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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]

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]

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]

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]

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]

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]

To address the issues of exploration imbalance and slow convergence speed in the Q‐learning path planning algorithm, an adaptive improved Q‐learning path planning algorithm based on an obstacle learning matrix and artificial potential field (APF) is proposed. First, an obstacle learning matrix is established to store the positions of obstacles encountered in each learning iteration, avoiding redundant learning of the same obstacle. Second, an adaptive exploration enhancement strategy is introduced by incorporating the concept of success rate. This strategy divides the decay process of the exploration rate into an exploration‐dominant initial stage and an exploitation‐dominant later stage. Finally, an APF‐weighted action selection strategy is introduced, utilizing the guiding force generated by the APF to encourage the mobile robot to avoid obstacles more efficiently. Simulation results show that the proposed method can effectively reduce the number of iterations and time consumption in the optimization process, resulting in a smoother and more stable planned path. [ABSTRACT FROM AUTHOR]

Path planning is an important part of the research field of mobile robots. How to plan more suitable paths more quickly has become a challenging optimization problem that requires consideration of several constraints and performance metrics. Given the multitude of optimization objectives and the intricate nature of addressing the static path planning problem, it is usually converted into a single-objective optimization problem. However, this approach would lead to the algorithm being unable to thoroughly explore the solution space. To address this problem, a nondominated sorting whale optimization algorithm based on crowding degree and memory (NSWOACDM) is presented and applied to unmanned vehicle path planning in this paper. Both the length and curvature of path are optimized simultaneously. Experimental results on benchmark functions show that the proposed algorithm is more effective compared with other traditional multi-objective algorithms. The effects of different control points on the optimization results are analyzed through path planning experiments. The experimental results demonstrate that the NSWOACDM algorithm exhibits superior performance in comparison to similar algorithms. Furthermore, it is capable of identifying several viable paths within a reduced timeframe in the context of the multi-objective path planning issue. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper investigates the formation tracking problem for multiple mobile robots via self‐triggered distributed model predictive control (DMPC) strategy and path‐parameter communication manner. To ensure the robots follow the desired formation structure along the predefined paths, we establish appropriate tracking error models that form a multi‐agent system. At triggered instants, each agent exchanges a sequence of path parameters representing the robot's position, resolves the optimal control problem (OCP) and subsequently determines the open‐loop phase. Different from existing coordination methodology, the proposed scheme exhibits two essential merits in environments where resources are particularly limited: (1) The tracking task of robots is achieved by designing an appropriate OCP under the DMPC scheme, and the formation task of robots is achieved through the synchronization of one‐dimensional path parameters instead of the multi‐dimensional state information, which demands less communication load; (2) The incorporation of the self‐triggered scheduler acquires the desired control performance with less calculation time, thereby relieving the computational and communication costs. Sufficient conditions are proposed to guarantee the recursive feasibility of the OCP and the closed‐loop stability. Simulation results illustrate the validity of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

Pipelines are crucial infrastructure supporting human life, yet their maintenance costs are substantial, necessitating the development of time-efficient robotic systems. Therefore, this study proposes an in-pipe mobile robot powered by wind generated using a ducted fan rotor, enabling faster movement within pipelines than conventional methods, e.g., inchworm. Deriving the propulsion force from wind requires analyzing airflow within the pipeline, which is challenging due to the confined space and complexity, especially when considering the presence of robots. Hence, we developed a prototype of a ducted-fan type pipeline robot (DPR) and experimentally investigated duct shapes that enhance the propulsion of the DPR within pipelines. As a result, we identified duct shapes that amplify the propulsion force generated by the ducted fan within pipelines. Additionally, we also experimentally elucidated the relationship between the distance from the pipe's end and the propulsion force of the robot. Furthermore, we demonstrated the capabilities of the DPR by traversing a pipeline with a diameter of 110 mm, achieving speeds of 1500 mm/s in horizontal pipes and 700 mm/s while ascending vertical pipes. The results show that DRP has the potential for in-pipe inspection. [ABSTRACT FROM AUTHOR]

To overcome the limitations of particle swarm optimization (PSO) in mobile robot path planning, including issues such as premature convergence and sensitivity to local optima, this study proposes a novel approach, dynamic multipopulation particle swarm optimization (DMPSO). First, the multipopulation particle swarm optimization (MPSO) framework is extended by introducing a dynamic multipopulation strategy that adjusts the number of subpopulations in real‐time. This strategy is designed to enhance the algorithm's local search capabilities and accelerate its convergence. Second, the inertia weights and learning factors within the algorithm are refined to achieve a balance between global exploration and local exploitation. Furthermore, an initialization strategy based on fitness variance is developed to improve population diversity, mitigate premature convergence, and enhance the algorithm's ability to locate global optima. Lastly, a positive feedback acceleration factor is introduced to optimize particle positions, thereby improving local search capabilities and accelerating convergence. Simulation experiments have validated that DMPSO offers improved exploration capabilities, enhanced search precision, and a more rapid convergence rate. In comparison to PSO, DMPSO reduces the path length by 3% and decreases the number of convergence iterations by 17%. [ABSTRACT FROM AUTHOR]

This study proposes a nonlinear adaptive optimal control method, the adaptive H2 control method, applied to the trajectory tracking problem of the wheeled mobile robot (WMR) with four-wheel mecanum wheels. From the perspective of solving mathematical problems, finding an analytical adaptive control solution that satisfies the adaptive H2 performance criterion for the trajectory tracking problem of the WMR with four-wheel mecanum wheels is an extremely challenging task due to the high complexity of the dynamic system. To analytically derive the control law and adaptive control law for this trajectory tracking problem, a proportional-derivative (PD) type transformation is employed to formalize the trajectory tracking error dynamics between the WMR and the desired trajectory (DT). Based on an in-depth analysis of the trajectory tracking error dynamics, a closed-form adaptive control law is analytically derived from the highly complex nonlinear dynamic system equations. This control law provides a solution to the trajectory tracking problem of the WMR while satisfying the adaptive H2 performance criterion. The proposed adaptive nonlinear control method offers a simple control structure and advantages such as improved energy efficiency. Finally, simulations and experimental implementations were conducted to verify the performance of the proposed adaptive H2 control method and the H2 control method in tracking the DT. The results demonstrate that, compared to the H2 control method, the adaptive H2 control method exhibits superior trajectory tracking performance, particularly in the presence of significant model uncertainties. [ABSTRACT FROM AUTHOR]

Inspection robots, which improve hazard identification and enhance safety management, play a vital role in the examination of high-risk environments in many fields, such as power distribution, petrochemical, and new energy battery factories. Currently, the position precision of the robots is a major barrier to their broad application. Exact kinematic model and control system of the robots is required to improve their location accuracy during movement on the unstructured surfaces. By a virtual engine and digital twins, this study put forward a visualization monitoring and control system framework which can address the difficulties in the intelligent factories while managing a variety of data sources, such as virtual–real integration, real-time feedback, and other issues. To develop a more realistic dynamic model for the robots, we presented a neural-network-based compensation technique for the nonlinear dynamic model parameters of outdoor mobile robots. A physical prototype was applied in the experiments, and the results showed that the system is capable of controlling and monitoring outdoor mobile robots online with good visualization effects and high real-time performance. By boosting the positional accuracy of robots by 18% when navigating obstacles, the proposed precise kinematic model can increase the inspection efficiency of robots. The visualization monitoring and control system enables visual, digital, multi-method, and complete real-time inspections in high-risk factories, such as new energy battery factories, to ensure the safe and stable operations. [ABSTRACT FROM AUTHOR]

Path planning is a core technology for mobile robots. However, existing state-of-the-art methods suffer from issues such as excessive path redundancy, too many turning points, and poor environmental adaptability. To address these challenges, this paper proposes a novel global and local fusion path-planning algorithm. For global path planning, we reduce path redundancy and excessive turning points by designing a new heuristic function and constructing an improved path generation method. For local path planning, we propose an environment-aware dynamic parameter adjustment strategy, incorporating deviation and avoidance dynamic obstacle evaluation factors, thus addressing issues of local optima and timely avoidance of dynamic obstacles. Finally, we fuse those global and local path-planning improvements to form our fusion path-planning algorithm, which can enhance the robot's adaptability to complex scenarios while reducing path redundancy and turning points. Simulation experiments demonstrate that the improved fusion path-planning algorithm not only effectively addresses existing issues but also operates with higher efficiency. [ABSTRACT FROM AUTHOR]

Odor source localization (OSL) technology allows autonomous agents like mobile robots to localize a target odor source in an unknown environment. This is achieved by an OSL navigation algorithm that processes an agent's sensor readings to calculate action commands to guide the robot to locate the odor source. Compared to traditional 'olfaction-only' OSL algorithms, our proposed OSL algorithm integrates vision and olfaction sensor modalities to localize odor sources even if olfaction sensing is disrupted by non-unidirectional airflow or vision sensing is impaired by environmental complexities. The algorithm leverages the zero-shot multi-modal reasoning capabilities of large language models (LLMs), negating the requirement of manual knowledge encoding or custom-trained supervised learning models. A key feature of the proposed algorithm is the 'High-level Reasoning' module, which encodes the olfaction and vision sensor data into a multi-modal prompt and instructs the LLM to employ a hierarchical reasoning process to select an appropriate high-level navigation behavior. Subsequently, the 'Low-level Action' module translates the selected high-level navigation behavior into low-level action commands that can be executed by the mobile robot. To validate our algorithm, we implemented it on a mobile robot in a real-world environment with non-unidirectional airflow environments and obstacles to mimic a complex, practical search environment. We compared the performance of our proposed algorithm to single-sensory-modality-based 'olfaction-only' and 'vision-only' navigation algorithms, and a supervised learning-based 'vision and olfaction fusion' (Fusion) navigation algorithm. The experimental results show that the proposed LLM-based algorithm outperformed the other algorithms in terms of success rates and average search times in both unidirectional and non-unidirectional airflow environments. [ABSTRACT FROM AUTHOR]

For deformed wheel-based staircase-climbing robots, the accuracy of staircase step geometry perception and scene mapping are critical factors in determining whether the robot can successfully ascend the stairs and continue its task. Currently, while there are LiDAR-based algorithms that focus either on step geometry detection or scene mapping, few comprehensive algorithms exist that address both step geometry perception and scene mapping for staircases. Moreover, significant errors in step geometry estimation and low mapping accuracy can hinder the ability of deformed wheel-based mobile robots to climb stairs, negatively impacting the efficiency and success rate of task execution. To solve the above problems, we propose an effective LiDAR-Inertial-based point cloud detection method for staircases. Firstly, we preprocess the staircase point cloud, mainly using the Statistical Outlier Removal algorithm to effectively remove the outliers in the staircase scene and combine the vertical angular resolution and spatial geometric relationship of LiDAR to realize the ground segmentation in the staircase scene. Then, we perform post-processing based on the point cloud map obtained from LiDAR SLAM, extract the staircase point cloud and project and fit the staircase point cloud by Ceres optimizer, and solve the dimensional information such as depth and height of the staircase by combining with the mean filtering method. Finally, we fully validate the effectiveness of the method proposed in this paper by conducting multiple sets of SLAM and size detection experiments in real different staircase scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we consider a control architecture for a mobile robot equipped with visual sensors to pursue a target object in an environment with convex-shaped obstacles. The pursuit involves crucial occlusion avoidance and field of view maintenance, referred to as visual safety. Our goal is to achieve this safety through a multi-layered control architecture consisting of a planning layer and a safety layer. We propose functions that represent occlusion avoidance and field of view maintenance and derive conditions for these to act as control barrier functions. Utilizing these functions, we implement an optimal control at the planning layer and an optimization-based control at the safety layer. The effectiveness of this method is verified through two tasks: guiding the target object into a target location and preventing the target object from entering a target location. [ABSTRACT FROM AUTHOR]

On‐ground emulation is crucial to cutting‐edge space technology involving the rendezvous and maintenance of noncooperative objects. However, existing systems are restricted to two objects and have a limited range of motion, and they cannot emulate some real space missions. The introduction of mobile robots is a potential solution, but on the one hand, their relatively low precision may ruin the emulation; on the other hand, how to take full advantage of mobile robots' larger range of motion in on‐ground emulation is still to be solved. This paper presents a novel emulation platform for noncooperative object missions in space that can complete high‐precision kinetic emulations of large‐scale and multiobject motion. We use different kinds of mobile robots in our system and overcome the poor kinetic accuracy of mobile platforms. First, the composition and kinetics of the whole system are characterized. We simplify the hyper‐redundant system and propose a trajectory mapping method based on the workspace of mobile manipulators. Then, a dynamic programming method is proposed to plan the joint trajectories of mobile manipulators. We propose a feasibility function based on manipulability and the singularity avoidance coefficient, which ensures that the mobile base moves smoothly and that high‐precision manipulators have enough space to compensate for the movement errors of mobile bases. Finally, experimental results verify the feasibility and effectiveness of the system and the planning methods. [ABSTRACT FROM AUTHOR]

The stability of the Tracking Wheel Mobile Robot with Teleoperation System and Path Following Method is discussed in this study. The path is to be tracked by the host computer which is the master robot. The response from the robot is captured on camera. As the slave robot approaches the target position, the camera captures the response robot's position and as well as moving trajectory. The host computer receives all of the images, enabling mobile robot deviation recoveries. The slave robot can use teleoperation to follow the sensor based on the decisions made by the master robot. The Lyapunov function in the Fuzzy Neural Network (FNN) control structure assures the system's stability and satisfactory performance. It supports a mobile robot's ability to adhere to a reference trajectory without deviating from it. Finally, the outcome of the simulation demonstrates that our controller is capable of tracking different environmental conditions and maintaining stability. [ABSTRACT FROM AUTHOR]

Accurate localization of pedestrians and mobile robots is critical for navigation, emergency response, and autonomous driving. Traditional localization methods, such as satellite signals, often prove ineffective in certain environments, and acquiring sufficient positional data can be challenging. Single image localization techniques have been developed to address these issues. However, current deep learning frameworks for single image localization that rely on domain adaptation fail to effectively utilize semantically rich high-level features obtained from large-scale pretraining. This paper introduces a novel framework that leverages the Contrastive Language-Image Pre-training model and prompts to enhance feature extraction and domain adaptation through semantic information. The proposed framework generates an integrated score map from scene-specific prompts to guide feature extraction and employs adversarial components to facilitate domain adaptation. Furthermore, a reslink component is incorporated to mitigate the precision loss in high-level features compared to the original data. Experimental results demonstrate that the use of prompts reduces localization errors by 26.4 % in indoor environments and 24.3 % in outdoor settings. The model achieves localization errors as low as 0.75 m and 8.09 degrees indoors, and 4.56 m and 7.68 degrees outdoors. Analysis of prompts from labeled datasets confirms the model's capability to effectively interpret scene information. The weights of the integrated score map enhance the model's transparency, thereby improving interpretability. This study underscores the efficacy of integrating semantic information into image localization tasks. [ABSTRACT FROM AUTHOR]

This paper introduces a motion planning and cooperative formation control approach for quadruped robots and multi-agent systems. First, in order to improve the efficiency and safety of quadruped robots navigating in complex environments, this paper proposes a new planning method that combines the dynamic model of quadruped robots and a gradient-optimized obstacle avoidance strategy without Euclidean Signed Distance Field. The framework is suitable for both static and slow dynamic obstacle environments, aiming to achieve multiple goals of obstacle avoidance, minimizing energy consumption, reducing impact, satisfying dynamic constraints, and ensuring trajectory smoothness. This approach differs in that it reduces energy consumption throughout the movement from a new perspective. Meanwhile, this method effectively reduces the impact of the ground on the robot, thus mitigating the damage to its structure. Second, we combine the dynamic control barrier function and the virtual leader-follower model to achieve efficient and safe formation control through model predictive control. Finally, the proposed algorithm is validated through both simulations and real-world scenarios testing. [ABSTRACT FROM AUTHOR]

Research on navigation of mobile robots in uncertain dynamic environments is of great importance. This article is focused on solving existing problems such as planning, optimization, failure in difficult situations, and error rate vector prediction under constantly changing uncertain conditions. The aim of the conducted research is to propose a multilayer fuzzy logic model based on decision making for robots to find safe path navigation by overcoming any kind of obstacles and to understand collision-free movement of mobile robots in an uncertain dynamic environment. In this study, fuzzy logic control prediction and multilayer solution priority rules are used to improve the quality of the next position based on the path length, safety and realization time. For this purpose, the article considers the topic of calculating the risk of collision with obstacles for planning the trajectory of a mobile robot with a fuzzy controller in an environment with dynamic obstacles. [ABSTRACT FROM AUTHOR]

Humanoid robots are becoming a global research focus. Due to the limitations of bipedal walking technology, mobile humanoid robots equipped with a wheeled chassis and dual arms have emerged as the most suitable configuration for performing complex tasks in factory or home environments. To address the high redundancy issue arising from the wheeled chassis and dual-arm design of mobile humanoid robots, this study proposes a whole-body coordinated motion control algorithm based on arm potential energy optimization. By constructing a gravity potential energy model for the arms and a virtual torsional spring elastic potential energy model with the shoulder-wrist line as the rotation axis, we establish an optimization index function for the arms. A neural network with variable stiffness is introduced to fit the virtual torsional spring, representing the stiffness variation trend of the human arm. Additionally, a posture mapping method is employed to map the human arm potential energy model to the robot, enabling realistic humanoid movements. Combining task-space and joint-space planning algorithms, we designed experiments for single-arm manipulation, independent object retrieval, and dual-arm carrying in a simulation of a 23-degree-of-freedom mobile humanoid robot. The results validate the effectiveness of this approach, demonstrating smooth motion, the ability to maintain a low potential energy state, and conformity to the operational characteristics of the human arm. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic highlighted the urgent need for effective surface disinfection solutions, which has led to the use of mobile robots equipped with ultraviolet (UVC) lamps as a promising technology. This study aims to optimize the navigation of differential mobile robots equipped with UVC lamps to ensure maximum efficiency in disinfecting complex environments. Bio-inspired metaheuristic algorithms such as the gazelle optimization algorithm, whale optimization algorithm, bat optimization algorithm, and particle swarm optimization are applied. These algorithms mimic behaviors of biological beings such as the evasive maneuvers of gazelles, the spiral hunting patterns of whales, the echolocation of bats, and the collective behavior of flocks of birds or schools of fish to optimize the robot's trajectory. The optimization process adjusts the robot's coordinates and the time it takes to stops at key points to ensure complete disinfection coverage and minimize the risk of excessive UVC exposure. Experimental results show that the proposed algorithms effectively adapt the robot's trajectory to various environments, avoiding obstacles and providing sufficient UVC radiation exposure to deactivate target microorganisms. This approach demonstrates the flexibility and robustness of these solutions, with potential applications extending beyond COVID-19 to other pathogens such as influenza or bacterial contaminants, by tuning the algorithm parameters. The results highlight the potential of bio-inspired metaheuristic algorithms to improve automatic disinfection and achieve safer and healthier environments. [ABSTRACT FROM AUTHOR]

Patrolling is one of the basic tasks of robots which has a wide range of applications in reality. The related issues have received much attention in the research of computational geometry and robotics. The problem of robot patrolling requires continuous coverage of designated areas for a long time, and this paper employed idle time to express the patrol efficiency of robots. The shorter the idle time, the better the efficiency. Therefore, this paper designed a traversable circle model, where the robot needs to patrol the boundary and the diameter of the circle. This paper firstly proposed an algorithm which used two variable speed robots for this model. On the basis of analyzing the geometric features of the model, this paper proved the optimality of the algorithm according to its requirements. Then, this paper proposed an algorithm for three variable speed robots to cooperate with each other to patrol traversable circle and proved the optimal idle time of this algorithm is 19/25 of the partition algorithm. Finally, this paper verified the correctness of the theoretical analysis through simulation experiments. [ABSTRACT FROM AUTHOR]

Detecting humans in images not only provides vital data for a wide array of applications in intelligent systems but also allows for the classification of specific groups of individuals for authorization through various methods based on several examples. This paper presents a novel approach to classify persons wearing a face mask as an example. The system utilizes an omnidirectional camera on the mobile robot. This choice is driven by the camera's ability to capture a complete 360° scene in a single shot, enabling the system to gather a wide range of information within its operational environment. Our system classifies persons using a deep learning model by gathering information from the equirectangular panoramic images, estimating a person's position, and computing robot path planning without using any distance sensors. In the proposed method, the robot can classify two groups of persons: those facing the camera but without face masks and those not facing the camera. In both cases, the robot approaches the persons, inspects their face masks, and issues warnings on its screen. The evaluation experiments are designed to validate our system performance in a static indoor setting. The results indicate that our suggested method can successfully classify persons in both cases while approaching them. [ABSTRACT FROM AUTHOR]

Snake-like robots can achieve flexible movement by simultaneously actuating multiple joints; however, the challenge of high power consumption by driving numerous servomotors under high-load conditions remains. To address this issue, we propose a mechanism that transforms the rear link of a snake-like robot into a wheel-like configuration, enabling a three-wheeled vehicle mode that provides the same traveling speed and efficiency as a wheeled mobile robots on flat surfaces. First, we detail the method for driving the servomotor to achieve undulating locomotion in the snake-like robot with nonuniform link lengths. Next, we propose a method for smoothly switching between the complex-shaped wheel mode and undulating locomotion. Finally, we conduct experiments to assess the travel efficiency in both the undulating mode and the proposed wheel mode across various road surface conditions. Our results demonstrate that the wheel mode achieves higher travel efficiency than the undulating mode on the smooth floors and asphalt. [ABSTRACT FROM AUTHOR]

This study presents an autonomous inspection system for underground pits using an articulated mobile robot. The underground pit is composed of several rooms surrounded by concrete connected to each other by winding pipes. Based on an action list created in advance and environmental maps, the robot autonomously inspects the underground pit by switching between three actions: planar motion, winding pipe passing motion, and image capturing. In planar motion, the robot moves around the room while avoiding obstacles and crosses ditches through distinctive behaviors, switching the allocation of the grounded/ungrounded wheels. In the winding pipe-passing motion, the target path is autonomously generated based on the parameters of the winding pipe. Laboratory and field tests were conducted to demonstrate the effectiveness of the proposed system. [ABSTRACT FROM AUTHOR]

The need for fully autonomous mobile robots has surged over the past decade, with the imperative of ensuring safe navigation in a dynamic setting emerging as a primary challenge impeding advancements in this domain. In this article, a Safety Critical Model Predictive Control based on Dynamic Feedback Linearization tailored to the application of differential drive robots with two wheels is proposed to generate control signals that result in obstacle‐free paths. A barrier function introduces a safety constraint to the optimization problem of the Model Predictive Control (MPC) to prevent collisions. Due to the intrinsic nonlinearities of the differential drive robots, computational complexity while implementing a Nonlinear Model Predictive Control (NMPC) arises. To facilitate the real‐time implementation of the optimization problem and to accommodate the underactuated nature of the robot, a combination of Linear Model Predictive Control (LMPC) and Dynamic Feedback Linearization (DFL) is proposed. The MPC problem is formulated on a linear equivalent model of the differential drive robot rendered by the DFL controller. The analysis of the closed‐loop stability and recursive feasibility of the proposed control design is discussed. Numerical experiments illustrate the robustness and effectiveness of the proposed control synthesis in avoiding obstacles with respect to the benchmark of using Euclidean distance constraints. [ABSTRACT FROM AUTHOR]

In this study, we developed a four‐wheel‐driven autonomous mobile robot to transport heavy objects on narrow alleys and steep slopes. The robot, which weighs 450 kg, is driven by four 650 W hub motors to climb 20° steep slopes and travel through a narrow alley. Two 2D‐LiDARs attached to the front and rear of the robot body are used for highly accurate driving. A method based on the least squares approach for estimating the position and posture of the robot body is proposed by using the point cloud information of the side wall of the narrow alley. Field experiments on test courses demonstrated that the autonomous robot could transport heavy objects over steep slopes and travel in a narrow alley with high accuracy. [ABSTRACT FROM AUTHOR]

Methods for socially-aware robot path planning are increasingly needed as robots and humans increasingly coexist in shared industrial spaces. The practice of clearly separated zones for humans and robots in shop floors is transitioning towards spaces where both humans and robot operate, often collaboratively. To allow for safer and more efficient manufacturing operations in shared workspaces, mobile robot fleet path planning needs to predict human movement. Accounting for the spatiotemporal nature of the problem, the present work introduces a spatiotemporal graph neural network approach that uses graph convolution and gated recurrent units, together with an attention mechanism to capture the spatial and temporal dependencies in the data and predict human occupancy based on past observations. The obtained results indicate that the graph network-based approach is suitable for short-term predictions but the rising uncertainty beyond short-term would limit its applicability. Furthermore, the addition of learnable edge weights, a feature exclusive to graph neural networks, enhances the predictive capabilities of the model. Adding workspace context-specific embeddings to graph nodes has additionally been explored, bringing modest performance improvements. Further research is needed to extend the predictive capabilities beyond the range of scenarios captured through the original training, and towards establishing standardised benchmarks for testing human motion prediction in industrial environments. [ABSTRACT FROM AUTHOR]

People across the globe witnessed the meteoric rise of the recently discovered coronavirus, which caught governments, healthcare systems, and civilisations off guard. Robotics, self-governing systems, and intelligent devices can contribute positively to a rapid epidemic. Smart assistive robots can help patients and healthcare staff in general and make the anticipation, containment, and exacerbation of coronavirus easier by highlighting the coronavirus burden on healthcare structures during a crisis. In this type of situation, a smart-assistive robot with aid capabilities is promising, especially for those putting their lives in jeopardy by interacting with ill patients in hygienic environments. By providing regular monitoring and patient assistance, the intelligent helper robot could assist in lowering the danger of illness spreading to others. Concerning the application this paper discusses, a robot with a wheeled base and a humanoid structure can aid isolated patients and connect wirelessly anywhere in the world (using the Internet of Things) with patients far away more effectively without any physical touch. The designed robot is incredibly light (approximately 7 kg) and easily transported to the appropriate location (any hospital or medical camp, for example). Its modular design allows quick assembly and disassembly. The robot can comfortably handle 10–15 kg of load on its holding plate. This study also explains the structural capability and method of directing the robot in an unfamiliar area using ultrasonic sensors for obstacle avoidance, a camera module for real-time vision, and a Skype call feature that may enable doctors to keep tabs on a patient's health. [ABSTRACT FROM AUTHOR]