task for mobile robots. While map-based navigation methods depend on global environmental maps for decision-making, their efficacy in unfamiliar or dynamic settings falls short. Current deep reinforcement learning navigation strategies can navigate successfully without pre-existing map data, yet they grapple with issues like inefficient training, slow convergence, and infrequent rewards. To tackle these challenges, this study introduces an improved two-delay depth deterministic policy gradient algorithm (LP-TD3) for local planning navigation. Initially, the integration of the long–short-term memory (LSTM) module with the Prioritized Experience Re-play (PER) mechanism into the existing TD3 framework was performed to optimize training and improve the efficiency of experience data utilization. Furthermore, the incorporation of an Intrinsic Curiosity Module (ICM) merges intrinsic with extrinsic rewards to tackle sparse reward problems and enhance exploratory behavior. Experimental evaluations using ROS and Gazebo simulators demonstrate that the proposed method outperforms the original on various performance metrics. [ABSTRACT FROM AUTHOR]

We have been developing human-sized biped robots based on passive dynamic mechanisms. In human locomotion, the muscles activate at the same rate relative to the gait cycle during running. To achieve adaptive running for robots, such characteristics should be reproduced to yield the desired effect, In this study, we designed a central pattern generator (CPG) involving fast and slow adaptation to achieve human-like running using a simple spring-mass model and our developed bipedal robot, which is equipped with actuators that imitate the human musculoskeletal system. Our results demonstrate that the CPG-based controller with fast and slow adaptations, and an adjustable actuator control timing can reproduce human-like running. The results suggest that the CPG contributes to the adjustment of the muscle activation timing in human running. [ABSTRACT FROM AUTHOR]

Continuum robotics has emerged as a prominent trend within the robotics field for about two decades. Nevertheless, motion analysis of continuum robots based on dynamic modelling remains severely limited. In the realm of continuum robots, it is obvious that simplified theoretical models or first-order approximations utilizing springs and dampers are insufficient for capturing the dynamics of soft-bodied structures. This inadequacy is especially apparent in applications involving significant deformations in 3D space, continuous actuation, and interface effects. In this regard, the development of precise theoretical models is crucial. Within this particular framework, the paper presents the absolute nodal coordinates formulation (ANCF) to construct the kinematic models of continuum robots. The transformation between the structural, body, and element coordinate systems, as well as the derivation of the generalized external forces and moments using the principle of virtual work and the velocity gradient tensor, are demonstrated. Additionally, it proceeds to develop the strain measures required for the computation of the elastic forces of elements with circular cross sections, ultimately enabling the construction of the Forward Static Model (FSM) of continuum robots. A procedure based on B-spline surface geometry is proposed to figure out the robot shapes in space and generate the necessary nodal coordinates and gradients of ANCF elements. Finally, the inverse static model (ISM) of the ANCF-B-spline generated structure is presented using an iterative solution approach. The effectiveness of the proposed model is evaluated by the analysis of numerical examples. According to the numerical results, it can be inferred that the ANCF method well captures the modelling aspects of continuum robots. The results obtained from the FSM demonstrate a high level of accuracy, moreover, the driving forces based on the ISM provide, from a practical standpoint, satisfactory results. [ABSTRACT FROM AUTHOR]

In areas inaccessible to humans, such as the lunar surface and landslide sites, there is a need for multiple autonomous mobile robot systems that can replace human workers. Robots are required to remove water and sediment from landslide sites such as river channel blockages as soon as possible. Conventionally, several construction machines are deployed at civil engineering sites. However, owing to the large size and weight of conventional construction equipment, it is difficult to move multiple units of construction equipment to a site, which results in significant transportation costs and time. To solve such problems, this study proposes GREEMA: growing robot by eating environmental material, which is lightweight and compact during transportation and functions by eating environmental materials once it arrives at the site. GREEMA actively takes in environmental materials, such as water and sediment, uses them as its structure, and removes them by moving itself. In this study, two types of GREEMAs were developed and experimentally verified. First, we developed a fin-type swimming robot that passively takes in water into its body using a water-absorbing polymer and forms a body to express its swimming function. Second, we constructed an arm-type robot that eats soil to increase the rigidity of its body. We discuss the results of these two experiments from the viewpoint of explicit-implicit control and describe the design theory of GREEMA. [ABSTRACT FROM AUTHOR]

This paper proposes a novel tour guide robot, "ASAHI ReBorn", which can lead a guest by hand one-on-one while maintaining a proper distance from the guest. The robot uses a stretchable arm interface to hold the guest's hand and adjusts its speed according to the guest's pace. The robot also follows a given guide path accurately using the Robot Side method, a robot navigation method that follows a pre-defined path quickly and accurately. In addition, a control method is introduced that limits the angular velocity of the robot to avoid the robot's quick turn while guiding the guest. We evaluated the performance and usability of the proposed robot through experiments and user studies. The tour-guiding experiment revealed that the proposed method that keeps distance between the robot and the guest using the stretchable arm enables the guests to look around the exhibits compared with the condition where the robot moved at a constant velocity. [ABSTRACT FROM AUTHOR]

The integration of machine learning and robotics brings promising potential to tackle the application challenges of mobile robot navigation in industries. The real-world environment is highly dynamic and unpredictable, with increasing necessities for efficiency and safety. This demands a multi-faceted approach that combines advanced sensing, robust obstacle detection, and avoidance mechanisms for an effective robot navigation experience. While hybrid methods with default robot operating system (ROS) navigation stack have demonstrated significant results, their performance in real time and highly dynamic environments remains a challenge. These environments are characterized by continuously changing conditions, which can impact the precision of obstacle detection systems and efficient avoidance control decision-making processes. In response to these challenges, this paper presents a novel solution that combines a rapidly exploring random tree (RRT)-integrated ROS navigation stack and a pre-trained YOLOv7 object detection model to enhance the capability of the developed work on the NAV-YOLO system. The proposed approach leveraged the high accuracy of YOLOv7 obstacle detection and the efficient path-planning capabilities of RRT and dynamic windows approach (DWA) to improve the navigation performance of mobile robots in real-world complex and dynamically changing settings. Extensive simulation and real-world robot platform experiments were conducted to evaluate the efficiency of the proposed solution. The result demonstrated a high-level obstacle avoidance capability, ensuring the safety and efficiency of mobile robot navigation operations in aviation environments. [ABSTRACT FROM AUTHOR]

Mobile robots require the ability to plan collision-free paths. This paper introduces a wheel-foot hybrid parallel-leg walking robot based on the 6-Universal-Prismatic-Universal-Revolute and 3-Prismatic (6UPUR + 3P) parallel mechanism model. To enhance path planning efficiency and obstacle avoidance capabilities, an improved artificial potential field (IAPF) method is proposed. The IAPF functions are designed to address the collision problems and issues with goals being unreachable due to a nearby problem, local minima, and dynamic obstacle avoidance in path planning. Using this IAPF method, we conduct path planning and simulation analysis for the wheel-foot hybrid parallel-legged walking robot described in this paper, and compare it with the classic artificial potential field (APF) method. The results demonstrate that the IAPF method outperforms the classic APF method in handling obstacle-rich environments, effectively addresses collision problems, and the IAPF method helps to obtain goals previously unreachable due to nearby obstacles, local minima, and dynamic planning issues. [ABSTRACT FROM AUTHOR]

Ultra-wideband (UWB) has gained increasing interest for providing real-time positioning to robots in GPS-denied environments. For a robot to act on this information, it also requires its heading. This is, however, not provided by UWB. To overcome this, either multiple tags are used to create a local reference frame connected to the robot or a single tag is combined with ego-motion estimation from odometry or Inertial Measurement Unit (IMU) measurements. Both odometry and the IMU suffer from drift, and it is common to use a magnetometer to correct the drift on the heading; however, magnetometers tend to become unreliable in typical GPS-denied environments. To overcome this, a lightweight particle filter was designed to run in real time. The particle filter corrects the ego-motion heading and location drift using the UWB measurements over a moving horizon time frame. The algorithm was evaluated offline using data sets collected from a ground robot that contains line-of-sight (LOS) and non-line-of-sight conditions. An RMSE of 13 cm and 0.12 (rad) was achieved with four anchors in the LOS condition. It is also shown that it can be used to provide the robot with real-time position and heading information for the robot to act on it in LOS conditions, and it is shown to be robust in both experimental conditions. [ABSTRACT FROM AUTHOR]

Geomagnetic matching navigation is extensively utilized for localization and navigation of autonomous robots and vehicles owing to its advantages such as low cost, wide-area coverage, and no cumulative errors. However, due to the influence of magnetometer measurement noise, geomagnetic localization algorithms based on single-point particle filters may encounter mismatches during continuous operation, consequently limiting their long-range localization performance. To address this issue, this paper proposes a real-time sequential particle filter-based geomagnetic localization method. Firstly, this method mitigates the impact of noise during continuous operation while ensuring real-time performance by performing real-time sequential particle filtering. Then, it enhances the long-range positioning accuracy of the method by rectifying the trajectory shape of the odometry through odometry calibration parameters. Finally, by performing secondary matching on the preliminary matching results via the MAGCOM algorithm, the positioning error of the method is further minimized. Experimental results show that the proposed method has higher positioning accuracy compared to related algorithms, resulting in reductions of over 28.58%, 37.11%, and 0.77% in RMSE, max error, and error at the end, respectively. [ABSTRACT FROM AUTHOR]

Wheeled robots play a crucial role in driving the autonomy and intelligence of robotics. However, they often encounter challenges such as tracking loss and poor real-time performance in low-texture environments. In response to these issues, this research proposes a real-time localization and mapping algorithm based on the fusion of multiple features, utilizing point, line, surface, and matrix decomposition characteristics. Building upon this foundation, the algorithm integrates multiple sensors to design a vision-based real-time localization and mapping algorithm for wheeled robots. The study concludes with experimental validation on a two-wheeled robot platform. The results indicated that the multi-feature fusion algorithm achieved the highest average accuracy in both conventional indoor datasets (84.57%) and sparse-feature indoor datasets (82.37%). In indoor scenarios, the vision-based algorithm integrating multiple sensors achieved an average accuracy of 85.4% with a processing time of 64.4 ms. In outdoor scenarios, the proposed algorithm exhibited a 14.51% accuracy improvement over a vision-based algorithm without closed-loop detection. In summary, the proposed method demonstrated outstanding accuracy and real-time performance, exhibiting favorable application effects across various practical scenarios. [ABSTRACT FROM AUTHOR]

In this study an innovative parameterized water-bomb wheel modeling method based on recursive solving are introduced, significantly reducing the modeling workload compared to traditional methods. A multi-link supporting structure is designed upon the foundation of the water-bomb wheel model. The effectiveness of the supporting structure is verified through simulations and experiments. For robots equipped with this water-bomb wheel featuring the multi-link support, base on the kinematic model of multi-link structure, a mapping algorithm that incorporates parameterized kinematic solutions and IMU-fused parameterized odometry is proposed. Based on this algorithm, SLAM and autonomous navigation experiments are carried out in simulation environment and real environment respectively. Compared with the traditional algorithm, this algorithm the precision of SLAM is enhanced, achieving high-precision SLAM and autonomous navigation with a robot error rate below 5%. [ABSTRACT FROM AUTHOR]

In the field of aerospace, large and heavy cabin segments present a significant challenge in assembling space engines. The substantial inertial force of cabin segments' mass often leads to unexpected motion during docking, resulting in segment collisions, making it challenging to ensure the accuracy and quality of engine segment docking. While traditional manual docking leverages workers' expertise, the intensity of the labor and low productivity are impractical for real-world applications. Human-robot collaboration can effectively integrate the advantages of humans and robots. Parallel robots, known for their high precision and load-bearing capacity, are extensively used in precision assembly under heavy load conditions. Therefore, human-parallel-robot collaboration is an excellent solution for such problems. In this paper, a framework is proposed that is easy to realize in production, using human-parallel-robot collaboration technology for cabin segment docking. A fractional-order variable damping admittance control and an inverse dynamics robust controller are proposed to enhance the robot's compliance, responsiveness, and trajectory tracking accuracy during collaborative assembly. This allows operators to dynamically adjust the robot's motion in real-time, counterbalancing inertial forces and preventing collisions between segments. Segment docking assembly experiments are performed using the Stewart platform in this study. The results show that the proposed method allows the robot to swiftly respond to interaction forces, maintaining compliance and stable motion accuracy even under unknown interaction forces. [ABSTRACT FROM AUTHOR]

Recent advancements in autonomous mobile robots have led to significant progress in area coverage tasks. However, challenges persist in optimizing the efficiency and computational complexity of complete coverage path planner (CCPP) algorithms for multi-robot systems, particularly in scenarios requiring revisiting or a double pass in specific locations, such as cleaning robots addressing spilled consumables. This paper presents an innovative approach to tackling the double-pass complete coverage problem using an autonomous inter-reconfigurable robot path planner. Our solution leverages a modified Glasius bio-inspired neural network (GBNN) to facilitate double-pass coverage through inter-reconfiguration between two robots. We compare our proposed algorithm with traditional multi-robot path planning in a centralized system, demonstrating a reduction in algorithm iterations and computation time. Our experimental results underscore the efficacy of the proposed solution in enhancing the efficiency of area coverage tasks. Furthermore, we discuss the implementation details and limitations of our study, providing insights for future research directions in autonomous robotics. [ABSTRACT FROM AUTHOR]

Insect‐scale mobile robots can execute diverse arrays of tasks in confined spaces. Although most self‐contained crawling robots integrate multiple actuators to ensure high flexibility, the intricate actuators restrict their miniaturization. Conversely, robots with a single actuator lack the requisite agility and precision for planar movements. Herein, a novel eccentric rotation‐dependent multidirectional transmission is presented using a tilted eccentric motor and a simplistic two‐legged structural configuration for planar locomotion. The speed of the eccentric motor is modulated to enable alternating microscopic jumps to propel the system, creating a mode of motion analogous to galumphing of seals. Upon modeling the motion dynamics and conducting experiments, the effectiveness of direct motion transmission is substantiated through microscopic galumphing encompassing left/right crawling and straight‐forward crawling. Finally, a 1.2 g untethered robot is developed, which demonstrates enhanced straight crawling and spot turning, traverses narrow tunnels, and achieves precise movements. Therefore, the proposed motion‐transmission technique provides a comprehensive set of innovative solutions of underactuated agile robots. [ABSTRACT FROM AUTHOR]

This study explores the kinematic model of the popular RHex hexapod robots which have garnered considerable interest for their locomotion capabilities. We study the influence of tripod trajectory parameters on the RHex robot's movement, aiming to craft a precise kinematic model that enhances walking mechanisms. This model serves as a cornerstone for refining robot control strategies, enabling tailored performance enhancements or specific motion patterns. Validation conducted on a bespoke test bed confirms the model's efficacy in predicting spatial movements, albeit with minor deviations due to motor load variations and control system dynamics. In particular, the derived kinematic framework offers valuable insights for advancing control logic, particularly navigating in flat terrains, thereby broadening the RHex robot's application spectrum. [ABSTRACT FROM AUTHOR]

Motivated by the need to convert move-atomic assumption in LOOK-COMPUTE-MOVE (LCM) robot algorithms to be implemented in existing distributed systems, we define a new distributed fundamental task, the neighborhood mutual remainder (NMR). Consider a situation where each process has a set of operations O p and executes each operation in O p infinitely often in distributed systems. Then, let O e ⊂ O p be a subset of operations, which a process cannot execute, while its closed neighborhood executes operations in O p \ O e . The NMR is defined for such a situation. A distributed algorithm that satisfies the NMR requirement should satisfy the following two properties: (1) Liveness is satisfied if a process executes each operation in O p infinitely often and (2) safety is satisfied if, when each process executes operations in O e , no process in its closed neighborhood executes operations in O p \ O e . We formalize the concept of NMR and give a simple self-stabilizing algorithm using the pigeon-hole principle to demonstrate the design paradigm to achieve NMR. A self-stabilizing algorithm tolerates transient faults (e.g., message loss, memory corruption, etc.) by its ability to converge from an arbitrary configuration to the legitimate one. In addition, we present an application of NMR to an LCM robot system for implementing a move-atomic property, where robots possess an independent clock that is advanced at the same speed. It is the first self-stabilizing implementation of the LCM synchronization for environments where each robot can have limited visibility and lights. [ABSTRACT FROM AUTHOR]

We often face obstacles to visit certain places, facilities, and institutions. This was especially noticeable during the COVID-19 pandemic. In this period telepresence applications were of exceptional importance. They supported communications and provided social interactions, enriching our lives during isolation. In this paper, we propose a solution for telepresence using Padbot U1 robot and the MQTT protocol. The idea is to use this solution for virtual tours in museums, art galleries, historical sites, etc. We have developed two different applications. One is a client application used by the visitor, for controlling the robot remotely. The other application is for the mobile device (tablet) attached to the robot, which is used to enable telepresence and to forward control commands to the robot. By scanning the QR codes placed on the objects, visitors can also get specific information about each of objects. Communication between applications (sending commands and receiving information for objects) is realized with the MQTT protocol. For the developed solution, we also conducted experiments to evaluate some basic criteria for VTs. [ABSTRACT FROM AUTHOR]

With the rapid development of origami technologies, worm-inspired robots have attracted a great deal of attention due to their flexible locomotion characteristics. In the present work, we have prepared a soft robot inspired by the worms, which can achieve various locomotion patterns under the actuation of magnetic field. First, the origami technique is used to form the backbone of the robot, and two NdFeB discs are adhered on its two ends. Next, the experiments for controlling the Omega motion and rolling of the robot are performed, and the mechanical analyses are given. In the experiments, the Omega locomotion speed and rolling speed can reach ∼5 mm/s and 2π rad/s, respectively. Then, two typical examples on the composite motion, including the Omega motion and rolling, are demonstrated, where the robot can realize the tasks of sweeping objects and obstacle crossing in unstructured environments. We further design a system to mimic the situation when the worm-like robot detects and responds to the dangerous signal, and the power of the electromagnet can be accurately controlled. These findings cast a new light on engineering intelligent robots and devices originating from the inspirations of living creatures. [ABSTRACT FROM AUTHOR]

In the face of humanitarian crises such as torrential rainfall, resulting floods, and landslides, rapid rescue operations are often imperative. However, owing to the inherent dangers and unpredictability of such circumstances, immediate on-site aid delivery is frequently unfeasible. In such challenging scenarios, mobile robot systems have emerged as the optimal solution for aiding search and rescue efforts. The 'Informational system for management of flood and landslide disaster areas using a distributed heterogeneous robotic team' project, initiated by the International e-ASIA Joint Research Program, united research teams from Japan, Russia, and Thailand, each contributing unique expertize and experience towards common objectives. Drawing upon our extensive theoretical and practical knowledge in disaster response research, we developed an integrated international operational framework for disaster site management, centered on dispersed, heterogeneous semi-autonomous Unmanned Aerial Vehicles (UAVs), Semi-Autonomous Rough Terrain Vehicles (UGVs), and Semi-Autonomous Surface Vehicles (USVs). This paper provides an overview of the methodologies, models, and algorithms employed throughout the successful three-year project conducted by the Thailand team while also directing interested readers to supplementary research papers for in-depth insights. [ABSTRACT FROM AUTHOR]

This article presents the design, simulation, and experimental validation of a novel modular aquatic snake robot capable of surface locomotion. The modular structure allows each unit to function independently, facilitating ease of maintenance and adaptability to diverse aquatic environments. Employing the material point method with the moving least squares (MPM-MLS) simulation technique, the robot's dynamic behavior was analyzed, yielding reliable results. The control algorithm, integral to the robot's autonomous navigation, was implemented to enable forward propulsion at high speed, steering, and obstacle detection and avoidance. Extensive testing of the aquatic snake robot was conducted, demonstrating its practical viability. The robot showcased promising swimming capabilities, achieving high speeds and maneuverability. Furthermore, the obstacle detection and avoidance mechanisms were proven effective, showing the robot's ability to navigate through dynamic environments. The presented aquatic snake robot represents an advancement in the field of underwater robotics, offering a modular and versatile solution for tasks ranging from environmental monitoring to search and rescue operations. [ABSTRACT FROM AUTHOR]

In the face of today's ever-evolving global health landscape and ambient assisted living (AAL), marked by the persistent emergence of novel viruses and diseases that impact vulnerable categories and individual safety, the need for innovative disinfection solutions has surged to unprecedented levels. In pursuit of advancing the field of autonomous UV-C disinfection robotics, we conducted two comprehensive state-of-the-art analyses: the first one in the literature and the second one in existing commercial disinfection robots to identify current challenges. Of all of the challenges, we consider the most outstanding ones to be safeguarding humans and animals and understanding the surroundings while operating the disinfection process challenges that we will address in this article. While UV-C lamps have demonstrated their effectiveness in sterilizing air and surfaces, the field of autonomous UV-C disinfection robotics represents a critical domain that requires advancement, particularly in safeguarding the wellbeing of humans and animals during operation. Operating UV-C disinfection robots in close proximity to humans or animals introduces inherent risks, and existing disinfection robots often fall short in incorporating advanced safety systems. In response to these challenges, we propose the RoboCoV Cleaner—an indoor autonomous UV-C disinfection robot equipped with an advanced dual and redundant safety system. This novel approach incorporates multiple passive infrared (PIR) sensors and AI object detection on a 360-degree camera. Under our test, the dual-redundant system reached more than 90% when detecting humans with high accuracy using the AI system 99% up to 30 m away in a university hallway (different light conditions) combined with the PIR system (with lower accuracy). The PIR system was proved to be a redundant system for uninterrupted operation during communication challenges, ensuring continuous sensor information collection with a swift response time of 50 ms (image processing within 200 ms). It empowers the robot to detect and react to human presence, even under challenging conditions, such as when individuals wear masks, in complete darkness, under UV light, or in environments with blurred visual conditions. In our test, the detection system performed outstandingly well with up to 99% detection rate of humans. Beyond safety features, the RoboCoV Cleaner can identify objects in its surroundings. This capability empowers the robot to discern objects affected by UV-C light, enabling it to apply specialized rules for targeted disinfection. The proposed system exhibits a wide range of capabilities beyond its core purpose of disinfection, making it suitable for healthcare facilities, universities, conference venues, and hospitals. Its implementation has the ability to improve significantly human safety and protect people. By showcasing the RoboCoV Cleaner's safety-first approach and adaptability, we aim to set a new benchmark for UV-C disinfection robots, promoting clean and secure environments while protecting vulnerable people, even in challenging scenarios. [ABSTRACT FROM AUTHOR]

For improving the functionality, a quadruped robot must have the capabilities to manoeuvre over different terrain conditions like slope & stair climbing, obstacle avoidance, uneven & rough terrain, etc. In all such conditions, the primary requirement for a robot is to be stable while walking. In the present work, an attempt is made to develop a gait scheme of quadruped robots for statically stable straightforward stair climbing and descending, with horizontal posture. The use of a discontinuous type of gait in the proposed scheme makes it more stable and easier to implement. The proposed scheme has been simulated and its stability is validated. [ABSTRACT FROM AUTHOR]

A chain formation strategy based on mobile frames for a set of n differential drive mobile robots is presented. Considering two consecutive robots in the formation, robots Ri and Ri+1. It is intended that robot Ri+1 follows the delayed trajectory, τ units of time, of the leader robot Ri. In this way, the follower robot Ri+1 becomes the leader robot for robot Ri+ 2 in the formation and so on. With this formation policy, the trailing distance between two consecutive robots varies accordingly to the velocity of the Ri leader robot. Mobile frames are located on the body of the vehicles, in such a way that the position of robot Ri is determined with respect to the frame located on Ri+1 robot. The strategy relies on the fact that the general leader robot R1 describes any trajectory generated by bounded linear v1(t) and angular ω1(t) velocities. For the remaining vehicles in the string, the strategy considers a desired trajectory for the follower robot Ri+1 obtained by an estimation of the delayed trajectory of the leader robot Ri. This desired estimated trajectory is obtained under the knowledge of the actual and past input velocities of the Ri robot. To formally prove the convergence of the formation strategy, the equations describing the time variation of the relative posture between any pair of consecutive vehicles in the formation are obtained, and a feedback law based on local measurements is proposed to get the convergence of robot Ri+1 to the delayed trajectory, τ units of time, of the trajectory previously described by robot Ri. Lyapunov techniques are considered for this fact. The effectiveness of the chain formation solution is evaluated by means of numerical simulations and real time experiments showing an adequate convergence. [ABSTRACT FROM AUTHOR]

This literature review investigates how self-driving autonomous delivery robots (ADRs) impact last-mile deliveries, add value to the logistics and transport industry, and contribute to creating competitive business models. Autonomous vehicles are still a developing technology and ADRs could possibly be one of the solutions to the last-mile problem, in particular in cities and for urban freight with an increasing number of parcels to deliver. Last-mile delivery is also changing as e-commerce and more demanding customers emerge. Such development, however, faces challenges regarding infrastructure, externalities such as CO2 emissions, and shorter delivery-time requirements. This review, focused on ADRs, reveals four major themes (operations, infrastructure, regulations, and acceptance) through which we explain the barriers and benefits of using ADRs for last-mile deliveries. The review shows that the operations of ADRs can impact last-mile deliveries by lowering costs, optimising the use of time, and reducing externalities. The review also shows that the foundation of last-mile infrastructure would have to change if ADRs are to be used to a greater extent. Regulations for ADRs are still not yet in place, which makes the market somewhat confused. The acceptance of ADRs in society is another challenge because the innovation of ADRs is still new and unfamiliar. Altogether, the use of ADRs for last-mile deliveries shows great potential, based on the promising results of the articles reviewed. However, most studies on ADRs have been theoretical in nature, such as models, which highlights the need for real-world case studies and implementations. [ABSTRACT FROM AUTHOR]

Continuum robots are complex structures that require sophisticated modeling and control methods to achieve accurate position and motion tracking along desired trajectories. They are highly coupled, nonlinear systems with multiple degrees of freedom that pose a significant challenge for conventional approaches. In this paper, we propose a system dynamic model based on the Euler–Lagrange formulation with the assumption of piecewise constant curvature (PCC), where we accounts for the elasticity and gravity effects of the continuum robot. We also develop and apply a particle swarm optimization (PSO) algorithm to optimize the parameters of our developed controllers: an inverse dynamic proportional integral derivative (PID) controller and an inverse dynamic fuzzy logic controller (FLC), where we use the integral time of absolute error (ITAE) as the objective function for the PSO algorithm. We validate our proposed model and optimized controllers through different designed trajectories, simulated using our developed unique animated MATLAB simulation. The results show that the PSO-PID controller improves the rise time, overshoot percentage, and settling time by 16.3%, 31.1%, and 64.9%, respectively, compared to the PID controller without PSO. The PSO-FLC controller shows the best performance among all controllers, with a settling time of 0.7 s and a rise time of 0.4 s, leading to the highest level of precision in trajectory tracking. The ITAE error for the PSO-FLC controller is 11.4% and 29.9% lower than that of the PSO-PID and FLC controllers, respectively. [ABSTRACT FROM AUTHOR]

The paper presents an approach for controlling a linefollowing robot using artificial intelligence algorithms. This study aims to evaluate and validate the design and implementation of a competitive line-following robot based on multilayer neural networks for controlling the torque on the wheels and regulating the movements. The configuration of the line-following robot consists of a chassis with a set of infrared sensors that can detect the line on the track and provide input data to the neural network. The performance of the line-following robot on a running track with different configurations is then evaluated. The results show that the line-following robot responded more efficiently with an artificial neural network control algorithm than with a PID control or fuzzy control algorithm. At the same time, the reaction and correction time of the robot to errors on the track is earlier by about 0.1 seconds. In conclusion, the capabilities of a neural network allow the line-following robot to adapt to environmental conditions and overcome obstacles on the track more effectively. [ABSTRACT FROM AUTHOR]

The ability to learn continuously is crucial for a robot to achieve a high level of intelligence and autonomy. In this paper, we consider continual reinforcement learning (RL) for quadruped robots, which includes the ability to continuously learn sub-sequential tasks (plasticity) and maintain performance on previous tasks (stability). The policy obtained by the proposed method enables robots to learn multiple tasks sequentially, while overcoming both catastrophic forgetting and loss of plasticity. At the same time, it achieves the above goals with as little modification to the original RL learning process as possible. The proposed method uses the Piggyback algorithm to select protected parameters for each task, and reinitializes the unused parameters to increase plasticity. Meanwhile, we encourage the policy network exploring by encouraging the entropy of the soft network of the policy network. Our experiments show that traditional continual learning algorithms cannot perform well on robot locomotion problems, and our algorithm is more stable and less disruptive to the RL training progress. Several robot locomotion experiments validate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Understanding elastic instability has been a recent focus of concentric tube robot research. Modeling advances have enabled prediction of when instabilities will occur and produced metrics for the stability of the robot during use. In this paper, we show how these metrics can be used to resolve redundancy to avoid elastic instability, opening the door for the practical use of higher curvature designs than have previously been possible. We demonstrate the effectiveness of the approach using a three-tube robot that is stabilized by redundancy resolution when following trajectories that would otherwise result in elastic instabilities. We also show that it is stabilized when teleoperated in ways that otherwise produce elastic instabilities. Lastly, we show that the redundancy resolution framework presented here can be applied to other control objectives useful for surgical robots, such as maximizing or minimizing compliance in desired directions. [ABSTRACT FROM AUTHOR]

In modern applications, mobile robots are important dynamic systems. Motion planning is a crucial responsibility for these robots as it enables them to move from one place to another safely and efficiently. Extensive research has been conducted on motion planning for static scenarios. However, research is limited when the scenarios are dynamic because it is difficult and expensive to continuously replan the robot's moves to ensure its safety. This paper presents the circular arc path algorithm for sensorless differential drive mobile robot movement, given that the initial point and endpoint are defined. The differential drive mobile robot, which is adopted for various vehicles and robots, is very popular due to its simple operation and robustness. A mobile robot with two driving wheels that uses a differential drive splits its overall velocity between its left and right wheels. Accordingly, the radius of the circle that forms after movement between two points is the main factor that determines the left and right velocity of the robot's wheels. Through this algorithm, all points will be interpolated with G1 continuity, which only requires an initial direction to move. Robot simulation is performed using CoppeliaSim, and the algorithm is developed using Remote API functions in Matlab. [ABSTRACT FROM AUTHOR]