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This study explores a novel multi-tote storage and retrieval autonomous mobile robot system, where multi-tote autonomous mobile robots transport totes (or 'SKU bins') for order picking. We investigate the joint optimisation problem of order sequencing, tote scheduling, and robot routing in a single workstation equipped with the capacity to accommodate multiple order bins and parallel totes. The inbound and outbound streams of SKUs stored in totes are crucial for order picking at the workstation, as they jointly affect the picking performance efficiency through synchronization with the order sequence. To address this synchronization problem, we formulate a Mixed-Integer Linear Programming (MILP) model and propose a two-stage hybrid heuristic combining a variable neighbourhood search (VNS) algorithm and a refinement model. This model further improves the VNS solution with partially fixed SKU inbound stream and problem-tailored inequalities. Our numerical studies highlight the superior performance of the proposed hybrid heuristic, where the VNS solution surpasses the benchmark VNS-Simplified by a significant margin of 14%. The rearrangement process enhances the VNS performance by reducing 33.8% of SKU revisits. We also offer managerial insights, suggesting that the maximum number of order bins and parallel totes, exhibit boundary points where adding capacity yields limited marginal improvements. [ABSTRACT FROM AUTHOR]

Last-mile delivery is a costly and time-consuming process in e-commerce operations. Piloted by several companies, autonomous mobile robots cooperating with trucks have been implemented for parcel delivery. In this system, a truck departs from the warehouse, carrying robots and parcels, and traverses several drop-off points to release robots for parcel delivery or to deliver parcels by itself. We focus on performance estimation and system configuration, considering the no-zoning policy, where all trucks serve the whole delivery area and the zoning policy, where each truck takes charge of one zone. A queuing network is constructed to estimate the parcel throughput time and a cost minimisation model is developed for the system with a required throughput time. The optimal number of drop-off points and batch size can be numerically found to minimise the order throughput time. The zoning policy is cheaper than no-zoning policy for a small number of drop-off points and a short required throughput time, and it is cheaper in a system with a large number of drop-off points. Moreover, we find that while the truck-based autonomous mobile robot delivery system typically has a larger order throughput time than a delivery system using only robots, it is much less costly. [ABSTRACT FROM AUTHOR]

Current studies on scheduling of machines and transporters assume that either a single transporter or an infinite number of homogeneous transporters such as AGVs or mobile robots are available to transport semi-finished jobs, which seems very restrictive in practice. This paper addresses this gap by studying a job-shop scheduling problem that incorporates a limited number of heterogeneous transporters, where the objective is to minimize the makespan. The problem is modelled using mixed-integer linear programming and constraint programming. Different structure-based branch-and-bound algorithms with two lower-bounding strategies are also developed. A comprehensive numerical study evaluates the proposed models and algorithms. The research demonstrates that the adjustment of the proposed MILP model outperforms the existing formulation when applied to the homogeneous case. The study also uncovers interesting practical implications, including the analysis of the impact of different transporter types in the system. It shows that utilizing a fleet of heterogeneous transporters can improve the overall performance of the job shop compared to a relevant homogeneous case. The importance of the study is emphasized by highlighting the negative consequences of disregarding transporters' differences during the scheduling phase. [ABSTRACT FROM AUTHOR]

In this article, we develop a novel adaptive extremum‐seeking control (AdESC) algorithm with robustness guarantees and without persistence of excitation (PE). Specifically, this builds on a proportional‐integral (PI)‐like parameter estimator. A zeroth‐order optimization framework is used, where the optimizer/agent can only query the numerical value of the cost function at the current coordinate given an unmodeled bounded disturbance. Since parameter estimation plays a decisive role in the stability and convergence properties of AdESC algorithm, it is also well established in the existing literature that to ensure parameter convergence a stringent PE condition is required. Here, we eliminate the need for a stringent PE condition by utilizing a novel set of weighted integral filter dynamics, while ensuring sufficient richness using a milder condition, called initial excitation (IE). Moreover, to validate the robustness guarantees towards unmodeled bounded disturbance, a detailed Lyapunov function based analysis is performed to establish the closed‐loop stability and convergence in the form of uniform ultimate boundedness (UUB). Furthermore, an experimental study using a unicycle wheeled mobile robot (WMR) is carried out as a proof‐of‐concept considering disturbance and disturbance‐free scenarios. [ABSTRACT FROM AUTHOR]

This article presents an innovative concept that harnesses the capabilities of large language models (LLMs) to revolutionize human‐robot interaction. This work aims to connect large language models with the Robot Operating System (ROS), the primary development framework for robotics applications. We develop a package for ROS that seamlessly integrates ChatGPT with ROS2‐based robotic systems. The core idea is to leverage prompt engineering with LLMs, utilizing unique properties such as ability eliciting, chain‐of‐thought, and instruction tuning. The concept employs ontology development to convert unstructured natural language commands into structured robotic instructions specific to the application context through prompt engineering. We capitalize on LLMs' zero‐shots and few‐shots learning capabilities by eliciting structured robotic commands from unstructured human language inputs. To demonstrate the feasibility of this concept, we implemented a proof‐of‐concept that integrates ChatGPT with ROS2, showcasing the transformation of human language instructions into spatial navigation commands for a ROS2‐enabled robot. Besides, we quantitatively evaluated this transformation over three use cases (ground robot, unmanned aerial vehicle, and Robotic arm) and five LLMs (LLaMA‐7b, LLaMA2‐7b, LLaMA2‐70b, GPT‐3.5, and GPT‐4) on a set of 3000 natural language commands. Our system serves as a new stride towards Artificial General Intelligence (AGI) and paves the way for the robotics and natural language processing communities to collaborate in creating novel, intuitive human‐robot interactions. The open‐source implementation of our system on ROS 2 is available on GitHub. [ABSTRACT FROM AUTHOR]

Autonomous navigation is one of the key challenges in robotics. In recent years, several research studies have tried to improve the quality of this task by adopting artificial intelligence approaches. Indeed, the neuro-fuzzy approach stands out as one of the most commonly employed methods for developing autonomous navigation systems. Nevertheless, it may encounter problems of accuracy, complexity, and interpretability due to redundancy in the fuzzy rule base, particularly in the fuzzy sets associated with the system's variables. In this work, a strategy is proposed to optimize an adaptive-network-based fuzzy inference system (ANFIS) controller for reactive navigation by addressing the problem of complexity and accuracy. It consists in combining a suite of methods, namely, data-driven fuzzy modeling, fuzzy sets merging, fuzzy rule base simplification, and parameter training. This process has produced a fuzzy inference system-based controller with high accuracy and low complexity, enabling smooth and near-optimal navigation. This system receives local information from sensors and predicts the appropriate kinematic behavior that enables the robot to avoid obstacles and reach the target in cluttered and previously unknown environments. The performance of the proposed controller and the efficiency of the followed strategy are demonstrated by simulation experiments and comparisons with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) LiDAR is crucial for the autonomous navigation of orchard mobile robots, offering comprehensive and accurate environmental perception. However, the increased richness of information provided by 3D LiDAR also leads to a higher computational burden for point cloud data processing, posing challenges to real-time navigation. To address these issues, this paper proposes a 3D point cloud optimization method based on the octree data structure for autonomous navigation of orchard mobile robots. This approach includes two key components: 1) In terms of orchard mapping, the spatial indexing and segmentation features of the octree data structure are introduced. According to the sparsity and density of the point cloud, the 3D orchard map is adaptively divided and the key information of the orchard is retained. 2) In terms of path planning, by using octree nodes as the unit nodes for RRT* random tree expansion, an improved RRT* algorithm based on octree is proposed. Field experiments were conducted in a pear orchard based on this method. The experimental results show that: 1) The overall number of point cloud data points in the map was reduced by approximately 76.32%, while important features, including tree morphology, trellis structure, and road surface information, were fully preserved. 2) When different octree node resolutions were applied, the improved RRT* algorithm demonstrated significant improvements in path generation time, sampling point utilization, path length, and curvature. The lateral tracking error increased as the resolution of octree nodes decreased. At a resolution of 0.20 m, the maximum average lateral tracking error was 0.079 m, indicating strong path trackability. This method exhibits tremendous potential for processing large-scale 3D point cloud data and enhancing path planning efficiency, providing a valuable technical reference for the real-time autonomous navigation of mobile robots in complex orchard environments. [ABSTRACT FROM AUTHOR]

Introduction: Path planning in complex and dynamic environments poses a significant challenge in the field of mobile robotics. Traditional path planning methods such as genetic algorithms, Dijkstra's algorithm, and Floyd's algorithm typically rely on deterministic search strategies, which can lead to local optima and lack global search capabilities in dynamic settings. These methods have high computational costs and are not efficient for real-time applications. Methods: To address these issues, this paper presents a Quantum-behaved Particle Swarm Optimization model enhanced by deep reinforcement learning (RL-QPSO Net) aimed at improving global optimality and adaptability in path planning. The RL-QPSO Net combines quantum-inspired particle swarm optimization (QPSO) and deep reinforcement learning (DRL) modules through a dual control mechanism to achieve path optimization and environmental adaptation. The QPSO module is responsible for global path optimization, using quantum mechanics to avoid local optima, while the DRL module adjusts strategies in real-time based on environmental feedback, thus enhancing decision-making capabilities in complex high-dimensional scenarios. Results and discussion: Experiments were conducted on multiple datasets, including Cityscapes, NYU Depth V2, Mapillary Vistas, and ApolloScape, and the results showed that RL-QPSO Net outperforms traditional methods in terms of accuracy, computational efficiency, and model complexity. This method demonstrated significant improvements in accuracy and computational efficiency, providing an effective path planning solution for real-time applications in complex environments for mobile robots. In the future, this method could be further extended to resource-limited environments to achieve broader practical applications. [ABSTRACT FROM AUTHOR]

Laboratory automation requires reliable and precise handling of microplates, but existing robotic systems often struggle to achieve this, particularly when navigating around the dynamic and variable nature of laboratory environments. This work introduces a novel method integrating simultaneous localization and mapping (SLAM), computer vision, and tactile feedback for the precise and autonomous placement of microplates. Implemented on a bi-manual mobile robot, the method achieves fine-positioning accuracies of ± 1.2 mm and ± 0.4°. The approach was validated through experiments using both mockup and real laboratory instruments, demonstrating at least a 95% success rate across varied conditions and robust performance in a multi-stage protocol. Compared to existing methods, our framework effectively generalizes to different instruments without compromising efficiency. These findings highlight the potential for enhanced robotic manipulation in laboratory automation, paving the way for more reliable and reproducible experimental workflows. [ABSTRACT FROM AUTHOR]

Controlling a constrained dynamic system in an environment with multiple obstacles is important yet challenging. Many existing methods are either heuristic (e.g. A* algorithm) or model-based (e.g. optimal control). In contrast to these methods, this paper addresses scenarios where the mathematical model of the dynamic system is unknown, relying solely on input–output data and environmental information. We propose a new data-driven framework to achieve safe path planning and efficient tracking control by integrating sample-based methods with more recent data-enabled predictive control. In the offline phase, we develop a safe path planning algorithm to generate a sequence of convex safe sets from the initial point to the target set. This is achieved by leveraging a sample-based planning algorithm and solving bi-linear optimization problems. The resulting adjacent safe sets have a nonempty intersection, and the distance between each safe set and any obstacle exceeds the required safe distance. In the online phase, we develop an efficient data-enabled predictive tracking control algorithm with the core of safe set contraction constraints to sequentially track the safe sets. The proposed algorithm transforms the nonconvex obstacle avoidance control problem into a convex optimization problem, which can be solved efficiently. We demonstrate that the proposed framework is safe, efficient, and scalable through quadcopter simulations, comparison simulations, and unmanned vehicle experiments. [ABSTRACT FROM AUTHOR]

The increasing demand for autonomous mobile robots in complex environments calls for efficient path-planning algorithms. Bio-inspired algorithms effectively address intricate optimization challenges, but their computational cost increases with the number of particles, which is great when implementing algorithms of high accuracy. To address such topics, this paper explores the application of the leader-based bat algorithm (LBBA), an enhancement of the traditional bat algorithm (BA). By dynamically incorporating robot orientation as a guiding factor in swarm distribution, LBBA improves mobile robot localization. A digital compass provides precise orientation feedback, promoting better particle distribution, thus reducing computational overhead. Experiments were conducted using a mobile robot in controlled environments containing obstacles distributed in diverse configurations. Comparative studies with leading algorithms, such as Manta Ray Foraging Optimization (MRFO) and Black Widow Optimization (BWO), highlighted the proposed algorithm's ability to achieve greater path accuracy and faster convergence, even when using fewer particles. The algorithm consistently demonstrated robustness in bypassing local minima, a notable limitation of conventional bio-inspired approaches. Therefore, the proposed algorithm is a promising solution for real-time localization in resource-constrained environments, enhancing the accuracy and efficiency in the guidance of mobile robots, thus highlighting its potential for broader adoption in mobile robotics. [ABSTRACT FROM AUTHOR]

Recent transportation systems are operated by cooperative factors including mobile robots, smart vehicles, and intelligent management. It is highly anticipated that the surveillance using mobile robots can be utilized in complex transportation areas where the high accuracy is required. In this paper, we introduce a crowd surveillance system using mobile robots and intelligent vehicles to provide obstacle avoidance in transportation stations with a consideration of different moving strategies of the robots in an existing 2D area supported by line-based barriers and surveillance formations. Then, we formally define a problem that aims to minimize the distance traveled by a mobile robot, while also considering the speed of the mobile robot and avoiding the risk of collisions when the mobile robot moves to specific locations to fulfill crowd surveillance. To solve this problem, we propose two different schemes to provide improved surveillance that can be used even when considering speed. After that, various ideas are gathered to define conditions, set various settings, and modify them to evaluate their performances. [ABSTRACT FROM AUTHOR]

To improve the efficiency of mobile robot movement, this paper investigates the fusion of the A* algorithm with the Dynamic Window Approach (DWA) algorithm (IA-DWA) to quickly search for globally optimal collision-free paths and avoid unknown obstacles in time. First, the data from the odometer and the inertial measurement unit (IMU) are fused using the extended Kalman filter (EKF) to reduce the error caused by wheel slippage on the mobile robot’s positioning and improve the mobile robot’s positioning accuracy. Second, the prediction function, weight coefficients, search neighborhood, and path smoothing processing of the A* algorithm are optimally designed to incorporate the critical point information in the global path into the DWA calculation framework. Then, the length of time and convergence speed of path planning are compared and simulated in raster maps of different complexity. In terms of path planning time, the algorithm reduces by 23.3% compared to A*-DWA; in terms of path length, the algorithm reduces by 1.8% compared to A*-DWA, and the optimization iterations converge faster. Finally, the reliability of the improved algorithm is verified by conducting autonomous navigation experiments using a ROS (Robot Operating System) mobile robot as an experimental platform. [ABSTRACT FROM AUTHOR]

IMUs (inertial measurement units) and cameras are widely utilized and combined to autonomously measure the motion states of mobile robots. This paper presents a loosely coupled algorithm for autonomous localization, the ICEKF (IMU-aided camera extended Kalman filter), for the weighted data fusion of the IMU and visual measurement. The algorithm fuses motion information on the velocity layer, thereby mitigating the excessive accumulation of IMU errors caused by direct subtraction on the positional layer after quadratic integration. Furthermore, by incorporating a weighting mechanism, the algorithm allows for a flexible adjustment of the emphasis placed on IMU data versus visual information, which augments the robustness and adaptability of autonomous motion estimation for robots. The simulation and dataset experiments demonstrate that the ICEKF can provide reliable estimates for robot motion trajectories. [ABSTRACT FROM AUTHOR]

A detailed theoretical design of an electric resonance-based coupler for dynamic wireless power transfer (DWPT) at the mobile robot level is presented. The scattering matrix of the coupler was derived by transforming and multiplying transmission matrices for each circuit network in a practical equivalent circuit that accounted for loss resistance. This theoretical approach was validated through equivalent circuit models, yielding results consistent with 3D full-wave simulations and showing an error rate of less than 1%. Additionally, a null-power point characteristic, where efficiency sharply decreases when the receiver moves outside the transmitter's range, was observed. The detailed theoretical design of the practical equivalent circuit for electric resonance-based dynamic WPT couplers is expected to contribute to the design of couplers for various specifications in future applications. [ABSTRACT FROM AUTHOR]

Inertially driven or vibration-driven capsule robots have recently attracted attention from various areas of science and engineering. The main advantage of such a mobile system is the lack of any external driving mechanism, which allows for an encapsulated construction and efficient operation in demanding environments. This study focuses on the conception of a mechanical system whose motion on a rough horizontal plane is caused only by the rotation of two internal masses. The mathematical model of the robot is presented in the non-dimensional form. The conditions for motion are formulated, and the working region of the system is specified. The active phase of the robot dynamics may include both forward motion and minor backward motion, or forward motion exclusively. Extensive numerical studies are conducted using two friction models. The numerical solutions to the dynamic problem are analyzed with regard to the total displacement and the average velocity per period of excitation. The interplay between model parameters is studied. The resulting maps on the parameter planes allow for substantial improvement in the robot performance. Moreover, it is demonstrated that the Stribeck effect occurring in the friction characteristics plays a positive role in the efficient motion of the system. [ABSTRACT FROM AUTHOR]

The objective of this paper was to investigate and design a novel vertical- and horizontal-arm independent suspension system aimed at enhancing the autonomous obstacle-crossing capabilities of unmanned ground platforms in complex, unstructured environments such as mountainous regions, hills, and mining areas. By thoroughly considering factors such as the suspension structure design, changes in the centroid position, distribution of driving forces, and dynamic stability analysis, we proposed an innovative suspension structure. An unmanned ground platform model equipped with this suspension system was developed using ADAMS and MATLAB/Simulink. Subsequently, a joint simulation was conducted to validate the performance of the suspension system. The results indicated that the unmanned ground platform could successfully traverse vertical steps up to 370 mm high and trenches measuring up to 600 mm wide. Furthermore, when confronted with intricate obstacles including vertical barriers, trenches, and side slopes, the platform demonstrated exceptional traversing capabilities. In conclusion, the proposed suspension system significantly enhances both the obstacle-surmounting ability and the terrain adaptability of unmanned ground platforms while providing crucial technical support for their deployment in complex unstructured environments. [ABSTRACT FROM AUTHOR]

This work aims to develop and compare the performance of a line-following robot using both neural networks and classical controllers such as Proportional–Integral–Derivative (PID). Initially, the robot's infrared sensors were employed to follow a line using a PID controller. The data from this method were then used to train a Long Short-Term Memory (LSTM) network, which successfully replicated the behavior of the PID controller. In a subsequent experiment, the robot's camera was used for line-following with neural networks. Images of the track were captured, categorized, and used to train a convolutional neural network (CNN), which then controlled the robot in real time. The results showed that neural networks are effective but require more processing and calibration. On the other hand, PID controllers proved to be simpler and more efficient for the tested tracks. Although neural networks are very promising for advanced applications, they are also capable of handling simpler tasks effectively. [ABSTRACT FROM AUTHOR]

This article focuses on the region-reaching control for a non-holonomic mobile robot system with two actuated wheels. The region-reaching control task consists of three basic control objectives: reaching an objective region, maintaining a rest state in the objective region, and stabilising at a desired posture. The constraints imposed on the motion of the mobile robot system are not integrable and have no inverse resolution, and thus increase the complexity of the controller design for non-holonomic systems. A system decomposition approach is proposed to convert the non-holonomic constraint control system to the cascade holonomic constraint control subsystems, and an adaptive control gain technique is presented to guarantee the stability of the non-holonomic system. Then, a novel torque controller is presented to implement the region-reaching control task. Specially, the objective region can be considered as a parking spot for a mobile robot. [ABSTRACT FROM AUTHOR]

The path complexity generated by the Voronoi diagram in the existing algorithms is usually high, which leads to low efficiency in mobile robot navigation. This work proposes a Voronoi diagram optimization method based on the ray model. By re-connecting the key nodes in the map skeleton using the ray model principle, a more complete and concise Voronoi diagram is generated, and navigation paths that better conform to the principle of mobile robot motion are found. The path optimization effect of the algorithm is verified by simulation and real experiments, reducing the speed loss of the robot motion and improving navigation efficiency. This optimization method helps to reduce time cost and energy consumption, enabling mobile robots to integrate more efficiently and economically into people's daily life. [ABSTRACT FROM AUTHOR]

In view of the problem that it is difficult to reasonably plan the path after the mobile robot completes its self positioning and map construction, which leads to the disordered movement of the mobile robot and the waste of resources, ant colony algorithm is adopted to realize the path planning of mobile robot in this study. Ant colony algorithm is a probabilistic algorithm to solve the optimal path in a problem. However, in the general ant colony algorithm, all parameters of the ant colony algorithm are unchanged, resulting in the result of the ant colony algorithm de- pendent on the pheromone parameters set in the algorithm. In order to solve the above problems, the parameters of ant colony algorithm and pheromone allocation are improved, and the pheromone update standard is improved by changing the pheromone volatility coefficient and pheromone update standard in each iteration and combining with heuristic factors. Setting the adjustable pheromone volatile factor increases the adaptability of the algorithm. According to the meaningful parameter space, the path planning results of the traditional ant colony algorithm and the improved ant colony algorithm are compared under different environments. The path length of the improved ant colony algorithm is reduced by 4.48% and 8.54%, respectively, and no path crossover nodes are generated, which achieves the expected effect of reasonable path planning for mobile robots. [ABSTRACT FROM AUTHOR]

Addressing the problems of the RIME in the mobile robot path planning problem, such as easy to fall into the local optimum and slow convergence speed, this paper proposed an enhanced rime optimization algorithm (ERIME) for the path planning of mobile robots in the complex environment. Firstly, this algorithm designed a lens imaging population selection strategy based on sine chaos mapping to improve the population initialization stage to increase the population diversity, so that the algorithm could be better explored and exploited. Secondly, this algorithm designed a stochastic factor-controlled optimal search strategy and a centroid-guided development mechanism to improve the exploration and exploitation stages of the algorithm, so as to enhance the algorithm's ability to escape from the local optimal solutions, better explore the global optimal solution, and accelerate the convergence speed of the algorithm. Additionally, this paper proposed a Markov chain model of the ERIME algorithm and proved the global convergence of the algorithm. To verify the effectiveness of ERIME, this paper validated the algorithm using the CEC2017 test set and compared it with other well-known meta-heuristic algorithms. The results show that the algorithm performs well. Finally, this paper applied the ERIME algorithm to the path planning problem of mobile robots in complex environments. The experimental results demonstrate that the proposed algorithm efficiently plans the robot's path and finds a high-quality route. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the event-based tracking control for two-wheeled mobile robots using a sliding mode control strategy. To address the conflict between the singularity problem and finite-time performance, a new nonsingular terminal sliding mode controller enabling mobile robots to achieve the tracking goal through a wireless network is developed. Further, redesign the controller using sampling information, in which an event condition is introduced to determine the sampling sequence, and the event-triggered controller avoids the high gain situation through the proposed sliding variables. The Zeno phenomenon for event condition is excluded by proofing the existence of minimal positive interevent execution time. Finally, an experiment has been implemented on a remote computer transmitting control signals to a mobile robot, demonstrating the effectiveness and applicability of the designed controller. • This paper considers the event-based tracking control of two-wheeled mobile robots. • A novel terminal sliding surface is presented to get the finite-time stability. • The controller designed by sampling information can avoid singularity problem. • An experiment is performed to verify the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

This paper presents a map-free navigation approach for industrial automatic mobile robots (AMRs), designed to ensure computational efficiency, cost-effectiveness, and adaptability. Utilizing deep reinforcement learning (DRL), the system enables real-time decision-making without fixed markers or frequent map updates. The central contribution is the Heuristic Dense Reward Shaping (HDRS), inspired by potential field methods, which integrates domain knowledge to improve learning efficiency and minimize suboptimal actions. To address the simulation-to-reality gap, data augmentation with controlled sensor noise is applied during training, ensuring robustness and generalization for real-world deployment without fine-tuning. Training results underscore HDRS's superior convergence speed, training stability, and policy learning efficiency compared to baselines. Simulation and real-world evaluations establish HDRS-DRL as a competitive alternative, outperforming traditional approaches, and offering practical applicability in industrial settings. • Map-free, DRL-based navigation architecture ensures real-time operational efficiency. • Heuristic dense reward shaping improves learning and navigation performance. • Data augmentation with noise strengthens model robustness for direct AMR deployment. [ABSTRACT FROM AUTHOR]

This manuscript proposes a method for analyzing the stability of the behavior of a cyber-physical system (CPS) under conditions of potential destructive impact, considering the tasks it performs, which does not require labeled sets of abnormal data. The considered CPS has an autonomous decision-making system. The method was formalized in terms of the Markov decision-making process. Proposed metrics for assessing CPS behavior based on changes in its parameters were defined. They allowed classifying the operating mode into three classes: normal, abnormal, and uncertain. Evaluation results prove the efficiency of the proposed method. Despite the proposed method being tested on an unmanned vehicle (UV), it can also be applied to other CPSs, primarily to autonomous mobile robots (AMRs). [ABSTRACT FROM AUTHOR]

TEB (timed elastic band) can efficiently generate optimal trajectories that match the motion characteristics of car-like robots. However, the quality of the generated trajectories is often unstable, and they sometimes violate boundary conditions. Therefore, this paper proposes a fuzzy logic control–TEB algorithm (FLC-TEB). This method adds smoothness and jerk objectives to make the trajectory generated by TEB smoother and the control more stable. Building on this, a fuzzy controller is proposed based on the kinematic constraints of car-like robots. It uses the narrowness and turning complexity of the trajectory as inputs to dynamically adjust the weights of TEB's internal objectives to obtain stable and high-quality trajectories in different environments. The results of real car-like robot tests show that compared to the classical TEB, FLC-TEB increased the trajectory time by 16% but reduced the trajectory length by 16%. The trajectory smoothness was significantly improved, the change in the turning angle on the trajectory was reduced by 39%, the smoothness of the linear velocity increased by 71%, and the smoothness of the angular velocity increased by 38%, with no reverse movement occurring. This indicates that when planning trajectories for car-like mobile robots, while FLC-TEB slightly increases the total trajectory time, it provides more stable, smoother, and shorter trajectories compared to the classical TEB. [ABSTRACT FROM AUTHOR]

Accurate localisation is key for the autonomy of mobile robots. Fiducial localisation utilises relative positions of markers physically deployed across an environment to determine a localisation estimate for a robot. Fiducial markers are strictly designed, with very limited flexibility in appearance. This often results in a "trade-off" between visual customisation, library size, and occlusion resilience. Many fiducial localisation approaches vary in their position estimation over time, leading to instability. The Stable Fiducial Marker System (STag) was designed to address this limitation with the use of a two-stage homography detection. Through its combined square and circle detection phases, it can refine detection stability. In this work, we explore the utility of STag as a basis for a stable mobile robot localisation system. Key marker restrictions are addressed in this work through contributions of three new chromatic STag marker types. The hue/greyscale STag marker set addresses constraints in customisability, the high-capacity STag marker set addresses limitations in library size, and the high-occlusion STag marker set improves resilience to occlusions. These are designed with compatibility with the STag detection system, requiring only preprocessing steps for enhanced detection. They are assessed against the existing STag markers and each shows clear improvements. Further, we explore the viability of various materials for marker fabrication, for use in outdoor and low-light conditions. This includes the exploration of "active" materials which induce effects such as retro-reflectance and photo-luminescence. Detection rates are experimentally assessed across lighting conditions, with "active" markers assessed on the practicality of their effects. To encapsulate this work, we have developed a full end-to-end deployment for fiducial localisation under the STag system. It is shown to function for both on-board and off-board localisation, with deployment in practical robot trials. As a part of this contribution, the associated software for marker set generation/detection, physical marker fabrication, and end-to-end localisation has been released as an open source distribution. [ABSTRACT FROM AUTHOR]

This paper deals with the optimization of the operational efficiency of a fleet of mobile robots, assigned with delivery-like missions in complex outdoor scenarios. The robots, due to limited onboard computation resources, need to offload some complex computing tasks to an edge/cloud server, requiring artificial intelligence and high computation loads. The mobile robots also need reliable and efficient radio communication with the network hosting edge/cloud servers. The resource assignment aims at minimizing the total latency and delay caused by the use of radio links and computation nodes. This minimization is a nonlinear integer programming problem, with high complexity. In this paper, we present reduced-complexity algorithms that allow to jointly optimize the available radio and computation resources. The original problem is reformulated and simplified, so that it can be solved by also selfish and greedy algorithms. For comparison purposes, a genetic algorithm (GA) is used as the baseline for the proposed optimization techniques. Simulation results in several scenarios show that the proposed sequential minimization (SM) algorithm achieves an almost optimal solution with significantly reduced complexity with respect to GA. [ABSTRACT FROM AUTHOR]

Compared to four-wheel independent drive and steering (4WID4WIS) mobile robots, three-wheel independent drive and steering (3WID3WIS) mobile robots are more cost-effective due to their lower cost, lighter weight, and better handling performance, even though their acceleration performance is reduced. This paper proposes a dynamic model predictive control (DMPC) controller for trajectory tracking of 3WID3WIS mobile robots to simplify the computational complexity and improve the accuracy of traditional model predictive control (MPC). The A* algorithm with a non-point mass model is used for path planning, enabling the robot to navigate quickly in narrow and constrained environments. Firstly, the kinematic model of the 3WID3WIS mobile robot is established. Then, based on this model, a DMPC trajectory tracking controller with dynamic effects is developed. By replacing MPC with DMPC, the computational complexity of MPC is reduced. During each control period, the prediction horizon is dynamically adjusted based on changes in trajectory curvature, establishing a functional relationship between trajectory curvature and prediction horizon. Subsequently, a comparative study between the proposed controller and the traditional MPC controller is conducted. Finally, the new controller is applied to address the trajectory tracking problem of the 3WID3WIS mobile robot. The experimental results show that DMPC improves the lateral trajectory tracking accuracy by 62.94% and the heading angle tracking accuracy by 34.81% compared to MPC. [ABSTRACT FROM AUTHOR]

The document "Mobile Robots: Trajectory Analysis, Positioning and Control" explores the advancements and challenges in mobile robotics, emphasizing trajectory analysis, positioning, and control as critical components. It discusses the integration of technologies like SLAM, control systems, and human-robot interaction to enhance the autonomy, efficiency, and adaptability of mobile robots. The document also highlights the importance of interdisciplinary collaboration and future directions in enhancing autonomy, SLAM technologies, human-robot collaboration, and ethical considerations in mobile robotics. The contributions in the document cover topics such as precise positioning systems, odometry error correction, VR-based teleoperation interfaces, and advanced control strategies for mobile robots. [Extracted from the article]

The last few decades have seen impressive developments in the field of robotics, especially in the areas of autonomous navigation and control. Robust algorithms that can facilitate effective decision-making in real-time settings are needed as the need for intelligent robots that can function in complex and dynamic contexts grows. Through trial-and-error interactions with their surroundings, reinforcement learning (RL) has become a promising method for teaching intelligent agents to navigate and control robots independently. The purpose of this study is to look at the creation and use of reinforcement learning algorithms for intelligent robot control and autonomous navigation. With an emphasis on methods like deep Q-learning, policy gradients, and actor-critic approaches, the research delves into the theoretical underpinnings of reinforcement learning and how it has been applied to the field of robotics. This study assesses how well RL algorithms work to help robots acquire the best navigational strategies in challenging surroundings through an extensive literature review and empirical investigation. In addition, the study suggests new improvements and optimizations for current reinforcement learning algorithms to tackle problems unique to robot navigation, such as avoiding obstacles, routing, and interactions with dynamic environments. These improvements increase the effectiveness, flexibility, and security of independent robot navigation systems by utilizing knowledge from cognitive science and neuroscience. The suggested methods are experimentally evaluated through both real-world applications on physical robotic platforms and simulation-based research. Performance measures including navigation speed, success rate, and collision avoidance ability are used to evaluate how well the suggested algorithms operate in different scenarios and circumstances. [ABSTRACT FROM AUTHOR]

In ground mobile robots, effective path planning relies on their ability to assess the types and conditions of the surrounding terrains. Neural network‐based methods, which primarily use visual images for terrain classification, are commonly employed for this purpose. However, the reliability of these models can vary due to inherent discrepancies between the training images and the actual environment, leading to erroneous classifications and operational failures. Retraining models with additional images from the actual operating environment may enhance performance, but obtaining these images is often impractical or impossible. Moreover, retraining requires substantial offline processing, which cannot be performed online by the robot within an embedded processor. To address this issue, this paper proposes a neural network‐based terrain classification model, trained using an existing data set, with a novel uncertainty rejection filter (URF) for terrain‐aware path planning of mobile robots operating in unknown environments. A robot, equipped with a pretrained model, initially collects a small number of images (10 in this work) from its current environment to set the target uncertainty ratio of the URF. The URF then dynamically adjusts its sensitivity parameters to identify uncertain regions and assign associated traversal costs. This process occurs entirely online, without the need for offline procedures. The presented method was evaluated through simulations and physical experiments, comparing the point‐to‐point trajectories of a mobile robot equipped with (1) the neural network‐based terrain classification model combined with the presented adaptive URF, (2) the classification model without the URF, and (3) the classification model combined with a nonadaptive version of the URF. Path planning performance measured the Hausdorff distances between the desired and actual trajectories and revealed that the adaptive URF significantly improved performance in both simulations and physical experiments (conducted 10 times for each setting). Statistical analysis via t‐tests confirmed the significance of these results. [ABSTRACT FROM AUTHOR]

In recent years, the navigation of mobile robots has been of great interest. One of the important challenges in the navigation of mobile robots is the obstacle avoidance problem so that the robots do not collide with each other and obstacles, during their movement. Hence, for good navigation, a reliable obstacle avoidance methodology is needed. On the other hand, some of the other most important challenges in robot control are in the field of motion planning. The main goal of motion planning is to compile (interpret) high-level languages into a series of primary low-level movements. In this paper, a novel online sensor-based motion planning algorithm that employs the Adaptive Neuro-Fuzzy Inference System (ANFIS) controller is proposed. Also, this algorithm is able to distance the robots from the obstacles (i.e. it provides a solution to the obstacle avoidance problem). In the proposed motion planning algorithm, three distances (i.e. the distance of the robot from the obstacles in three directions: right, left, and front) have been used to prioritize the goal search behavior and obstacle avoidance behavior and to determine the appropriate angle of rotation. Then, for determining the linear velocity, the nearest distance from obstacles and distance from the goal have been used. The proposed motion planning algorithm has been implemented in the gazebo simulator (by using Turtlebot) and its performance has been evaluated. Finally, to improve the performance of the proposed motion planning algorithm, We have used type-1, interval type-2, and interval type-3 fuzzy sets, then, we have evaluated and compared the efficiency of the proposed algorithm for each of these fuzzy sets under specific criteria. [ABSTRACT FROM AUTHOR]

Comprehensive Summary Key Scientists As well known, cilia play an irreplaceable role in sensing and movement of natural organisms because they can respond to external signals and generate net flow in complex environments. Based on these findings, scientists further explored the functions of natural cilia and have developed many artificial cilia in the past nearly thirty years. This review provides an overview of recent progress of artificial cilia. Firstly, we summarize the characteristics of natural cilia. Subsequently, we introduce the fabrication methods including template, magnetic assembly, lithography, and 3D printing. Then we discuss the stimulus actuation of artificial cilia from two major modes: contact control and remote control. In addition, five typical types of applications, including adhesion regulation, intelligent control, mobile microrobot, biological sensor and anti‐counterfeiting, were reviewed in detail. Finally, we present the challenges and future development in the fields of advanced artificial cilia.From 1994 to 1997, research teams including Bohringer, Donald, and Macdonald from Cornell University and Suh and Kovacs from Stanford University reported on the application of artificial cilia in the field of micro‐electro‐mechanical systems (MEMS) technology,[1‐3] while Fujita from the University of Tokyo and Stemme from KTH were also conducting research in artificial cilia fields at the same time. In 2006, Krijnen's team designed a combination of cricket cerci cilia and MEMS technology to further extend the application of artificial cilia to flow sensors.[4] In 2007, Superfine's crew introduced the polycarbonate track‐etched (PCTE) membrane method into the fabrication of artificial cilia, achieving the fabrication of high aspect ratio cilia in a liquid free environment.[5] Since 2008, Toonder's lab has been focusing on the research of artificial cilia and have made outstanding contributions in lab on chip and achieve various stimuli responsive actuation of artificial cilia.[6‐8] In 2010, Alexeev's research team used computer simulations to design a hydrodynamic model of ciliary strike.[9] Since 2017, Jiang and his coworkers have made progress in the application of directional manipulation of artificial cilia, including research on solids, droplets and bubbles.[10‐13] In 2020—2024, Sitti's research group has introduced cilia into the field of bioinspired microrobot and realized the programmed actuation of artificial cilia from the perspectives of electricity, photothermal and magnetic.[14‐19] In 2022, Jeong's group innovated the traditional magnetic assembly method to fabricate three‐dimensional nanoscale cilia with regular spatial distribution and controllable geometry.[20‐21] [ABSTRACT FROM AUTHOR]