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A mobile agent, which is an autonomous device navigating in a graph, has to explore a given graph by visiting all of its nodes. We adopt the (arguably) weakest possible model of such a device: a deterministic memoryless automaton (DMA), i.e., a deterministic automaton with a single state. As expected, such a weak machine is incapable of exploring many graphs without marking nodes. Hence we allow the agent to use identical movable pebbles that can be dropped on nodes or picked from them. It turns out that this marking capability significantly enhances the exploration power of the agent. Our goal is to study how the availability of pebbles impacts the class of graphs that a DMA can explore. We first concentrate on finite graphs and show that any finite tree can be explored by a DMA without pebbles but there exist (small) finite graphs that cannot be explored by a DMA without pebbles. Then we turn attention to infinite graphs and fully characterize the class of infinite trees that can be explored by a DMA without pebbles. We also define a large class of infinite trees that can be explored by a DMA with finitely many pebbles. It turns out that many of these trees cannot be explored by a DMA without pebbles. On the other hand, we show a large class of infinite trees that cannot be explored by a DMA with finitely many pebbles. Thus, availability of pebbles yields a strict hierarchy of difficulty of exploration of infinite graphs by a DMA, and this hierarchy is strict even for the class of infinite trees: some trees can be explored without pebbles, some trees can be explored with finitely many pebbles but not without pebbles, and some trees require infinitely many pebbles. Finally, we consider exploration by a DMA with infinitely many pebbles. It turns out that all infinite trees can be explored by a DMA with infinitely many pebbles. By contrast, we construct infinite graphs that cannot be explored by any DMA, even with infinitely many pebbles. [ABSTRACT FROM AUTHOR]

This paper proposes a novel adaptive variable power sliding mode observer-based model predictive control (AVPSMO-MPC) method for the trajectory tracking of a Mecanum-wheeled mobile robot (MWMR) with external disturbances and model uncertainties. First, in the absence of disturbances and uncertainties, a model predictive controller that considers various physical constraints is designed based on the nominal dynamics model of the MWMR, which can transform the tracking problem into a constrained quadratic programming (QP) problem to solve the optimal control inputs online. Subsequently, to improve the anti-jamming ability of the MWMR, an AVPSMO is designed as a feedforward compensation controller to suppress the effects of external disturbances and model uncertainties during the actual motion of the MWMR, and the stability of the AVPSMO is proved via Lyapunov theory. The proposed AVPSMO-MPC method can achieve precise tracking control while ensuring that the constraints of MWMR are not violated in the presence of disturbances and uncertainties. Finally, comparative simulation cases are presented to demonstrate the effectiveness and robustness of the proposed method. • kinematic and dynamic constraints are handled efficiently. • The improved observer has a fast convergence rate without chattering. • The method can improve tracking performance. [ABSTRACT FROM AUTHOR]

This paper addressed the robust distributed fixed-time cooperative hunting problem of multiple quadrotors subject to disturbances in obstacles environment. To handle the underactuated issue inherent in quadrotor dynamics, an inner–outer (attitude-position) loop cascade control configuration is proposed to achieve the cooperative flight control of quadrotors. For position subsystem, as the information of target cannot be accessible to all quadrotors, a distributed fixed-time observer is devised to estimate the target's information. To improve the system's robustness, a fixed-time extended state observer is designed to reject disturbances actively. Based on two observers, the sliding mode position encirclement control protocol with repulsive force is presented to avoid obstacles and encircle the target within a fixed time. For attitude subsystem, a sliding mode attitude tracking control protocol is proposed such that tracking errors can converge to zero even under disturbances. The stability analysis is performed to show the stabilization of the whole closed-loop system with fixed-time convergence. Finally, two sets of comparison simulation are provided to show the superiority of the developed control strategy. • A distributed fixed-time observer is designed to estimate the target's information. • Two fixed-time extended state observers are presented to compensate the effects of disturbances. • The sliding mode position controller and attitude tracking controller are developed to avoid obstacles and encircle the target. [ABSTRACT FROM AUTHOR]

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

When legged robots perform complex tasks in unstructured environments, falls are inevitable due to unknown external disturbances. However, current research mainly focuses on the locomotion control of legged robots without falling. This paper proposes a comprehensive decision-making and control framework to address the falling over of quadruped robots. First, a capturability-based fall prediction algorithm is derived for planar single-contact and 3D multi-contact locomotion with a predefined gait sequence. For safe fall control, a novel contact-implicit trajectory optimization method is proposed to generate both state and input trajectories and contact mode sequences. Specifically, incorporating uncertainty into the system and terrain models enables mitigating the non-smoothness of contact dynamics while improving the robustness of the resulting trajectories. Furthermore, a model-free deep reinforcement learning-based approach is presented to achieve fall recovery after the robot completes a fall. Experimental results demonstrate that the proposed fall prediction algorithm accurately predicts robot falls with up to 95% accuracy approximately 395ms in advance. Compared to classical locomotion controllers, which often struggle to maintain balance under significant pushes or terrain perturbations, the presented framework can autonomously switch to the fall controller approximately 0.06s after the perturbation, effectively preventing falls or achieving recovery with a threefold reduction in touchdown impact velocity. These findings highlight the effectiveness of the proposed framework in enhancing the stability and safety of legged robots in unstructured environments. • The proposed fall prediction approach exhibits significantly enhanced reliability and flexibility. • The proposed controller can autonomously generate contact sequences, reducing damage from falls. • The designed reinforcement learning-method improves efficiency and robustness in fall recovery. [ABSTRACT FROM AUTHOR]

Volatility in supply and demand caused by global disruptions such as wars or pandemics requires adaptable and changeable production systems. Since assembly accounts for significant production time and costs, the demand for changeable, line-less assembly systems is advancing. Controlling mobile robots in line-less assembly depends on understanding task executability. We propose the implementation of performance maps to evaluate the executability of assembly tasks within robot workspaces. Firstly, established performance metrics and typical assembly tasks are categorized to identify which metrics evaluate the executability of which type of tasks. The assembly tasks are grouped according to the type of movement (continuous or discrete), the required execution precision (high or low), and the amount of poses for execution (reachable or dexterous). The metrics are categorized according to their range (local or global), their physics (kinematic or dynamic), task reference (intrinsic or extrinsic), and scale (absolute or relative). Metrics are then matched to task types. This matching provides a systematic way to identify metrics to assess the executability of a task. Secondly, the performance map is presented. The performance map is a discretized representation of the distribution of chosen performance metrics for a specific robot. The current implementation is restricted to calculating the manipulability, dexterity, and condition number. Based on the input of a robot model and a task type, the metrics are calculated in distributed poses for a given resolution in the robot's workspace to form the performance map. The performance map is applied to exemplary tasks and robots. Previous approaches to workspace evaluation fail to consider the suitability of performance metrics to evaluate specific tasks, as different metrics are more or less relevant for different tasks. Consequently, the paper contributes by introducing performance maps and providing quantific metrics for comparing base placements of mobile robots according to the executability of specific assembly tasks. [ABSTRACT FROM AUTHOR]

• A novel redundantly actuated series-parallel manipulator is proposed. • Unified recursive kinematics and statics models of the manipulator are established. • The theoretical model is validated by comprehensive numerical results. • Load/mass ratio of the tested manipulator in non-redundant actuation mode is 1.34. • The manipulator has higher load-carrying capacity in redundant actuation mode. Construction robots with mobile systems are an integral part of automatic building processes. However, with the vigorous development of prefabricated buildings, traditional manipulators have progressively failed to satisfy the requirements of handling heavy materials on construction sites. Thus, this paper proposes a redundantly actuated series-parallel manipulator (RAS-PM). To explore the feasibility of applying the manipulator in construction tasks, this paper establishes the unified recursive kinematics and statics of the manipulator and verifies the validity and reliability of the theoretical modeling method through comprehensive numerical results. The prototype of the manipulator is developed and installed on a bionic hexapod construction robot. The test results demonstrate that the manipulator exhibits an excellent load/mass ratio in non-redundant actuation mode, and its load-carrying capacity can be further enhanced with the optimized force/position hybrid actuation. In the on-site test, the robot can freely switch between modes to handle horizontal and vertical states of materials, such as curtain walls and large panels, thereby contributing to modern prefabricated construction. [ABSTRACT FROM AUTHOR]

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

Accurate wheel slip estimation facilitates Wheeled Mobile Robots (WMRs) with improved localization and traversability monitoring which are crucial to their autonomy in challenging planetary environments. Although distributed track-level fusion offers better computational efficiency, often sensor-level fusion is adopted in slip estimators of planetary rovers. Extending upon our earlier work, we develop a novel explicit track-to-track fusion algorithm for multi-sensor networks of unscented Kalman filters that has immediate application to slip ratio estimation in WMRs. The algorithm demonstrates better consistency compared to the existing fusion techniques since it implements a sub-optimal fusion rule to sequentially combine local tracks. It also saves computational power due to the recursive propagation of cross-covariance matrices based on the statistical linearization technique, instead of performing online optimizations. We prove that this recursion only requires the information of the first and last tracks in a sequence if all local tracks are unbiased. We rigorously study various key properties of the developed fusion algorithm and show its superior level of confidence when compared to the two prominent fusion methods of sequential and batch covariance intersection. The slip ratio estimation in a six-wheel WMR is considered as a case study, where the steerable wheel sets act as a network of sensors. The proposed slip estimator works based on the rigid body kinematics and readings of purely proprioceptive sensors, i.e., an inertial measurement unit and encoders. The slip estimator's performance is evaluated in a high-fidelity software-in-the-loop simulation environment connecting MATLAB and CM Lab's Vortex Studio software. In a comparison study that considers four rival strategies, we show the superiority of the proposed fusion method offering a balance between consistency, accuracy, and speed in real-time slip estimations. • Developing a sequential track-to-track fusion algorithm considering correlations. • Recursively propagating the correlations based on statistical linearization. • Using only proprioceptive sensors for slip estimation of planetary wheeled rovers. • Providing a balance between accuracy, consistency and efficiency in slip estimation. [ABSTRACT FROM AUTHOR]

• 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]

Wheeled Mobile Robots (WMRs) are systems with applications in diverse fields such as transportation, civilian services, military use, and space exploration. Then, their use will continue increasing, making WMRs an essential research topic that deserves further study. To this end, this work presents a novel observer-based finite-time controller for trajectory tracking control of WMRs disturbed by kinematic disturbances. In the proposed approach, the kinematic model of the WMR is transformed into a set of two decoupled second-order systems. Then, the proposed controller is divided into two parts. The first one employs an observer to estimate the effect of the kinematic disturbances. The second part consists of a finite-time controller designed to achieve finite-time convergence of the tracking error. A detailed synthesis procedure theoretically demonstrates the feasibility of the proposed controller. Subsequently, the proposed scheme is compared against finite-time, feedback, and H ∞ controllers. Exhaustive numerical simulations show that the proposed new control methodology achieves the trajectory tracking objective despite kinematic disturbances and outperforms the other control procedures. Finally, some comments and numerical results are given to clarify how the proposed control methodology can be used to design new controllers for trajectory tracking in WMRs and demonstrate that the new proposal remains to have a good performance when the system's coordinates are corrupted by measurement noise. [Display omitted] • A new observer-based finite-time control for wheeled mobile robots is proposed. • The new control scheme permits achieving the trajectory-tracking task efficiently. • The novel methodology outperforms existing finite-time, feedback, and H ∞ methodologies. • Excellent performance is obtained even in the presence of kinematic disturbances and measurement noise. • The proposed scheme permits the design of new controllers for WMRs easily. [ABSTRACT FROM AUTHOR]

To improve the payload capacity and maneuverability of a Differentially-Driven Wheeled Robot (DDWR), a wheeled vehicle, which is called trailer, is connected to the DDWR. In all of the previous studies of DDWRs with a trailer, the robot wheels are subject to pure rolling constraints. However, when these multibody systems move with high velocities/accelerations, transfer a heavy payload, or travel on a slippery surface, they experience slipping and/or skidding. In the present study, a Tractor-Trailer Wheeled Mobile Robot (TTWMR) is modeled whose wheels may slip in lateral and longitudinal directions. To this end, Lagrange formalism is employed along with the LuGre friction model to derive dynamics of the considered multibody system. Next, the problem of path following for the trailer is addressed. Toward this goal, the partial feedback linearization technique will be utilized. The obtained simulation results prove the superiority of the performance of the suggested method in comparison to the previous studies. Additionally, the response of the system in the presence of the external disturbances and uncertainties in system parameters will be examined. • The lateral and longitudinal slipping of wheels are considered in the modeling. • A novel controller is proposed to path following of wheeled mobile robots. • The performance of the controller has been compared against a previous study. [ABSTRACT FROM AUTHOR]

LiDAR-based place recognition (LPR) aims to localize autonomous vehicles and mobile robots relative to pre-built maps or retrieve previously visited places. However, the complexity of real-world scenes and changes in viewpoint are significant challenges for place recognition. As high-level information, semantics makes it easier to distinguish geometrically similar scene situations. Unlike most existing methods that rely solely on a single type of information (geometric or semantic) to construct scene descriptors, we consider the complementary nature of the semantic and geometric information and propose a semantics-enhanced discriminative feature learning method for LPR. Specifically, we first develop a Multi-layer Fusion Feature Extraction Network (MFFEN) based on the transformer encoder to hierarchically fuse local geometric and semantic information and utilize contextual information for extracting discriminative local features. To obtain semantic information, we introduce the dynamic graph convolution network to extract local semantic features with local relations. In addition, to weaken the interference of redundancy and dynamic objects in the scene, we design a semantics-guided local attention network (SLAN) to focus on salient local features that are helpful for recognizing scenes, thereby enhancing the descriptive ability of the global descriptor. Extensive experiments on public datasets KITTI and KITTI-360 demonstrate that the proposed method performs better than recent LiDAR-based methods on the 3D place recognition task. For instance, it achieves the mean F1 max score of 96.9% on the KITTI dataset, surpassing the strongest prior model by 2.7%. [ABSTRACT FROM AUTHOR]

The integration of the metaverse with advanced communication networks like 6G underscores the importance of optimizing user experiences. This research introduces a novel approach to addressing traffic prioritization within the metaverse using the Parallel Tempering (PT) algorithm. To ensure adaptive traffic priority management, especially due to the dynamic nature of user interaction, our framework uses temperature in PT chains to model this nature. User activities differ in urgency and importance, each with their respective chains running parallelly with different temperatures. The choice between higher or lower temperatures depends on the application to either exploration (higher temperatures) or exploitation (lower temperatures), which allow adaptation for activity prioritization. As part of the parallel tempering algorithm, chains swap to keep the states with lowest loss with the chains of lower temperature. This exchange of information between chains models the adaptation to user interactions in the metaverse. This research contributes to an adaptive framework for metaverse traffic prioritization, enhancing communication network responsiveness. Our experiments show 7.49% improvement in network latency using the k-medoids algorithm when integrated with parallel tempering in comparison to the traditional k-medoids approach. [ABSTRACT FROM AUTHOR]

The widespread use of wheeled mobile robots (WMRs) in many fields has created new challenges. A critical issue is wheel slip, which, if not accurately determined and controlled, causes instability and deviation from the robot's path. In this paper, an intelligent approach for estimating the longitudinal and lateral slip of wheels is proposed that can effectively compensate for the negative effects of slippage. The proposed algorithm relies on three regression networks to estimate the longitudinal slip ratio of the right and left wheels and sideslip angle on terrains with different friction coefficients. The datasets collected during tests on different surfaces with various maneuvers are used to train the artificial neural networks (ANNs). A developed dynamic model of a WMR considering wheel slip and modified traction force is presented. The adaptive robust controller, based on sliding mode control (SMC), is introduced to deal with the problems related to slipping, unknown uncertainties, and disturbances. The simulation results demonstrate that the presented controller has better performance than SMC in handling external disturbances and uncertainties, which leads to reduction in tracking error and faster convergence to zero. The proposed controller with an intelligent slip estimator, has been applied to a four-wheel mobile robot to demonstrate its effectiveness and feasibility. The high accuracy of slip estimation in the mentioned intelligent algorithm has resulted in the presented method being on average 26% more effective in reducing the tracking error than the control method without slip compensation in each test for circular trajectory. • Developing a dynamic model of WMR using modified traction force. • Proposing slip estimator based on an intelligent learning for both longitudinal and lateral slip. • Eliminating the time delay in slip estimation and compensating for its effect in the same time step by predicting the slip amount. • Improved robot performance in trajectory tracking on slippery surfaces. [ABSTRACT FROM AUTHOR]

Autonomous routing through curved path for point-to-point mobile robot is a well-established challenging problem in the area of intelligent path navigation. Point-to-point collision-free locomotion of AGV (Automated Guided Vehicle) requires precise differentiation of movable path from other environmental visual noise. As these path navigating depend on various visual cues and precepts, naturally the semantic meaning of path in contrast to other closely available objects on the visual window requires significant amount of understanding of the input from precepts. In this paper, it is experimentally established that semantic segmentation has proved to be a concise method in making comparative better segregation of paths from midway structured obstacles that are with high computational cost. The claims are also evident from surveyed works. The challenge of reducing high computational performance with efficient segmentation of movable area has been addressed in this research work with a focus on deriving better filtered accessible paths with a degree of algorithmic confidence. Further, this segmented result is taken for curved trajectory tracking by the AGV. A systematic percept mediated deep based Semantic-region Aware Model Predictive Trajectory Tracking(SAMP-TT)Algorithm has been proposed to solve the problem of precise obstacle detection on curved path trajectories. RGB (Red, Green Blue) image of outdoor multi-sectioned area has been collected by Intel Realsense D455 which worked as input feed to the respective neural network models. The performance of the proposed technique has been benchmarked with existing segmentation models including ENET (Efficient Neural Network), U-NET to confirm the considerable reliability and accuracy of the presented algorithm. It has been followed by the state-of-art comparative visual result analysis of different taken models with the proposed architecture. [ABSTRACT FROM AUTHOR]

Simultaneous Localization and Mapping enable a mobile robot that is exploring an uncharted environment to localize itself and calculate its path within the map. In the context of green technologies and applications, there is a growing need for efficient SLAM solutions that not only provide accurate localization and mapping but also minimize power consumption. EKF-SLAM is a SLAM solution based on the Extended Kalman Filter, it is well known In the domain of robotics for its ability to handle non-linear models, its ability to handle noise related to the sensors, and its extremely high degree of precision. To guarantee real-time performance, the EKF-SLAM implementation requires a high-performance hardware architecture. In light of this challenge, researchers are thinking about using parallel processing platforms like FPGAs, which can provide the required level of performance and meet strict constraints on physics, computing capacity, and electrical power. This study describes a hardware architecture's implementation design for EKF-SLAM on an FPGA platform. The entire design is built on the Cyclone 2 FPGA, which has a maximum speed of 114 MHz and 18577 LUTs, creating a highly efficient hardware architecture. [ABSTRACT FROM AUTHOR]

In this paper, the novel leader-following tracking control method is proposed for mobile robots, which consists estimation technique of the speed of the leader robot (LR), and a parameter-dependent controller for the follower robot (FR). To estimate the speed of LR, a novel Physics Informed Machine Learning (PIML) is proposed to learn the dynamics of the state observer via the error state model. The dynamics of the state observer in PIML play a significant role for stable learning and state estimation of uncertain models. The gain of the parameter-dependent controller is determined by the convex combination of the robust control technique via the polytopic model. Finally, the tracking performance of the proposed method is verified through the simulation and experiment. • A novel PIML with the state observer is proposed to learn physical equations. • A parameter estimation method is developed for the time-varying uncertain systems. • The performance of the parameter-dependent controller is verified by the experiment. [ABSTRACT FROM AUTHOR]

The uncertainty in mobile robot greatly affects control accuracy. This makes it difficult to apply to more rigorous high-precision engineering fields. Therefore, the fuzzy set theory is introduced to describe the uncertainty. Based on that, the fuzzy mobile robot system is established. The virtual speed controller using backstepping method is designed. Then, a robust control method is proposed to guarantee the uniform boundedness and uniform ultimate boundedness of the controlled system. Furthermore, the balance optimization problem of the performance and cost of the controlled system is explored. By minimizing the performance index containing fuzzy numbers, the optimal control parameter is obtained. Compared with the linear quadratic regulator algorithm, which is the representative optimal robust controller, the proposed control method and optimization strategy based on fuzzy set theory are verified to be effective. The control accuracy is further improved. • The kinematic and dynamic models of the mobile robot are established. • A virtual speed controller is designed by using the backstepping method. • A deterministic robust control is proposed to ensure the system performance. • A fuzzy performance index is introduced for optimizing parameter. [ABSTRACT FROM AUTHOR]

[Display omitted] • This study shows how the delta robot was designed to physically pick-and-place MSW. • Combined with the YOLO-based model, the robot forms the Intelligent MSW Sorter. • IMSWS could automatically sort the multiple MSWs on the conveyor belt. • The mechanical efficiency of the robot dominated IMSWS performance. • Compared with the MSW detection, IMSWS must improve the mechanical efficiency first. Sorting Municipal Solid Waste (MSW) has helped promote the awareness of sustainable development of environment. A robot equipped with an intelligent deep learning (DL) detection algorithm have been proposed to improve the sorting task. But most of the related studies aimed to better the DL algorithms on MSW detection, and few studies integrated the DL algorithms with a robot to identify the dominated factors to Intelligent MSW Sorter (IMSWS). Therefore, this study is to develop IMSWS prototype to better sort MSW, based on the pick-and-place process, and preliminarily evaluate the dominated factors. First, the delta robot prototype was manufactured, and IMSWS was performed with a camera to acquire the RGB image and the height of a MSW in the conveyor belt. The DL algorithm, YOLOv3 or YOLOv4, detected the type and plane location of the MSWs in the conveyor belt. Next, the sequence program transferred the valid MSW data to the delta robot. After the calculation of the absorbed location of the target MSW was made, the arm of this delta robot moved to absorb and then transfer the MSW to the bin. Results showed that the IMSWS prototype could sort the multi-object MSWs in the MSW stream. Both YOLOv3 and YOLOv4 reached high detection accuracy on the MSW image dataset. However, the improvement should be made in the actually moving MSW stream even though the YOLOv4 performed the acceptable detection accuracy. The gripping stability of the arm mainly dominated the performance of IMSWS, and this should be improved first. [ABSTRACT FROM AUTHOR]

In this paper, a distributed formation tracking protocol is proposed for multi-robot systems with switching directed topologies based on the Udwadia-Kalaba approach. The basic idea is to use the consensus-based scheme to reconstruct formation tracking control requirement into a second-order constraint first, and then apply the Udwadia-Kalaba approach to obtain the constraint force required to achieve the formation tracking, and afterward derive the explicit equations of motion for the constrained multi-robot systems for the control algorithm design. The convergence analysis of the tracking error system is conducted to affirm that the proposed control algorithm can achieve the formation tracking when the switching directed topologies have a directed spanning tree across each switching time interval. Numerical simulations are performed to validate the effectiveness of the proposed algorithm. • The proposed strategy reconstructs the formation tracking requirement as a constrained motion problem. • The constraint forces for the kinematic performance constraints are formulated in an explicit form. • The proposed strategy is based on switching directed topologies and only needs to set two control parameters. [ABSTRACT FROM AUTHOR]

Autonomous Mobile Robots, as the advanced version of Automated Guided Vehicles have received a lot of interest and recognition in recent years. Simultaneous Localization and Mapping (SLAM) techniques enable the vehicles to independently navigate and map their surroundings so that they can drive autonomously in changing and uncharted areas. Due to the increasing importance and contributive development of SLAMs for automated guided vehicles and autonomous mobile robots, this study seeks to provide an in-depth analysis of well-known SLAM techniques developed and applied during the previous ten years. Well-known SLAM algorithms considered in this paper include GMapping, Cartographer, LIO-SAM, and so on. They are mainly examined and compared from the viewpoints of basic principles, sensor requirements, computing complexity, and performance. The aim of this paper is to offer insights into various SLAM approaches to researchers, practitioners, and developers in the field of automated guided vehicles and autonomous mobile robots, facilitating the selection of suitable SLAM methods for specific applications and fostering innovation in autonomous navigation and mapping. [ABSTRACT FROM AUTHOR]

Swarm Production is a novel paradigm promoting a high degree of flexibility in material flow and reconfigurability in shop floor layout, enabling high-mix production in manufacturing through the induction of autonomous robots. The conceptual description promises a leap within smart manufacturing and the Industry 4.0 domain. Implementing this theoretical concept becomes crucial to the self-organising production domain and gradually learning the relevant technological stack, optimisation methods, objective functions, and shop floor constraints needed for Swarm Production. This research paper addresses the challenges of the simulation of autonomous mobile robots in the production environment and concludes with an exemplified case simulation. A simulation testbed visually demonstrates the concept with planning, scheduling and control systems, providing their feasibility and reliability. [ABSTRACT FROM AUTHOR]

In recent times, the use of autonomous mobile robots (AMRs) has increased in many industries. As more AMRs roam the same area, traffic management becomes essential to prevent congestion and deadlocks. In related work, traffic management is often achieved using sophisticated, centralised planning approaches, albeit this often suffers from scalability issues. This paper therefore explores an approach where multi-AMR traffic management is achieved with layered costmaps and a modified Dijkstra's algorithm. This keeps the global path planner in the individual AMRs, thus not suffering scalability issues. To achieve multi-AMR coordination, traffic lanes and restricted areas are added to the AMRs' global costmaps. Furthermore, the AMRs also use a modified Dijkstra's algorithm that supports implementation of traffic directions. A proof-of-concept solution is implemented in Robot Operating System 2 with Nav2 and Gazebo. The implemented solution was tested against a standard solution without any traffic management in three scenarios designed to provoke collisions. The results indicate that the implemented solution can prevent a set of collisions better than one without traffic management. [ABSTRACT FROM AUTHOR]

Autonomous robots that understand human instructions can significantly enhance the efficiency in human-robot assembly operations where robotic support is needed to handle unknown objects and/or provide on-demand assistance. This paper introduces a vision AI-based method for human-robot collaborative (HRC) assembly, enabled by a large language model (LLM). Upon 3D object reconstruction and pose establishment through neural object field modelling, a visual servoing-based mobile robotic system performs object manipulation and navigation guidance to a mobile robot. The LLM model provides text-based logic reasoning and high-level control command generation for natural human-robot interactions. The effectiveness of the presented method is experimentally demonstrated. [ABSTRACT FROM AUTHOR]

Wheeled mobile robots (WMRs) have a wide range of applications in logistics transportation and industrial productions, among which the motion control has always been one of the hot spots in the current WMR researches. However, most of previous designed controllers assumed that the WMR motion had no slippage. Ignoring the slippage factors usually results in a decrease in control performance and even leads to unstable motion. To address such a challenge, a kinematic model with differential flatness is established through dynamic feedback-linearization, which comprehensively considers the multidirectional slippage of mobile robot, including longitudinal and steering slippage. Subsequently, benefited from the one-to-one mapping of states and inputs to flat outputs in differential flat system, an adaptive robust control (ARC) method is proposed to stabilize the system. Different from previous robust control studies, even if the knowledge of the upper bound of system uncertainties is unknown in advance, the proposed adaptive robust controller can still achieve satisfying performance by adaptive estimation of the upper bound of system uncertainties. The effectiveness and feasibility of the proposed method are confirmed by comparative experiments on WMR with slippage disturbance. [ABSTRACT FROM AUTHOR]

• A new model for a vehicle is developed and validated using real- experimental data from an automotive software. • The model is applied to a mobile agriculture robot to develop a coupled control law. • The coupled controller is implemented in cascade with a slip ratio controller using terramechanics law. • The results of this control law give us good results in low velocity compared to high velocity. In this paper, the coupled longitudinal and lateral control of the mobile agricultural robot "Agri-Eco-Robot" is addressed. As a first step, the Newton's law is used to develop the dynamical modeling of the mobile agricultural robot. The wheel-ground contact is modeled using the Terramechanics law called Extended-Bekker. The validation of the developed vehicle model was then conducted using an automotive simulator. The developed vehicle model is then used to derive the coupled control laws for the lateral and the longitudinal vehicle dynamics. The proposed controller is realized using two overlapping controllers, the first is dealing with coupled control of longitudinal and lateral dynamics to command the traction, and the second is the controller that minimize slipping, both are developed using the Lyapunov's theory. This controller is compared with the dynamics where the slip ratio is not controlled according to two scenarios in which one is the heigh velocity and the other is low velocity, this control law is validated using an automotive simulator applied to the mobile robot 'Agri-Eco-Robot'. The result of this control law shows the necessity of the slipping control when navigating in a rough environment such as agricultural fields, assuming a low-speed command to ensure system stability. [ABSTRACT FROM AUTHOR]

Consider a team of k ≤ n autonomous mobile robots initially placed at a node of an arbitrary graph G with n nodes. The dispersion problem asks for a distributed algorithm that allows the robots to reach a configuration in which each robot is at a distinct node of the graph. If the robots are anonymous, i.e., they do not have any unique identifiers, then the problem is not solvable by any deterministic algorithm. However, the problem can be solved even by anonymous robots if each robot is given access to a fair coin which they can use to generate random bits. In this setting, it is known that the robots require Ω (log Δ) bits of memory to achieve dispersion, where Δ is the maximum degree of G. On the other hand, the best known memory upper bound is m i n { Δ , m a x { log Δ , log D } } (D = diameter of G), which can be ω (log Δ) , depending on the values of Δ and D. In this paper, we close this gap by presenting an optimal algorithm requiring O (log Δ) bits of memory. [ABSTRACT FROM AUTHOR]

This paper proposes a self-learning sliding mode control (SlSMC) strategy with stability guarantee for the trajectory tracking of nonholonomic mobile robots (NMRs) under matched uncertainties, which improves the control performance of NMRs by optimizing the reaching law and the sliding mode surface of SMC as well as retaining the finite-time convergence and the robustness to uncertainties. In the presence of adverse factors such as skidding, slipping and environmental noise, the kinematic model of NMRs is reconstructed and an integral terminal sliding mode controller is designed for the trajectory tracking of NMRs. Then, based on the sliding mode controller, the proposed control strategy formulates the optimization of the SMC's reaching law and the sliding mode surface under stability constraints as two asynchronous optimal control problems with control constraints. Meanwhile, an online continuous-time receding-horizon optimization mechanism based on an actor-critic algorithm is proposed to solve the optimal problems asynchronously and improve online learning efficiency. The stability and the convergence of the proposed strategy are validated both in theory and simulations. Furthermore, extensive contrastive simulation results illustrate that the proposed receding horizon learning-based control strategy outperforms three recent methods in control performance. Finally, experiments of the proposed self-learning SMC strategy are carried out based on a real intelligent vehicle, and the experimental results also verify that the proposed method can meet the actual control needs of NMRs. • A self-learning sliding mode control strategy with stability guarantee is proposed for the trajectory tracking of NMRs, which improves the control performance while retaining the robustness and the finite-time convergence of sliding mode control (SMC). • An online continuous-time receding-horizon optimization mechanism based on an actor-critic algorithm is proposed to optimize the SMC's reaching law and sliding mode surface asynchronously under stability constraints. • Simulation results illustrate that the proposed strategy outperforms three recently developed approaches. Experimental studies are carried out based on an intelligent vehicle, and the experimental results verify that the proposed method can meet the actual control needs of NMRs. [ABSTRACT FROM AUTHOR]

This paper proposes a Q-learning based fault estimation (FE) and fault tolerant control (FTC) scheme under iterative learning control (ILC) framework. Due to the repetitive demands on control actuators for repetitive tasks, ILC is sensitive to actuator faults. Moreover, unknown faults varying with both time and trial axes pose a challenge to the control performance of ILC. This paper introduces Q-learning algorithm for FE to continuously adjust the estimator and adapt the changing faults. Then, FTC is designed by adopting the norm-optimal iterative learning control (NOILC) framework, where the controller is adjusted based on the FE results from Q-learning to counteract the influence of faults. Finally, the simulation on the plant of a mobile robot verifies the effectiveness of the proposed algorithm. • A new fault tolerant ILC scheme is proposed for systems with actuator faults. • Actuator faults varying with both time and trial axes are considered. • Q-learning algorithm is introduced to estimate the actuator faults. • The FE results are provided for controller reconfiguration. [ABSTRACT FROM AUTHOR]

Wheeled Mobile Robots (WMRs) are systems with multiple industrial and civilian applications. Trajectory tracking is essential in many applications, such as surveillance, monitoring, and autonomous driving. However, in practical applications, a WMR is always affected by kinematic disturbances, state estimation error, and measurement noise, which may diminish the system's performance. Hence, this work proposes a novel observer-based H ∞ controller that is robust against matched and unmatched disturbances. The proposed methodology compensates for disturbances through a disturbance observer, transforming the closed-loop system into a new one affected by uniformly bounded disturbances. Then, an H ∞ controller is designed to make the WMR track a desired reference signal. A formal stability proof demonstrates the feasibility of the new proposal. Also, feedback and finite-time controllers are used to assess the novel controller. Numerical simulations and experimental results with a scaled autonomous car-like robot demonstrate the novel controller's efficiency and outstanding performance, despite disturbances when compared against finite-time and feedback controllers. [Display omitted] • An observer-based H ∞ controller for trajectory tracking of wheeled mobile robots is developed. • The kinematic model is transformed into decoupled systems, simplifying the control design stage. • The disturbances affecting the system are compensated through an observer. • Robust performance is achieved despite matched and unmatched disturbances. • The controller is validated through numerical simulations and experimental results. [ABSTRACT FROM AUTHOR]

This article focuses on addressing three practical issues encountered when applying a data-driven model-free adaptive control (MFAC) approach to mobile robots. The first practical issue lies in a common assumption in MFAC schemes that the sign of all elements in pseudo-partial derivative (PPD) should be constant, while it cannot be satisfied if omnidirectional mobile manipulators (OMMs) move with platform rotation. To solve this problem, a new coordinate frame is introduced, which is crucial for applying MFAC to any mobile robots with rotation. The second one is that the initial value setting method for estimation of PPD is unclear. Improper settings may easily lead to control system instability. An initial value setting method for estimation of PPD is proposed with explicit physical interpretation. Lastly, applying the typical MFAC scheme directly to OMM fails to converge well to the desired trajectory. To tackle this, a new data-driven MFAC controller is proposed by incorporating a sliding mode control. Finally, experimental tests on an OMM are carried out to verify the effectiveness of the proposed control scheme. To the best of our knowledge, this is the first MFAC scheme that has been experimentally verified on a prototype mobile robot with rotation. • A new coordinate frame is introduced for applying MFAC to any mobile robots with rotational motion. • An initial value setting method for estimation of PPD is proposed with explicit physical interpretation. • A data-driven MFAC scheme is proposed for OMMs by incorporating a sliding mode control. • Experimental tests demonstrate effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper focuses on the tracking control problem for the two-wheeled mobile robot (TWMR) with unknown parameters. The robot collects its own states from the networked positioning system, which is subject to Denial-of-Service (DoS) attacks. To handle the uncertainties in the robot model and mitigate the attack effects on the system performance, parameter estimators with the projection operator technique are introduced. Then, an adaptive tracking controller is designed by adopting the backstepping technique. Correspondingly, a stability condition is derived, which guarantees that all the closed-loop signals are semi-globally uniformly bounded and tracking errors can converge to an adjustable compact set. The stability condition also reveals the relationships among the attack durations, design parameters and tracking errors, which can be utilized to guide the choice of design parameters. Experimental results are provided to validate the effectiveness of the proposed control scheme. • An adaptive tracking controller is proposed with uncertainties and DoS attacks. • Attack effects on the tracking performance can be mitigated. • A recursive method is utilized to analyze attack effects on the tracking performance. • The stability condition guarantees that tracking errors converge to a compact set. [ABSTRACT FROM AUTHOR]

The speed of solving and processing factors that are beneficial in reaching the desired target is one of the problematic aspects of controlling robots that has been neglected by the majority of researchers. Therefore, it is essential to look into the factors that influence calculation speed and goal achievement, and there should be some solutions to control robots in a lower time without sacrificing accuracy. The speed of processing and operations in wheeled mobile robots (WMRs), as well as the speed of a nonlinear model predictive control (NMPC), are examined in this article. The "Prediction horizon", which is the most efficient element in increasing the calculations of the NMPC, is determined separately and intelligently at every step based on the magnitude of the error and the significance of the state variable by training a multilayer neural network, to decrease the time-delay in software mode. In addition, the processing speed in the hardware mode has increased due to the investigations conducted and the optimal selection of equipment effective in the speed of performing actuators, such as the use of the U2D2 interface instead of interface boards with their own processing, and the use of the pixy2 as a smart camera. The results have employed that the proposed intelligence method responds 40 to 50% faster compared to the conventional method of NMPC. Also, the path tracking error has been reduced by using the proposed algorithm due to the optimal gain extraction at each step. In addition, there is a comparison of solving speed in hardware mode between the proposed and usual methods. In this regard, about 33% increase in solving speed has been shown. • Identification of the highly effective time-delay factors in the NMPC. • Reducing solving-time without accuracy sacrificing. • Intelligently regulating of prediction horizon gain to increase the solving-speed. • Decreasing the path tracking error due to the optimal gain extraction at each step. • The speed of processing and operations in wheeled mobile robots (WMRs). [ABSTRACT FROM AUTHOR]

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

• Small mobile robots are becoming increasingly popular in construction. • Small mobile robots raise novel occupational safety issues that need to be evaluated. • No previous research examined the impact of small mobile robots on workers' safety. • This conceptual paper identifies and categorizes various risk posed by small mobile robots. • Study outcomes can be used in creating guidelines for safe operations of small mobile robots. Introduction: Small mobile robots have become increasingly popular in the construction domain over the last few years. They are stable on rough terrains, can walk over small obstacles, climb stairs, and carry various sensors or arms to perform diverse functions and sub-tasks required to complete construction-related tasks. Saving time, improving accessibility to difficult or unsafe spaces, and reducing costs while accomplishing construction tasks are some of the benefits of using small, mobile robots in construction. However, serious concerns about new workplace hazards could arise from having mobile robots on the jobsite. Unfortunately, no study has attempted to evaluate these risks, especially in the construction domain. Therefore, there was a significant need to develop a holistic understanding of the direct and indirect risks of mobile robot applications in construction. Method: In this paper, we used inferential and Virtual Reality (VR) visualization techniques to: (1) construct conceptual visualizations of proximal and distant human-robot interaction within the construction context; and (2) identify potential safety challenges of robots, which were categorized into three groups: (a) physical risks, (b) attentional costs, and (c) psychological impacts. These identified safety challenges were then validated and ranked by a group of construction safety and robotic experts who had knowledge and experience using such robots in construction. Practical Applications: The outcomes of the study provided a detailed understanding of how robots might adversely affect workers' safety and health. The study outcomes could also be ultimately used in creating regulatory and administrative guidelines for the safe operations of small mobile robots in construction. [ABSTRACT FROM AUTHOR]

In this article, the fault-attack control problem is investigated for wheeled mobile robot (WMR) systems subjected to actuator faults, disturbances, communication attacks, and limited communication resources. An event-observer based compensation controller is presented. With the help of the observer estimation values and the attack sleep/active instant trigger information, the tracking control performance is realized for the robot system with the assistance of the neural network approximation technology. Concretely, first, the robot system dynamic model with actuator faults, disturbances, and attacks is established. Then, an event-based proportional–integral observer (PIO) is established. In the observer framework, a state estimator, an actuator fault efficiency estimator, and a disturbance estimator are embedded. Based on the observer outputs, a second-order adaptive sliding mode fault-compensation reliable controller is presented. In this controller framework, the fault compensation, disturbance attenuation, and the attack sleep/active time instant information are contained to guarantee the reliability and performance recoverability of the robot system. Furthermore, a dynamic even condition and an adaptive trigger scheme are constructed in the sensor and actuator channel to achieve the communication-efficient purpose. Finally, two cases of the robot system are performed to verify the system recoverability of the presented approach. • A class of wheeled mobile robot systems with actuator faults, disturbances, and communication attacks is established. • An event-based proportional–integral observer framework is designed for the robot system to estimate the states, actuator faults and lumped disturbances. • A novel observer-event-based adaptive fault-attack-tolerant control architecture is presented to simultaneously preserve and recover control performance for the robot system while saving the communication resources. [ABSTRACT FROM AUTHOR]

This paper investigates the teleoperation problem of networked disturbed mobile manipulators (NDMMs), in which the human operator remotely controls multiple slave mobile manipulators through a master manipulator. Each individual of the slave ones consisted of a nonholonomic mobile platform and a holonomic constrained manipulator that is mounted on the nonholonomic mobile platform. The cooperative control objective of the considered teleoperation problem includes: (1) synchronizing the states of the slave manipulators to the human-controlled master one; (2) forcing the mobile platforms of the slave ones to form a user-defined formation; (3) controlling the geometric center of all the platforms to track a reference trajectory. We present a hierarchical finite-time cooperative control (HFTCC) framework to achieve the cooperative control goal in a finite time. The presented framework includes the distributed estimator, the weight regulator and the adaptive local controller, where the estimator generates the estimated states of the desired formation and trajectory, the regulator selects the slave robot that the master one needs to track, as well as the presented adaptive local controller guarantees the finite-time convergence of the controlled states with model uncertainties and disturbances. Additionally, for improving the telepresence, a novel super twisting observer is presented to reconstruct the interaction force between the salve mobile manipulators and the remote operating environment on the master (i.e., the human) side. Finally, the effectiveness of the proposed control framework is demonstrated by several simulation results. • This paper solves the teleoperation problem of NDMMs in a distributed manner. • A novel HFTCC framework is proposed to achieve the finite-time cooperative control of the NDMMs in the teleoperation system. • A super twisting observer is designed for reconstructing the interaction force between the slave mobile manipulators and the task environment on the human operator. [ABSTRACT FROM AUTHOR]

• Proposes a unified multi-feature tightly coupled joint optimization framework UPLP-SLAM. • Establishes a uniform model representation and a consistent residual metric for point, line and plane features. • Proposes a mutual association method for both homogeneous and heterogeneous features. • Achieves an accurate, compact and robust multi-feature-fusion RGB-D SLAM system. Most of the existing RGB-D simultaneous localization and mapping (SLAM) systems are based on point features or point-line features or point-plane features. However, the existing multi- feature fusion SLAM methods based on the filter framework are not accurate and robust. And the fusion methods based on the optimization framework process different kinds of features separately and integrate them loosely. In the optimization-based framework, how to tightly fuse various kinds of features for achieving more accurate and robust pose estimation has been rarely considered. In this paper, we propose a unified point-line-plane fusion RGB-D visual SLAM method for navigation of mobile robots in structured environments, making full use of the information of three kinds of geometric features. Specifically, it extracts point, line, and plane features using images captured by the RGB-D camera and expresses them in a uniform way. Then, a mutual association scheme is designed for data association of point, line and plane features, which not only considers the correspondence of homogeneous features, i.e., point-point, line-line and plane-plane pairs, but also includes the association of heterogeneous features, i.e., point- line, point-plane and line-plane pairs. Afterwards, the matching errors between homogeneous features and the association errors between heterogeneous features are uniformly represented and jointly optimized to estimate the camera pose and feature parameters for accurate and consistent localization and map building. It is worth pointing out that the proposed unified framework contains two levels. From the system framework perspective, all the main components of the SLAM system, i.e., feature representation, feature association and error function are handled in a unified manner, which increases the accuracy and compactness of the multi-feature SLAM system. From the feature processing perspective, both homogeneous features and heterogeneous features are uniformly used, which provides more spatial constraints on pose estimation. Finally, the accuracy and robustness of the proposed method are verified by experiment comparisons with state-of-the-art visual SLAM systems on public datasets and in real-world environments [ABSTRACT FROM AUTHOR]

An Improved-RRT-based route planning technique with navigable trajectories was proposed. In this research work, the classical RRT technique is enhanced by optimizing the Non-linear equations and extending the mechanism of the technique. At first, the proposed I-RRT Expansion procedure is done and a task called N-action is performed that could identify the navigable trajectory in the configuration space. Then, an efficient extend-based-obstacles avoidance action has been carried out in the chosen navigable trajectory that was traced by the proposed I-RRT Expansion procedure. Finally, a subsequent-operation process was performed to carry out the node trimming process and smoothening of the final optimal path traced by the I-RRT Expansion method. Trajectories that have been generated using the proposed Improved Rapidly-Exploring Random Tree (I- RRT) method not only satisfied direction constraints approach on both start and goal points, but also simultaneously guaranteed the effectiveness and continuity of the trajectory. Consequently, the trajectories produced by the non-linear vehicle systems like Wheeled Mobile Robots (WMRs) are found to be geometrically viable and dynamically feasible. The optimal trajectories for five identical scenarios each of grid size of 100 × 100 and each scenario consisting of different number of nodes (500, 750, 1000, 1250 and 1500) were generated and the proposed method Improved Rapidly-Exploring Random Tree (I-RRT) was applied to all the scenarios and the performances were evaluated. The simulation results illustrate that the time traversed by the wheeled mobile robot and the path lengths traced by the WMR to reach the goal using the proposed I-RRT algorithm in each scenario are reduced considerably when compared to the corresponding time traversed by the wheeled mobile robot and the path lengths traced by the WMR to reach the goal using the existing RRT algorithm. [ABSTRACT FROM AUTHOR]

The automatic positioning of underground mobile applications plays a crucial role in enabling intelligent coal mining. However, due to the diverse kinematics and dynamics of these applications, various positioning methods have been proposed to match different targets. Nonetheless, the accuracy and applicability of these methods still fall short of meeting the requirements for field applications. Based on the vibration characteristics of underground mobile devices, a multi-sensor fusion positioning system is developed to enhance the accuracy of positioning in long and narrow global positioning system denied (GPS-denied) underground coal mine roadways. The system combines inertial navigation (INS), odometer, and ultra wide band (UWB) technologies through extended Kalman filter (EKF) and unscented Kalman filter (UKF). This approach enables accurate positioning by recognizing target carrier vibrations and facilitating fast conversion between multi-sensor fusion modes. The proposed system is tested on both a small unmanned mine vehicle (UMV) and a large roadheader, demonstrating that UKF enhances stability for roadheaders with strong nonlinear vibrations while EKF is more suitable for flexible UMVs. Detailed results confirm that the proposed system achieves an accuracy level of 0.15 m, meeting most coal mine application requirements. • An accurate and widely applicable positioning solution is presented. • The applicable positioning method is converted based on vibration recognition. • UKF performs better in large mining applications while EKF suits mobile robots. [ABSTRACT FROM AUTHOR]

Multi-robot cooperative object transport on uneven roads is challenging. The key barrier is dealing with nonholonomic and rigid-formation motion constraints. In this study, to alleviate the influence of these constraints on a multi-robot cooperative transport system (MRCTS), a six degree-of-freedom connector capable of sensing three-axial displacements, three-axial forces, and three-axial angular displacements is designed and employed. Based on the local displacements derived from each connector, we develop a position calibration method to calculate the relative position of each robot and achieve a centralized control strategy. Based on the forces sensed by each connector, we design a decentralized control strategy to accomplish cooperative transport in which a leader robot guides the follower robots toward a destination by applying forces, instead of centralized information broadcasting. The experimental results show that the MRCTS works well on an uneven surface, and the tracking errors are within the design stroke of the connectors, demonstrating the effectiveness of the design and control methods of the MRCTS. • A multi-robot cooperative transport system suitable for uneven roads is designed. • A mobile robot trajectory tracking controller appropriate for the measurement uncertainty is designed. • A self-calibration method is developed to calculate the initial relative poses among the robots and the payload. • A centralized cooperative strategy is achieved based on the self-calibration method and the trajectory tracking controller. • A decentralized cooperative strategy based on an admittance controller is proposed with the force noises median filter. [ABSTRACT FROM AUTHOR]

The role of target coordination problem in robot control is very important, but there is a problem of low collaborative control. The goal coordination problem cannot solve the problem of pairing teachers and classrooms in robot control, and it is reasonably Lower. Therefore, this paper proposes a complex algorithm to construct an optimization model for target collaboration problems. First, the management standard is used to divide the robot control data, and the robot control data is carried out according to the management requirements to realize robot control Preprocessing. Then, the management standards are divided according to the teaching standards, forming an optimized set of robot control and digging deeper into the Japanese time. MATLAB simulation shows that complex algorithms' optimization degree and stability are better than those of online control methods because the teaching standards are consistent. [ABSTRACT FROM AUTHOR]

In this paper, the control problem of aerial continuum manipulation systems (ACMSs) is considered. This study introduces a new platform for ACMSs in which a quadrotor is equipped with a tendon-bent concentric tube continuum robot. The decoupled dynamic modeling of ACMSs is utilized to derive the proposed feedback control law. The control design process is split into two distinct control loops. In the first loop, a terminal synergetic controller is presented to control the continuum robot's tip to track the desired trajectory. In the second loop, an improved synergetic controller is introduced to control the position and velocity of the quadrotor, which ensures asymptotic stability of the origin of the error dynamic. Moreover, in this loop a hybrid feedback law is designed to operate with the improved synergetic controller, which overcomes topological obstructions and globally stabilizes the attitude of the quadrotor. Furthermore, the stability of the resultant closed-loop system is verified utilizing the Lyapunov stability theorem. The efficiency and advantages of the proposed controller are verified by a comparative analysis in simulations. • Introducing dynamics of Aerial Continuum Manipulator with concentric tubes. • Designing a primary synergetic controller for manipulator's trajectory tracking. • Developing a secondary synergetic controller for quadrotor's position and velocity. • Designing a hybrid feedback law to overcome the topological obstructions. [ABSTRACT FROM AUTHOR]

In this paper, a formation controller for leader–follower mobile robots is presented based on reinforcement learning and the Fourier series expansion. The controller is designed based on the dynamical model in which permanent magnet direct-current (DC) motors are included as actuator. Thus, motor voltages are the control signals and are designed based on the actor–critic strategy which is a well-known approach in the field of reinforcement learning. Stability analysis of formation control of leader–follower mobile robots using the proposed controller verifies that the closed-loop system is globally asymptotically stable. Due to the existence of sinusoidal terms in the model of mobile robots, the Fourier series expansion has been selected to construct the actor and critic, while previous related works utilized neural networks in actor and critic. In comparison with neural networks, the Fourier series expansion is simpler and involves the designer in fewer tuning parameters. In simulation studies, it has been assumed that some follower robots can play the role of leader for the other follower robots behind it. Simulation results show that there is no need to use large number of the sinusoidal terms in the Fourier series expansion and just the first three terms can overcome uncertainties. In addition, the proposed controller reduced the performance index of tracking errors considerably in comparison with radial basis function neural networks (RBFNN). • Leader–follower formation control considering actuators has been presented. • The controller is combination of 2 model-free approach. • The proposed controller does not need motor current feedback. • In comparison with RBF neural network, the Fourier series expansion is superior in this system. [ABSTRACT FROM AUTHOR]

The autonomous robot has been the attraction point among robotic researchers since the last decade by virtue of increasing demand of automation in defence and intelligent industries. In the current research, a modified flow direction optimization algorithm (MFDA) and firefly algorithm (FA) are hybridized and implemented on wheeled robots to encounter multi-target trajectory optimization with smooth navigation by negotiating obstacles present within the workspace. Here, a hybrid algorithm is adopted for designing the controller with consideration of navigational parameters. A Petri-Net controller is also aided with the developed controller to resolve any conflict during navigation. The developed controller has been investigated on WEBOTS and MATLAB simulation environments coupled with real-time experiments by considering Khepera-II robot as wheeled robot. Single robot- multi-target, multiple robot single target and multiple robots-multiple target problems are tackled during the investigation. The outcomes of simulation are verified through real-time experimental outcomes by comparing results. Further, the proposed algorithm is tested for its suitability, precision, and stability. Finally, the developed controller is tested against existing techniques for authentication of proposed technique, and significant improvements of an average 34.2% is observed in trajectory optimization and 70.6% in time consumption. • Design of novel hybrid controller for multiple target trajectory optimization and control of multiple robotics system. • Execution of proposed hybrid model in WEBOTS and MATLAB simulation platform. • Real-time experiments in laboratory with single and multiple robots for multiple targets. • Aided inter-collision avoidance controller. • Comparison between proposed and existing technique for effectiveness measurement. [ABSTRACT FROM AUTHOR]

In the context of motion planning in robotics, the problem of path planning based on artificial potential fields has been examined using different algorithms to avoid trapping in local minima. With this objective, this paper proposes a novel method based on a deterministic annealing strategy to improve the potential field function by introducing a temperature parameter to increase the robot's obstacle avoidance efficiency. The annealing and tempering strategies prevent the robot from being trapped at the local minima and allow it to continue towards its destination. The initial path is optimised using an annealing algorithm to enhance the overall performance. The time, length and success rate of the planned path measures the quality of the solution. Simulation results and comparative experiments demonstrate that the proposed algorithm can solve path planning in different environments. The proposed algorithm is suitable for complex environments with convex or non-convex polygon obstacles. • Proposing a new algorithm based on Deterministic Annealing for local and global path planning. • Preventing the robot from being trapped at the local minima. • Providing comparative experiments under complex environments with convex or non-convex polygon obstacles. [ABSTRACT FROM AUTHOR]