Robotics and Autonomous Systems最新文献

{"title":"CRESTA: A Cognitivist Robot Execution framework for Semantic-driven Task Awareness","authors":"Damiano Gasperini ,&nbsp;Luca Muratore ,&nbsp;Nikos Tsagarakis","doi":"10.1016/j.robot.2025.105303","DOIUrl":"10.1016/j.robot.2025.105303","url":null,"abstract":"<div><div>The advancement of autonomous robots is still in need of a comprehensive framework for task execution awareness, enabling the generation of autonomous behaviors and responses in unknown dynamic environments. Situation awareness aims to enhance robot autonomy and adaptation during task execution by effectively combining key capabilities, such as reasoning, planning, projection of actions’ effects into future states, as well as perception and comprehension of the surroundings. In this work, we propose CRESTA, a novel cognitivist framework for semantic-driven task awareness that addresses the intricate challenges of perceiving, navigating, and manipulating dynamic environments. CRESTA’s objective of achieving effective robot awareness relies on the perceived environment semantics and on the combined use of online planning, reasoning, and monitoring, while also enabling recovery from task-level failures. It is designed as a set of online modules for (a) collecting and analyzing multi-sensor data as well as updating the world model description, (b) real-time decision-making and task states monitoring, and (c) execution of each action. Being highly modular and configurable to assorted robotic systems, the proposed framework aims for adaptability across diverse robotic platforms and tasks. In this work, a detailed description of CRESTA’s framework comes along with demonstrative tasks to showcase its capabilities on both the CENTAURO robot and on a custom 6 DoF manipulator. In the discussed experimental results, CRESTA leads the robot to open a door or to navigate and manipulate a lever, while recovering from failures by adapting the parameters of its actions.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"197 ","pages":"Article 105303"},"PeriodicalIF":5.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790308","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":"Passive Reinforcement Learning with Optimal Control for Safe Convergence in Cyber–physical Systems","authors":"Nicola Piccinelli ,&nbsp;Daniele Meli ,&nbsp;Enrico Bonoldi ,&nbsp;Riccardo Muradore","doi":"10.1016/j.robot.2025.105293","DOIUrl":"10.1016/j.robot.2025.105293","url":null,"abstract":"<div><div>Reinforcement Learning (RL) can compute optimal strategies for accomplishing difficult tasks in complex scenarios. However, most RL algorithms do not provide safety and performance guarantees during the deployment phase. This is a critical drawback when RL is applied to cyber–physical systems such as robotic manipulators, where the goal is to always and safely converge to a desired goal or equilibrium state. Specifically, one fundamental safety requirement for robotic systems is closed-loop <span><math><msub><mrow><mi>L</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span>-stability, which has <em>passivity</em> as a sufficient condition. This paper proposes a novel switched RL control scheme for robotic systems, with <em>passivity and asymptotic stability guarantees</em>. This combines RL over constrained Markov decision processes, for <em>passive training and inference</em>, with Linear Quadratic Regulation (LQR) for asymptotic convergence to the desired equilibrium point. During RL training, the energy stored in the system is monitored via the virtual energy tank approach to train a cost critic function. During inference, the virtual energy tank modulates the command input to guarantee passivity. Finally, the reward design of the RL agent is based on the Lyapunov function associated with LQR control, in order to steer the system state towards the LQR basin of attraction, where a switching mechanism is triggered to guarantee asymptotic convergence. We compare our methodology with a model-based controller and other RL and model-based architectures applied to a paradigmatic under-actuated cart–pole system, an instance of a 2-DOF robotic manipulator, both in simulation and on a real setup. We also test the generality of our approach, with an experiment on a 6-DOF manipulator in simulation. The experimental validation shows that our methodology performs better in training and inference, even in the presence of plant modelling errors, while guaranteeing passivity and safety in the presence of large disruptive disturbances.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"197 ","pages":"Article 105293"},"PeriodicalIF":5.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790831","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":"Beyond coverage path planning: Can UAV swarms perfect scattered regions inspections?","authors":"Socratis Gkelios ,&nbsp;Savvas D. Apostolidis ,&nbsp;Pavlos Ch. Kapoutsis ,&nbsp;Elias B. Kosmatopoulos ,&nbsp;Athanasios Ch. Kapoutsis","doi":"10.1016/j.robot.2025.105297","DOIUrl":"10.1016/j.robot.2025.105297","url":null,"abstract":"<div><div>Unmanned Aerial Vehicles (UAVs) have revolutionized inspection tasks by offering a safer, more efficient, and flexible alternative to traditional methods. However, battery limitations often constrain their effectiveness, necessitating the development of optimized flight paths and data collection techniques. While existing approaches like coverage path planning (CPP) ensure comprehensive data collection, they can be inefficient, especially when inspecting multiple non-connected Regions of Interest (ROIs). This paper introduces the Fast Inspection of Scattered Regions (FISR) problem and proposes a novel solution, the multi-UAV Disjoint Areas Inspection (mUDAI) method. The introduced approach implements a two-fold optimization procedure, for calculating the best image capturing positions and the most efficient UAV trajectories, balancing data resolution and operational time, minimizing redundant data collection and resource consumption. The mUDAI method is designed to enable rapid, efficient inspections of scattered ROIs, making it ideal for applications such as security infrastructure assessments, agricultural inspections, and emergency site evaluations. A combination of simulated evaluations and real-world deployments is used to validate and quantify the method’s ability to improve operational efficiency while preserving high-quality data capture, demonstrating its effectiveness in real-world operations. An open-source Python implementation of the mUDAI method can be found on GitHub<span><span>¹</span></span> and the collected and processed data from the real-world experiments are all hosted on Zenodo<span><span>²</span></span>. Finally, this on-line platform<span><span>³</span></span> allows the interested readers to interact with the mUDAI method and generate their own multi-UAV FISR missions.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"197 ","pages":"Article 105297"},"PeriodicalIF":5.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790832","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":"Multi-goal path planning for robot-aided transcranial magnetic stimulation","authors":"Lixia Wang ,&nbsp;Qing Tang ,&nbsp;Haoyang Xing ,&nbsp;Jiarui Dong","doi":"10.1016/j.robot.2025.105294","DOIUrl":"10.1016/j.robot.2025.105294","url":null,"abstract":"<div><div>Since the proposal of transcranial magnetic stimulation (TMS), it has found extensive applications in the treatment of brain disorders and other fields. Robot-assisted TMS therapy ensures the accuracy and durability of treatment positioning. However, the use of robot-assisted therapy has also raised potential collisions and path-planning challenges. Due to the redundant degree of freedom (DOF), the robot has infinite configurations while performing each given stimulation. The problem that involves visiting multiple target points and returning to the initial position in TMS therapy is a combination of a generalized traveling salesman problem with neighborhoods (GTSPN) and a collision-free path planning problem. A global method based on the probabilistic roadmap (PRM) and the A* algorithm is proposed. To safely visit target points, the proposed method samples uniformly on a sphere. A special nearest neighbor definition and delayed collision queries for edges are introduced to accelerate the roadmap construction. Then, the proposed method utilizes a modified A* algorithm with a multi-goal heuristic to rapidly search for a global path through all target points. Finally, a strategy combining local re-search and global re-search is proposed to get the final collision-free path. Experiments are conducted on both simulation and physical platforms using a typical model of the TMS therapy system. The results indicate that our proposed algorithm can effectively avoid collisions and produce optimal planning results for TMS therapy in a short time.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"197 ","pages":"Article 105294"},"PeriodicalIF":5.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790834","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":"SR-SLAM: Scene reliability-based RGB-D SLAM in diverse environments","authors":"Haolan Zhang ,&nbsp;Chenghao Li ,&nbsp;Thanh Nguyen Canh ,&nbsp;Lijun Wang ,&nbsp;Nak Young Chong","doi":"10.1016/j.robot.2025.105306","DOIUrl":"10.1016/j.robot.2025.105306","url":null,"abstract":"<div><div>Visual simultaneous localization and mapping (SLAM) plays a critical role in autonomous robotic systems, especially where accurate and reliable measurements are essential for navigation and sensing. In feature-based SLAM, the quantity and quality of extracted features significantly influence system performance. Due to the variations in feature quantity and quality across diverse environments, current approaches face two major challenges: (1) limited adaptability in dynamic feature culling and pose estimation, and (2) insufficient environmental awareness in assessment and optimization strategies. To address these issues, we propose SR-SLAM, a scene reliability-based framework that enhances feature-based SLAM through environment-aware processing. Our method introduces a unified scene reliability assessment mechanism that incorporates multiple metrics and historical observations to guide system behavior. Based on this assessment, we develop: (i) adaptive dynamic region selection with flexible geometric constraints, (ii) depth-assisted self-adjusting clustering for efficient dynamic feature removal in high-dimensional settings, and (iii) reliability-aware pose refinement that dynamically integrates direct methods when features are insufficient. Furthermore, we propose (iv) reliability-based keyframe selection and a weighted optimization scheme to reduce computational overhead while improving estimation accuracy. Extensive experiments on public datasets and real-world scenarios show that SR-SLAM outperforms state-of-the-art dynamic SLAM methods, achieving up to 90% improvement in accuracy and robustness across diverse environments. These improvements directly contribute to enhanced measurement precision and reliability in autonomous robotic sensing systems.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"197 ","pages":"Article 105306"},"PeriodicalIF":5.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840373","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":"Real-time algorithm for table tennis with a desktop robotic arm","authors":"Baptiste Toussaint,&nbsp;Maxime Raison","doi":"10.1016/j.robot.2025.105288","DOIUrl":"10.1016/j.robot.2025.105288","url":null,"abstract":"<div><div>Table tennis with collaborative robots has been a challenge in robotics for decades, due to its unique challenges, especially high-speed movements and real-time ball trajectory predictions for responsive and accurate gameplay. Over the years, several table tennis robots have been developed, showing progressively enhanced abilities for returning balls, hitting specific targets, rallying with collaborative human users, and playing amateur-level games. However, these robotic systems remain costly for individuals, often relying on industrial components, or specialized designs. Emerging AI-integrated personal desktop robotic arms could help bridge the performance gap between affordable personal robotic systems and traditional industrial robots, particularly in terms of dexterity, speed, and precision. Despite this potential, desktop robotic arms have not yet been used for table tennis. However, existing table tennis algorithms require specific adaptations to accommodate the constraints of desktop robots. This paper aims to develop a dedicated algorithm for a collaborative table tennis system using a desktop robotic arm to demonstrate the achievable performance of AI-integrated desktop robots. The proposed system utilizes a 5-degree-of-freedom (DOF) serial robot, integrating advanced algorithms and machine learning models to improve performance. This system enables short collaborative rallies, returning 71.3% of balls overall, improving to 81.4% after fine-tuning system parameters — approaching the best one from the literature (88%) using a 7-DOF industrial robotic arm. This underscores the potential of affordable, AI-integrated desktop robotic arms for high-speed human–robot collaboration. Future works will focus on adapting the algorithm for specialized desktop hardware, expanding desktop robots to other applications, and further enhancing their performance.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"197 ","pages":"Article 105288"},"PeriodicalIF":5.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738058","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":"Decentralized formation control, collision avoidance and global connectivity maintenance using non-smooth barrier functions","authors":"Pranjal Bhatia ,&nbsp;Sayan Basu Roy ,&nbsp;P.B. Sujit ,&nbsp;Luis Mejias Alvarez ,&nbsp;Aaron McFadyen","doi":"10.1016/j.robot.2025.105272","DOIUrl":"10.1016/j.robot.2025.105272","url":null,"abstract":"<div><div>This paper presents a decentralized control framework for multi-agent systems that simultaneously addresses formation control, global connectivity maintenance, and collision avoidance. To achieve these multiple objectives, we employ control barrier functions (CBFs), with each function tailored to a specific safety constraint. However, the combination of multiple CBFs inherently introduces non-smoothness in the system, posing challenges for real-time control. To address this, we adopt non-smooth barrier functions (NBFs), which enable the integration of multiple safety constraints in a decentralized manner while systematically avoiding critical non-smooth regions. The proposed method allows agents to maintain a desired formation, avoid inter-agent and obstacle collisions, and preserve global connectivity without requiring centralized coordination. The results are validated using mathematical analysis and simulations.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"197 ","pages":"Article 105272"},"PeriodicalIF":5.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685199","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":"AUV decision-making in bistatic sonar target tracking via deep reinforcement learning","authors":"Weicong Zhan ,&nbsp;Yu Tian ,&nbsp;Feng Zheng ,&nbsp;Qiming Sang ,&nbsp;Jiancheng Yu","doi":"10.1016/j.robot.2025.105262","DOIUrl":"10.1016/j.robot.2025.105262","url":null,"abstract":"<div><div>The maneuvering decisions of an Autonomous Underwater Vehicle (AUV) equipped with a sonar receiver are critical for acquiring informative measurements, thereby enhancing target tracking accuracy and maintaining persistent track continuity. This study investigates a bistatic sonar configuration comprising a stationary acoustic source and a mobile AUV carrying a linear sonar array, with a focus on real-time generation of optimized heading commands for the AUV. To address the practical limitations of conventional model-based strategies under stringent decision-time constraints, a deep reinforcement learning (DRL) framework is employed to derive an efficient decision policy. Implemented using the Proximal Policy Optimization algorithm, the DRL policy integrates with a target tracker by utilizing estimated target states and associated uncertainties as inputs. A reward function based on the Optimal Sub-Pattern Assignment metric is designed to guide policy learning. To train the policy, a comprehensive simulation environment is developed, incorporating target properties, acoustic propagation characteristics, and bistatic measurement models. Once trained, the DRL-based policy effectively guides the AUV in real-time to establish favorable bistatic configurations and enhance tracking performance. Comparative Monte Carlo simulations against a state-of-the-art model-based receding horizon control method demonstrate that the DRL-based approach yields a 14.5 % average improvement in tracking accuracy. Detailed analyses of AUV behaviors under the learned policy, along with robustness and generalization evaluations, underscore the practical potential of the proposed approach for real-world deployment in bistatic sonar target tracking scenarios.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"197 ","pages":"Article 105262"},"PeriodicalIF":5.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145646035","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":"Multi-objective optimization for dimensional synthesis of tendon placement and structural design for energy-efficient and feasible static workspace in continuum robots","authors":"Mohammad Jabari ,&nbsp;Carmen Visconte ,&nbsp;Giuseppe Quaglia ,&nbsp;Med Amine Laribi","doi":"10.1016/j.robot.2025.105300","DOIUrl":"10.1016/j.robot.2025.105300","url":null,"abstract":"<div><div>Tendon-driven continuum robots (TDCRs) face a critical trade-off between energy efficiency and static performance for navigating constrained environments, a challenge in medical and industrial applications. This study proposes a bi-objective optimization framework to enhance tendon placement and dimensional synthesis in a two-segment TDCR, featuring seven disks and four tendons per segment. Leveraging a kineto-static model based on piecewise constant curvature (PCC) theory and a multi-objective genetic algorithm (MOGA), radial tendon distances and angular offsets have been optimized. These solutions achieve up to 30 % reduction in mechanical work and a 3–5 % workspace expansion, validated through 100 randomized tendon force samples. The results offer practical guidelines for improving TDCR performance in both minimally invasive surgery and industrial inspection.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"197 ","pages":"Article 105300"},"PeriodicalIF":5.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738064","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":"The FriûlBot dataset: Experimental validation of an autonomous ground robot for vineyard 3D mapping","authors":"Diego Tiozzo Fasiolo,&nbsp;Lorenzo Scalera,&nbsp;Eleonora Maset,&nbsp;Alessandro Gasparetto","doi":"10.1016/j.robot.2025.105283","DOIUrl":"10.1016/j.robot.2025.105283","url":null,"abstract":"<div><div>In this paper, we present the FriûlBot open-source dataset, collected by an autonomous mobile robot for vineyard 3D mapping and monitoring. The dataset is designed to provide a reference testbed for the development and benchmarking of localization, mapping, and multi-sensor data fusion algorithms. It includes detailed information on the robot status, point clouds of the vineyard, and multispectral images, offering valuable resources for future research on autonomous robotic systems in agriculture. The mobile robot employed for the dataset acquisition is capable of autonomously navigating, reaching GNSS way points, and building a map of the canopy integrating geometric and multispectral data. The navigation and mapping approach proposed in this work is based on a SLAM algorithm that integrates multiple odometry sources, and is designed for agricultural environments with plants arranged in rows, e.g., vineyards and orchards. The performance of the proposed mobile robot and navigation approach are tested during an extensive experimental campaign in a vineyard of University of Udine (Italy). During the tests, the robot successfully navigates along several vineyard rows, building point clouds of the environment that are merged with data regarding multiple vegetation indexes. The experimental results confirm the reliability of the autonomous mobile robot and the potential of the proposed dataset to foster further advances in robotics for vineyard 3D mapping.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"197 ","pages":"Article 105283"},"PeriodicalIF":5.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738115","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}