Robotics and Autonomous Systems最新文献

{"title":"A survey on theories and applications for multi-robot cooperative hunting","authors":"Jianjun Ni ,&nbsp;Yonghao Zhao ,&nbsp;Ziru Zhang ,&nbsp;Chunyan Ke ,&nbsp;Simon X. Yang","doi":"10.1016/j.robot.2025.105296","DOIUrl":"10.1016/j.robot.2025.105296","url":null,"abstract":"<div><div>Multi-robot cooperative hunting problem has gained widespread attention due to its potential applications in fields such as surveillance, search and rescue, and military. Recently, many excellent solutions have been proposed in this field. Thus, this paper provides a comprehensive review of the latest advancements and challenges in the field of multi-robot cooperative hunting. This survey provides an overview of the key elements in this field, such as robot types, team compositions, system architectures, communication mechanisms, cooperative modes, and so on. In addition, the key issues in multi-robot cooperative hunting are analyzed, including perception and environmental understanding, task allocation, formation control, path planning, trajectory tracking, and decision-making. Then, some representative theories and methods for these issues are summarized. Finally, some potential solutions are discussed and future research directions in this field are presented.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"197 ","pages":"Article 105296"},"PeriodicalIF":5.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737593","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 hierarchical planning framework for self-assembly and self-reconfiguration of autonomous modular space robots","authors":"Zhen Huang ,&nbsp;Yajie Cheng ,&nbsp;Rongqiang Liu ,&nbsp;Minghe Shan","doi":"10.1016/j.robot.2025.105273","DOIUrl":"10.1016/j.robot.2025.105273","url":null,"abstract":"<div><div>Owing to the extreme conditions of the space environment and the difficulty of external intervention, autonomous modular robotic systems offer a viable solution for constructing structures in space through self-assembly and self-reconfiguration. This paper presents a comprehensive planning system designed for modular robot self-assembly and self-reconfiguration tasks in space. The proposed system consists of two hierarchical layers: a sequential path planning layer and a task and trajectory planning layer. At the sequential path planning layer, the self-assembly and self-reconfiguration tasks are formulated as time-varying online Multi-Agent Path Finding (MAPF) problems, and are effectively solved using the enhanced Time-Expanded Network (TEN) and Minimum-Cost Maximum-Flow (MCMF) algorithm to generate collision-free and efficient sequential paths. At the task and trajectory planning layer, robot tasks are automatically partitioned based on the connection status and motion characteristics, with corresponding dynamic models generated adaptively according to the current task state. A hybrid iterative Linear Quadratic Regulator (iLQR) algorithm is introduced to achieve rapid response and trajectory optimality while satisfying constraints on the joint angles, actuator torques, and interface forces. Simulations using the HexaFlipBot modular robot platform confirmed that the proposed planning approach can efficiently and accurately complete the construction and reconfiguration of typical structures in space.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"197 ","pages":"Article 105273"},"PeriodicalIF":5.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685203","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 generic task model and control strategy to support learning, robust control, and generalization of contact-rich manipulation tasks","authors":"Ali Mousavi Mohammadi,&nbsp;Maxim Vochten,&nbsp;Erwin Aertbeliën,&nbsp;Joris De Schutter","doi":"10.1016/j.robot.2025.105270","DOIUrl":"10.1016/j.robot.2025.105270","url":null,"abstract":"<div><div>Learning and controlling contact-rich tasks are based on a task model that is built from motion and wrench data collected from demonstration. In previous work, the procedure for choosing a proper reference frame to express the learned signals is often implicit, requires expert insight, or starts from proposed frame candidates. Furthermore, the exact location of the geometric features in the world or tool that determine this frame may be uncertain during task demonstration or execution. This article presents an automatic method to derive the optimal reference frame for the task model directly from recorded motion and wrench data. This frame maximizes decoupling between the learned signals, enabling the design of a decoupled task controller. The method is founded on screw theory, entirely probabilistic and requires no hyperparameters. The method is generic as it works regardless of whether the task involves translation, rotation, force, or moment, or any combination thereof. While other control approaches could be used, we propose a generic, decoupled, constraint-based and velocity-resolved controller to work in tandem with the derived task model. It involves a small number of task-dependent control parameters that need to be tuned. The derivation of the task model, the robustness of the controller, and the task generalization capabilities of their combination were validated for various tasks involving contact between rigid objects, including surface following and manipulation of articulated objects. The results show a good agreement between derived and assumed expert task frames as well as the effectiveness and versatility of the overall approach.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"197 ","pages":"Article 105270"},"PeriodicalIF":5.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685204","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":"Deterministic delay-aware reinforcement learning","authors":"Sathira Dilshan Bataduwaarachchi ,&nbsp;Zoran Najdovski ,&nbsp;Hieu Trinh ,&nbsp;Chee Peng Lim ,&nbsp;Van Thanh Huynh","doi":"10.1016/j.robot.2025.105271","DOIUrl":"10.1016/j.robot.2025.105271","url":null,"abstract":"<div><div>Reinforcement Learning (RL) has effectively paved the way in achieving robotic control during the past decade. As a result, the avenue of integrating RL-powered robotic control and teleoperation has caught the attention of researchers. Every RL framework involves the basis of suitable observation and action communication between the environment and the agent, and the involvement of teleoperation can introduce random time delays within the said communication process. Achieving robotic control under such constraints remains an untapped area in the domain of reinforcement learning. We take the initiative to achieve the goal of robotic control while handling delays in the RL setting based on a fitting Markov Decision Process (MDP) structure. Our algorithm will learn a deterministic policy and can tackle control environments, especially robotic manipulation environments, using observations with proprioceptive information. We methodically present the theoretical adjustments based on an existing dominant off-policy algorithm to express the algorithm’s competency with proof of convergence. We perform experimentations with DeepMind Control Suite, illustrating significant results showing the algorithm’s capabilities in learning complex environments powered by delay-aware RL.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"197 ","pages":"Article 105271"},"PeriodicalIF":5.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738062","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 Multi-UAV unfixed scale task assignment method with uncertainty based on deep Q network","authors":"Wenqi Ding,&nbsp;Jia Song,&nbsp;Jingwei Yu","doi":"10.1016/j.robot.2025.105269","DOIUrl":"10.1016/j.robot.2025.105269","url":null,"abstract":"<div><div>Task assignment is a fundamental aspect in optimizing the efficiency of multiple unmanned aerial vehicle (Multi-UAV) systems. This paper presents a data-driven approach for addressing the Multi-UAV unfixed scale task assignment problem (MUSTA) in the presence of uncertain conditions. Firstly, we propose a task allocation model that incorporates uncertainty, thereby simulating the penetration process that may result in losses for unmanned aerial vehicles, and comprehensively evaluating the algorithm’s effectiveness. Secondly, we refine the task allocation strategy in network processing to effectively handle scenarios with variable-scale configurations of both UAVs and targets. By devising appropriate allocation rules, a Q-network can be trained to approximate allocation strategies suitable for diverse UAV-task scales, eliminating the need for network structure reinitialization and training for variable scale problems. Leveraging the characteristics of the allocation problem under study, we design a state–action space and a reward function, and simulation results confirm that this approach not only efficiently provides relatively optimal solutions for different large-scale problems but also acquires allocation strategies that closely resemble real-world application scenarios through training in uncertain environments.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"197 ","pages":"Article 105269"},"PeriodicalIF":5.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738060","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":"Adaptive path planning method for multi-AUVs in complex environments","authors":"Jianhao Wu ,&nbsp;Chang Liu ,&nbsp;Vladimir Filaretov ,&nbsp;Dmitry Yukhimets","doi":"10.1016/j.robot.2025.105290","DOIUrl":"10.1016/j.robot.2025.105290","url":null,"abstract":"<div><div>In modern marine scientific exploration, environmental protection, and resource investigation missions, autonomous underwater vehicles (AUV) have seen increasingly widespread applications. However, complex underwater environments present challenges such as multi-objective optimization conflicts and communication constraints, particularly in scenarios with high-density obstacles and dynamic ocean current disturbances. This paper proposes a Distributed Learning-Enhanced Double Deep Q-Network (DLE-DDQN) algorithm for adaptive path planning, integrating deep reinforcement learning with distributed collaborative optimization. The algorithm employs an improved Double Deep Q-Network (DDQN) architecture and a dynamically adaptive reward function. These components guide AUVs in path planning within unknown environments. Meanwhile, distributed learning mechanisms are leveraged to enhance training efficiency under limited communication bandwidth. Experimental results demonstrate that DLE-DDQN achieves an 18.7% improvement in task success rate, a 29.6% increase in path efficiency, and a 51.4% reduction in response time compared to traditional DDQN and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) methods in high-obstacle-density scenarios. Through a cross-attention mechanism that fuses environmental perception with navigational states, the algorithm maintains path curvature stability with a standard deviation below 0.15 rad/m even under abrupt current disturbances. These results validate the algorithm’s robustness and real-time performance in unstructured dynamic environments, confirming its capability to overcome both static obstacles and current interference for rapid, precise path planning.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"197 ","pages":"Article 105290"},"PeriodicalIF":5.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737592","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 sufficient condition for feature agnostic video stabilization for surveillance robots: A state space approach","authors":"Adwaith Vijayakumar,&nbsp;Abhishek Gupta,&nbsp;Leena Vachhani","doi":"10.1016/j.robot.2025.105275","DOIUrl":"10.1016/j.robot.2025.105275","url":null,"abstract":"<div><div>A camera is a ubiquitous sensor in surveillance robots. Often the camera is subjected to unintended motion from either external factors or inherent dynamics. The intended camera motion induces constant flow vectors for short segments of a video sequence. Video stabilization is the removal of the effect of unintended camera motion from a video sequence. The surveillance robots are often made to operate indoors, where features are scarce for motion estimation rendering traditional video stabilization methods unreliable. Unlike feature/motion extraction methods, investigation in this work is to explore the possibility of stabilizing video while remaining agnostic to the scene. One such possibility is stabilizing the video by manipulating the coefficients of a basis of the signal subspace, the smallest subspace containing the video. We show a sufficient condition for which stabilization can be performed in the signal subspace of an unstabilized video. The sufficient condition is derived by developing a novel state-space model that describes a short-length video sequence as a discrete-time state-space model. The properties of matrices corresponding to the state model are analyzed, and the corresponding results are utilized in deriving the sufficient condition. Simulation videos and experimental videos collected from a UAV validate the proposed sufficient condition.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"197 ","pages":"Article 105275"},"PeriodicalIF":5.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685197","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":"Prior-Backbone-Prediction cascade depth completion framework for robotic grasp oriented to transparent objects","authors":"Hongkun Tian ,&nbsp;Shuo Liu ,&nbsp;Shuai Liu ,&nbsp;Jun Li ,&nbsp;Ling Tong ,&nbsp;Yi Man","doi":"10.1016/j.robot.2025.105268","DOIUrl":"10.1016/j.robot.2025.105268","url":null,"abstract":"<div><div>Transparent objects are frequent manipulation objects for industrial production and manufacturing systems. The texture-less characteristics of transparent objects lead to insignificant RGB image features and incomplete and inaccurate Depth images, which significantly challenge robotic grasping detection and potentially dangerous for independent and safe manipulation in situations such as industrial production and manufacturing system. Reconstruction of depth data by fusing RGB-Depth (RGB-D) data through depth completion is the mainstream solution as a pre-processing robot-acquired data. Therefore, the quality, as well as the efficiency of depth completion, determines the effectiveness of the robot grasping transparent objects. However, the no-prior processing used in existing studies leads to an unimpressive depth completion. In addition, existing studies employ one-way prediction results and do not address the issue of image distortion. In general, current studies are not ideal in balancing accuracy and efficiency. To this end, we propose a Prior-Backbone-Prediction (PBP) cascade lightweight depth completion framework, named PBP framework. <strong>Prior:</strong> the proposed Reverse Texture Prior Guidance strategy guides the backbone network to focus on texture-less regions better. <strong>Backbone:</strong> the proposed Pyramid Interactive Residual Backbone network is used for in-depth feature learning. <strong>Prediction:</strong> the proposed consistency-difference Joint Prediction strategy maximizes the accuracy of prediction results and reduces distortion. The dual validation is performed on both public datasets and robotic platforms. PBP achieves state-of-the-art performance on the well-known TransCG dataset with a model size of only 17.2 M. The success rate of the Aubo i5 robot platform is improved by 44 % based on GG-CNN to grasp transparent objects.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"197 ","pages":"Article 105268"},"PeriodicalIF":5.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685201","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":"Physics-informed LSTM-based delay compensation framework for high-fidelity teleoperation of wheeled mobile robots","authors":"Ahmad Abubakar ,&nbsp;Yahya Zweiri ,&nbsp;Abdel Gafoor Haddad ,&nbsp;Mubarak Yakubu ,&nbsp;Ruqayya Alhammadi ,&nbsp;Lakmal Seneviratne","doi":"10.1016/j.robot.2025.105265","DOIUrl":"10.1016/j.robot.2025.105265","url":null,"abstract":"<div><div>Bilateral teleoperation of wheeled mobile robots (WMRs) traversing soft terrains with interaction force feedback is crucial for applications like lunar exploration, as it help regulates travel reduction and path deviation. However, network-induced communication delays present a challenge in achieving high-fidelity closed-loop integration, degrading slippage awareness and command-tracking performance. To address this, a physics-informed long short-term memory (PiLSTM)-based predictor framework is proposed to predicts intended commands and anticipated slippage feedback in WMR teleoperation systems under large delays. Conventional model-free predictors often demonstrate significant prediction errors when compensating for large delays in complex dynamics systems due to their limited adaptability and generalizability. In contrast, the proposed PiLSTM predictor integrates an LSTM network with time-delay dynamics and a feedback mechanism to better capture strong nonlinear and temporal dynamics for improved adaptability and generalizability while ensuring accurate prediction. A series of human-in-the-loop experiments for a track-following task were conducted to validate the effectiveness of the PiLSTM framework in compensating for the large delays, similar to those encountered in lunar exploration. Its performance was compared against first-order time-delay (FOD) and wave variable transformation (WV) approaches for delay compensation. The results demonstrate the robustness of the PiLSTM framework across various test scenarios, achieving up to 29.7% improvement in prediction accuracy compared to the FOD framework. Furthermore, the proposed PiLSTM-based teleoperation system achieved significantly improved tracking performance and force transparency compared to FOD- and WV-based systems, ensuring higher fidelity and more robust closed-loop integration. These findings highlight the PiLSTM framework’s adaptability and generalizability for complex teleoperation systems. A supplementary video is available at <span><span>https://youtu.be/dMVLEV7qTvE</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"197 ","pages":"Article 105265"},"PeriodicalIF":5.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685202","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":"Hybrid and trustworthy AI approach to anomaly detection in realistic human–robot collaboration scenarios","authors":"Rafał Kozik ,&nbsp;Szymon Buś ,&nbsp;Jakub Główka ,&nbsp;Aleksandra Pawlicka ,&nbsp;Marek Pawlicki ,&nbsp;Rafał Renk ,&nbsp;Michał Choraś","doi":"10.1016/j.robot.2025.105286","DOIUrl":"10.1016/j.robot.2025.105286","url":null,"abstract":"<div><div>DNN (Deep Neural Networks) have recently demonstrated superior performance in various difficult and challenging tasks, such as natural language processing (NLP) and computer vision (CV). However, the researchers often stress the need for more practical cognitive and reasoning abilities of ML systems. In contrast to DNN, symbolic machine learning demonstrates exceptional reasoning capabilities. It is an obvious and beneficial approach to hybridize these two research domains to merge the advantages of both methodologies in order to achieve neuro-symbolic learning systems that exhibit effective perception and reasoning capabilities. In this paper, the authors propose a neuro-symbolic approach for anomaly detection in a real human–robot collaboration scenario. The emphasis in our research is put on constrained and industrial environments where the so-called cobots assist humans in the assembly process. The proposed approach encompasses several symbolic programs which allow enforcing safety guidelines in the human–robot collaboration as well as make the neural system aware of the physical and environmental constraints. The experiments and the ablation studies show each symbolic program plays an important role and contributes to improved anomaly detection capabilities.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"197 ","pages":"Article 105286"},"PeriodicalIF":5.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738061","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}