Robotics and Autonomous Systems最新文献

{"title":"Learning multi-robot task allocation using capsule networks and attention mechanism","authors":"Steve Paul,&nbsp;Souma Chowdhury","doi":"10.1016/j.robot.2025.105085","DOIUrl":"10.1016/j.robot.2025.105085","url":null,"abstract":"<div><div>This paper presents a new graph reinforcement learning (RL) architecture to solve multi-robot task allocation (MRTA) problems without requiring any tedious heuristics. Multi-feature tasks are abstracted as nodes in an undirected graph in this case. The primary goal is to not only generalize across unseen problems of similar size but also scale to problems with much larger task spaces without retraining; which otherwise could be particularly expensive when simulating multi-robot operations. While drawing inspiration from the emerging paradigm in learning to solve combinatorial optimization (CO) problems, a new encoder–decoder architecture called Capsule Attention-based Mechanism or CAPAM is presented here to achieve this goal. More specifically, a novel choice of <em>encoder</em> is made in the form of graph capsule convolutional networks, which enables permutation invariant embeddings that capture the local and global structure of the task graph by using higher-order statistical moments of the vectors of node features. This encoded information is combined with a <em>context</em> component encoding mission and robot states, and processed through the <em>decoder</em> that computes the probability of selecting different available tasks by a robot. To train the CAPAM model, a policy-gradient method based on Proximal Policy Optimization is used. When evaluated over unseen scenarios, CAPAM demonstrates comparable task completion performance and faster decision-making compared to standard non-learning-based online MRTA methods. CAPAM demonstrates substantial gains in generalizability and (task) scalability in comparison to a popular approach for learning to solve CO problems (the pure attention mechanism) and preserves this performance advantage even under partial observation scenarios.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"193 ","pages":"Article 105085"},"PeriodicalIF":4.3,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Positioning of autonomous mobile robot using multi-lateration with pattern recognition and differential evolution","authors":"Tz-Yu Yang ,&nbsp;Brijesh Patel ,&nbsp;Po-Yan Chiu ,&nbsp;Chien-Ching Ma ,&nbsp;Chii-Rong Yang ,&nbsp;Chao-Lung Yang ,&nbsp;Po Ting Lin","doi":"10.1016/j.robot.2025.105113","DOIUrl":"10.1016/j.robot.2025.105113","url":null,"abstract":"<div><div>The extensive adoption of Autonomous Mobile Robots (AMR) in manufacturing, processing, and intelligent logistics has witnessed a remarkable increase, driven by the rapid growth of smart manufacturing and Industry 4.0. AMRs serve a dual role, facilitating both product handling and transportation. The precision of AMR positioning is of paramount importance. The prevalent approach to indoor positioning and navigation involves the use of cameras, optical Light Detection and Ranging (LiDAR) sensors. However, relying solely on LiDAR-based motion estimation for relative positioning can result in gradual displacement errors, impacting accuracy. This paper introduces a dual-positioning strategy to address this challenge, incorporating secondary localization methods to ensure precise spatial confirmation and task execution for a High Payload Autonomous Mobile Robot (HAMR). This proposed method integrates a RGB-D camera with the HAMR’s manipulator. It recognizes wall patterns (ArUco) and measures their distance from the HAMR, employing multi-lateration to calculate the HAMR’s position within the real-world coordinate system. This paper presents an indoor positioning method for HAMRs using ArUco code, enabling multi-lateration measurements within a 15 mm error. Differential Evolution (DE) is employed for motion analysis to solve inverse kinematics, enabling dynamic analysis of HAMRs with redundant degrees of freedom. This technique effectively compensates for positioning errors, significantly enhancing the AMR’s capabilities.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"193 ","pages":"Article 105113"},"PeriodicalIF":4.3,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transformer-based aerial robot tracking system in environments with wind disturbances","authors":"Pengkai Wang ,&nbsp;Jonghoek Kim ,&nbsp;Mitra Ghergherehchi ,&nbsp;Mingxuan Zhang ,&nbsp;Estrella Montero ,&nbsp;Luwei Liao ,&nbsp;Zhong Yang ,&nbsp;Hongyu Xu","doi":"10.1016/j.robot.2025.105104","DOIUrl":"10.1016/j.robot.2025.105104","url":null,"abstract":"<div><div>Unmanned aerial vehicles (UAVs) are increasingly used in agriculture, surveillance, and search and rescue. However, maintaining stable flight and accurate navigation in dynamic environments, especially with wind disturbances, remains a challenge. Traditional navigation systems often struggle with unreliable sensor data, complicating pose estimation and tracking. This article proposes an advanced master–slave UAV system combining a transformer-based model with YOLO for enhanced tracking in wind-affected environments. YOLO performs real-time object detection, extracting feature points matched with known landmarks to estimate the UAV’s position. To address the challenges of wind disturbances, we simulate various wind conditions and train the model under different wind disturbance environments. Using transformer-based trajectory and pose predictions, we provide control compensation to counteract the effects of wind disturbances, ensuring stable flight in dynamic conditions. The pose estimation is refined by integrating visual data with inertial measurement unit (IMU) data using transformer architectures. A vision-based formation control strategy is introduced for precise relative positioning in multi-UAV formations. Initially designed for three UAVs, this strategy is extended to handle larger formations and complex geometric shapes, focusing on maintaining a triangle formation. A graph-based dynamic formation control framework enables real-time adaptation to formation changes and environmental conditions. The approach improves MPC control with a transformer model, enhancing adaptability to wind disturbances. The system’s effectiveness is validated using webots simulations, demonstrating its ability to track UAVs and adapt to challenging environmental conditions. A theorem proves the convergence of the control law using Lyapunov’s direct method, ensuring that formation errors decay over time. Comparative experiments and webots simulations confirm the approach’s feasibility, validating its robustness in maintaining precise formation control under dynamic environmental factors. Finally, we validate the reliability of our method in real-world environments, confirming its practical applicability.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"193 ","pages":"Article 105104"},"PeriodicalIF":4.3,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based inverse kinematics scalability for prismatic tensegrity structural manipulators","authors":"Aidar Shakerimov ,&nbsp;Medetkhan Altymbek ,&nbsp;Koichi Koganezawa ,&nbsp;Azamat Yeshmukhametov","doi":"10.1016/j.robot.2025.105102","DOIUrl":"10.1016/j.robot.2025.105102","url":null,"abstract":"<div><div>Tensegrity structures are gaining attention due to their distinctive features that stem from wire-driven mechanisms and their highly redundant nature. These features include a lightweight framework, improved resistance to impacts, and ability to carry high payloads. Nonetheless, controlling these structures and understanding their movement remain complex challenges. Our research introduces a pioneering control strategy that utilizes some machine learning algorithms (linear regression, ridge regression, and neural network feedforward) to achieve inverse kinematics for prismatic tensegrity manipulators. This approach has been experimentally validated on two different structures, one with a triangular and the other with a quadrangular configuration, each forming a dual-layer setup. Our experimental results indicate that each of the presented algorithms facilitates the approximate inverse kinematics required for the control of the manipulators with average precision error of 2 cm.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"193 ","pages":"Article 105102"},"PeriodicalIF":4.3,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144335990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HEXmorph: Fault tolerance against single and dual rotor failure using geometric morphing on hexacopter","authors":"Jun Kiat Tan ,&nbsp;Archit Krishna Kamath ,&nbsp;Karanjot Singh,&nbsp;Mir Feroskhan","doi":"10.1016/j.robot.2025.105047","DOIUrl":"10.1016/j.robot.2025.105047","url":null,"abstract":"<div><div>Rotor failure in hexacopters with alternating rotor configurations often results in propulsion asymmetry and destabilizing moments, which can lead to loss of hover stability and maneuverability. This paper introduces HEXmorph, a novel fault-tolerant hexacopter design that employs geometric morphing through arm sweeping to redistribute thrust and counteract the effects of single and adjacent rotor failures. The proposed system integrates two optimization strategies: Moment Optimized Solver (MOS) and Center of Mass Optimized Solver (COS), tailored to minimize attitude changes and maintain stability during morphing. A feed-forward neural network is utilized to predict servo angles for arm morphing, ensuring real-time adaptability. The morphing mechanism is governed by a global event-triggered sliding mode control, which locks servo movements within a predefined error threshold. At the same time, system stability is guaranteed using a Modified Nonsingular Terminal Sliding Mode Controller (MNTSMC). Simulation and experimental results demonstrate the ability of HEXmorph to maintain near-zero attitude static hover and maneuverability, even under scenarios involving up to two adjacent rotor failures. By combining hardware adaptability with robust control strategies, HEXmorph significantly advances fault tolerance for multi-rotor aerial systems.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"193 ","pages":"Article 105047"},"PeriodicalIF":4.3,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A degree of flowability for virtual tubes","authors":"Quan Quan,&nbsp;Shuhan Huang,&nbsp;Kai-Yuan Cai","doi":"10.1016/j.robot.2025.105108","DOIUrl":"10.1016/j.robot.2025.105108","url":null,"abstract":"<div><div>With the rapid development of robotic swarm technology, there are more tasks that require the swarm to pass through complicated environments safely and efficiently. Virtual tube technology is a novel way to achieve this goal. A virtual tube is a free space that connects two places, providing safety boundaries and direction of motion for swarm robotics. How to determine the design quality of a virtual tube is a fundamental problem. For such a purpose, this paper presents a degree of flowability (DOF) for two-dimensional virtual tubes according to a minimum energy principle. After that, the method of calculating DOF is proposed with a feasibility analysis. Simulations of swarm robotics in different kinds of two-dimensional virtual tubes are performed to demonstrate the effectiveness of the proposed method of calculating DOF.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"193 ","pages":"Article 105108"},"PeriodicalIF":4.3,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Formal and scalable multi-robot coordination methods for long horizon tasks with time uncertainty","authors":"Carlos Azevedo ,&nbsp;Pedro U. Lima","doi":"10.1016/j.robot.2025.105103","DOIUrl":"10.1016/j.robot.2025.105103","url":null,"abstract":"<div><div>Many real-world robotic applications, such as monitoring, inspection, and surveillance tasks, require effective multi-robot coordination over extended time horizons. These scenarios benefit from long-term planning and execution, and the ability to handle time uncertainty a priori significantly enhances efficiency in unpredictable environments. In this work, we introduce and compare two approaches for synthesizing coordination policies for multi-robot systems that account for time uncertainty and optimize performance over an infinite horizon. Both approaches are based on reasoning over a generalized stochastic Petri net with rewards (GSPNR) model and optimize the average reward criterion. The first approach is an exact method that provides formal guarantees on the synthesized policies and ensures convergence to the optimal policy. We evaluate this method in a solar farm inspection scenario, comparing its performance to discounted reward optimization methods and a carefully designed hand-crafted policy. The results demonstrate that, over the long term, the exact method outperforms these alternatives. However, its scalability is limited, as it cannot handle large state spaces. To address this limitation, we propose a second approach that uses an actor-critic deep reinforcement learning algorithm. This method learns policies directly within the GSPNR formalism and optimizes for the average reward criterion. We assess its performance in the same solar farm inspection scenario, and the results show that it outperforms proximal policy optimization methods. Moreover, it is capable of finding near-optimal solutions in models with state spaces five orders of magnitude larger than those tractable by the exact method.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"193 ","pages":"Article 105103"},"PeriodicalIF":4.3,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fire front path planning and tracking control of Uncrewed Aerial Vehicles using deep reinforcement learning","authors":"Mona Raoufi ,&nbsp;Akbar Telikani ,&nbsp;Tieling Zhang ,&nbsp;Jun Shen","doi":"10.1016/j.robot.2025.105076","DOIUrl":"10.1016/j.robot.2025.105076","url":null,"abstract":"<div><div>This study develops a unified path planning and control framework based on reinforcement learning for Uncrewed Aerial Vehicles (UAVs) operating in dynamic wildfire environments. The Deep Deterministic Policy Gradient (DDPG) algorithm facilitates tracking fire evolution through a structured architecture comprising high-level planning and low-level control components. The path planner computes the linear velocity and refines the heading angle by incorporating the fire’s directional properties to generate the target trajectory. The low-level controller ensures stable trajectory tracking by adaptively tuning the control gains during the learning process. The closed-loop stability of the overall system is analytically validated using Lyapunov-based analysis. The framework is evaluated using the FARSITE fire area simulator, calibrated with real-world wildfire data. The simulation results demonstrate that the framework generates smooth planning variables, provides adaptive tracking, and remains robust against a range of external disturbances.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"193 ","pages":"Article 105076"},"PeriodicalIF":4.3,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient path planning for a dexterous arm–hand in complex environments","authors":"Wenpei Fan ,&nbsp;Yaonan Wang ,&nbsp;Wenrui Chen ,&nbsp;Licheng Liu ,&nbsp;Conghui Tang ,&nbsp;Xin Li ,&nbsp;Mingjie Dong","doi":"10.1016/j.robot.2025.105086","DOIUrl":"10.1016/j.robot.2025.105086","url":null,"abstract":"<div><div>Path planning represents a critical research direction for dexterous arm–hand (DAH) systems. However, path planning for high-degree-of-freedom manipulators presents the following challenges: (1) time-consuming collision detection, and (2) an expanded search space due to high-dimensional configurations, particularly in dynamic environments. In this paper, a new path planning strategy based on rapidly-exploring random tree (RRT) path is proposed for the DAH. Firstly, an adaptive step-size RRT (ADA-RRT*) algorithm is proposed to avoid the tunneling problem caused by discrete collision detection. Secondly, to improve the efficiency of the algorithm in high-dimensional spaces, a hierarchical planning framework is first introduced, consisting of coarse planning and fine planning. Coarse planning quickly finds a rough path with large steps without considering the tunneling problem, which then guides the fine planning. Then, the beetle antennae optimization algorithm and multi-objective optimization algorithm are used to optimize the global path, reducing path length and improving path safety. Finally, the execution of corresponding simulations and experiments demonstrates the effectiveness and efficiency of the proposed method.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"193 ","pages":"Article 105086"},"PeriodicalIF":4.3,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal contractor for triangle constraint","authors":"Algassimou Diallo ,&nbsp;Sebastien Lagrange ,&nbsp;Remy Guyonneau ,&nbsp;Daouda Niang Diatta ,&nbsp;Sebastien Lahaye","doi":"10.1016/j.robot.2025.105079","DOIUrl":"10.1016/j.robot.2025.105079","url":null,"abstract":"<div><div>This paper presents an optimal method for contracting a triangle Constraints Satisfaction Problem, composed of three distance constraints. Unlike traditional interval analysis methods, this approach takes into account all three constraints globally and focuses on the edges of the CSP boxes, rather than on their entirety, to compute the smallest boxes containing the CSP solution. It is based on constraint propagation, interval analysis tools and some topological properties of the boundary solution of the triangle CSP. This contractor can be applied in various contexts, and in particular to improve localization in robotics.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"193 ","pages":"Article 105079"},"PeriodicalIF":4.3,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}