Robotics and Autonomous Systems最新文献

{"title":"Online optimization enhanced closed-loop control of multi-section continuum robots","authors":"Laihao Yang ,&nbsp;Yi Zheng ,&nbsp;Yu Sun,&nbsp;Xuefeng Chen","doi":"10.1016/j.robot.2025.104986","DOIUrl":"10.1016/j.robot.2025.104986","url":null,"abstract":"<div><div>Due to the inherent characteristics of continuum robots (high flexibility, multiple degrees of freedom), controlling the continuum robots safely and precisely in practical applications has always been a challenging task. In this paper,a real-time kinematic closed-loop controller that optimizes the step length to boost control performance is proposed. Initially, a differential-based generalized inverse kinematics solution is formulated to resolve the DOF coupling in twin-pivot continuum robots that intertwined two DOFs in one joint. Subsequently, an adaptive online optimization strategy utilizing the algorithm of Particle Swarm Optimization (PSO) is proposed to refine the controller, overcoming the limitations of traditional Jacobian-based approaches. This novel method innovatively decouples control direction and step length, optimizing safety and efficiency. Comparative simulations and tracking tests confirm the controller's superior precision and efficiency, with an average accuracy of 0.33 %, a 35 % enhancement over the Jacobian controller, thus facilitating the broader application of multi-section continuum robots.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"189 ","pages":"Article 104986"},"PeriodicalIF":4.3,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680836","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 combined learning and optimization framework to transfer human whole-body loco-manipulation skills to mobile manipulators","authors":"Jianzhuang Zhao ,&nbsp;Francesco Tassi ,&nbsp;Yanlong Huang ,&nbsp;Elena De Momi ,&nbsp;Arash Ajoudani","doi":"10.1016/j.robot.2025.104958","DOIUrl":"10.1016/j.robot.2025.104958","url":null,"abstract":"<div><div>Humans’ ability to smoothly switch between locomotion and manipulation is a remarkable feature of sensorimotor coordination. Learning and replicating such human-like strategies can lead to the development of more sophisticated robots capable of performing complex whole-body tasks in real-world environments. To this end, this paper proposes a combined learning and optimization framework for transferring human loco-manipulation soft-switching skills to mobile manipulators. The methodology starts with data collection of human demonstrations for locomotion-integrated manipulation tasks through a vision system. Next, the wrist and pelvis motions are mapped to the mobile manipulators’ End-Effector (EE) and mobile base. A kernelized movement primitive algorithm learns the wrist and pelvis trajectories and generalizes them to new desired points according to task requirements. Then, the reference trajectories are sent to a hierarchical quadratic programming controller, where the EE and the mobile base reference trajectories are provided as the first and second priority tasks, respectively, generating the feasible and optimal joint level commands. Locomotion-integrated pick-and-place and door opening tasks have been chosen to validate the proposed approach. After a human demonstrates the two tasks, a mobile manipulator executes them with the same and new settings. The results showed that the proposed approach successfully transfers and generalizes the human loco-manipulation skills to mobile manipulators, even with different geometry.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"189 ","pages":"Article 104958"},"PeriodicalIF":4.3,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A feasibility-driven MPC scheme for robust gait generation in humanoids","authors":"Nicola Scianca ,&nbsp;Filippo M. Smaldone,&nbsp;Leonardo Lanari,&nbsp;Giuseppe Oriolo","doi":"10.1016/j.robot.2025.104957","DOIUrl":"10.1016/j.robot.2025.104957","url":null,"abstract":"<div><div>We present a Robust Intrinsically Stable Model Predictive Control (RIS-MPC) framework for humanoid gait generation, which realizes as closely as possible a predefined sequence of footsteps in the presence of both persistent and impulsive perturbations. The MPC-based controller has two modes of operations, each involving a Quadratic Program. Since perturbations act by modifying the state, as well as the feasibility region itself, the fundamental idea is to select in real time the operation mode based on the feasibility properties of the current state. In <em>standard mode</em>, footsteps are regarded as fixed and the MPC computes a Center of Mass (CoM) and a Zero Moment Point (ZMP) trajectory. Robustness is ensured by a robust stability constraint which uses a disturbance estimate and by restricted ZMP constraints along the control horizon. In the presence of strong perturbations, that violate the aforementioned conditions, the system switches to <em>recovery mode</em>, in which footsteps positions and timings can be modified in order to recover feasibility. We analyze the feasibility of both modes of operation and provide conditions for recursive feasibility of the standard mode. Simulations on an HRP-4 robot as well as experiments on NAO and OP3 are provided to validate the scheme.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"189 ","pages":"Article 104957"},"PeriodicalIF":4.3,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive bézier curve-based path following control for autonomous driving robots","authors":"Li An ,&nbsp;Xiuwei Huang ,&nbsp;Peng Yang ,&nbsp;Zhen Liu","doi":"10.1016/j.robot.2025.104969","DOIUrl":"10.1016/j.robot.2025.104969","url":null,"abstract":"<div><div>This article presents a concise and efficient path-following strategy, along with a set of real robot experiments to evaluate its superior performance. The following trajectory is generated in the form of a quartic Bézier curve with an adaptive control point generation method based on the integral length and curvature of the reference path. An impressive merit is that the cutting-corner problem during sharp turns can be avoided and smooth speed regulation can be achieved automatically. Another advantage is that the robot can quickly return to the reference path from a large lateral position or heading deviation, without any large space requirement for adjustment. The first few commands derived from the differentiation of the following trajectory are utilized. Simulation results show that the proposed method has a higher accuracy <strong>under the same-level computation time compared with other simple geometric methods</strong>. Real-world robot experiments are conducted in various environments to verify the proposed algorithm's accuracy, robustness, and flexibility. The average path-following error of real-world experiments is under 0.1 m, even with sudden path changing for obstacle avoidance. Additionally, with the proposed algorithm, the robot can navigate safely in a residential community where frequent pedestrian incursions occur.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"189 ","pages":"Article 104969"},"PeriodicalIF":4.3,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620310","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 task and motion planning framework using iteratively deepened AND/OR graph networks","authors":"Hossein Karami ,&nbsp;Antony Thomas ,&nbsp;Fulvio Mastrogiovanni","doi":"10.1016/j.robot.2025.104943","DOIUrl":"10.1016/j.robot.2025.104943","url":null,"abstract":"<div><div>In this paper, we present an approach for integrated task and motion planning based on an AND/OR graph network, which is used to represent task-level states and actions, and we leverage it to implement different classes of task and motion planning problems (TAMP). Several problems that fall under task and motion planning do not have a predetermined number of sub-tasks to achieve a goal. For example, while retrieving a target object from a cluttered workspace, in principle the number of object re-arrangements required to finally grasp it cannot be known ahead of time. To address this challenge, and in contrast to traditional planners, also those based on AND/OR graphs, we grow the AND/OR graph at run-time by progressively adding sub-graphs until grasping the target object becomes feasible, which yields a network of AND/OR graphs. The approach is extended to enable multi-robot task and motion planning, and (i) it allows us to perform task allocation while coordinating the activity of a <em>given</em> number of robots, and (ii) can handle multi-robot tasks involving an <em>a priori</em> unknown number of sub-tasks.</div><div>The approach is evaluated and validated both in simulation and with a real dual-arm robot manipulator, that is, Baxter from Rethink Robotics. In particular, for the single-robot task and motion planning, we validated our approach in three different TAMP domains. Furthermore, we also use three different robots for simulation, namely, Baxter, Franka Emika Panda manipulators, and a PR2 robot. Experiments show that our approach can be readily scaled to scenarios with many objects and robots, and is capable of handling different classes of TAMP problems.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"189 ","pages":"Article 104943"},"PeriodicalIF":4.3,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143580443","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":"Reinforcement learning-based optimal formation control of multiple robotic rollers in cooperative rolling compaction","authors":"Yong-Hang Wei ,&nbsp;Jun-Wei Wang ,&nbsp;Qinglong Zhang","doi":"10.1016/j.robot.2025.104947","DOIUrl":"10.1016/j.robot.2025.104947","url":null,"abstract":"<div><div>For the sake of enhancing the rolling compaction quality and operation efficiency in infrastructure construction, this paper addresses the issue of optimal formation control for cooperative rolling compaction of a group of robotic rollers (RRs) by a combination of the reinforcement learning (RL)-based tracking control technique and the virtual structure method. The RR’s kinematic model is first established by fully considering the structural characteristics of the active revolute joint. Via the kinematic model and the virtual structure method, formation control of multiple RRs is formulated as path-following control with respect to their corresponding node in the desired rolling compaction formation shape. Then, optimal formation control policies of RRs are derived by the value functional that is the solution to tracking Hamilton–Jacobi-Bellman equation. By resorting to the RL-based tracking control, approximate optimal control policies are obtained by forward-in-time online neural network estimation of the value functional. Locally uniform ultimate boundedness of the closed-loop formation error system is analyzed rigorously by the Lyapunov technique. Finally, numerical simulation results are presented for three-RR cooperative rolling compaction of a clay core wall dam in Qianping reservoir to show the effectiveness of the main results of this paper.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"189 ","pages":"Article 104947"},"PeriodicalIF":4.3,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645035","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":"Sli-EfficientDet: A slimming and efficient water surface object detection model","authors":"Sai Ma ,&nbsp;Zhibin Xie ,&nbsp;Changbin Shao ,&nbsp;Xin Shu ,&nbsp;Peiyu Yan","doi":"10.1016/j.robot.2025.104960","DOIUrl":"10.1016/j.robot.2025.104960","url":null,"abstract":"<div><div>In the field of water surface object detection, deep learning technology has become a mainstream method. Unmanned Surface Vehicles (USVs), which perform precise sensing and measurement tasks on water surfaces, particularly benefit from these advancements. However, for hardware resource-constrained USVs, current detection models still struggle to find a balance between being lightweight and maintaining accuracy. To address this challenge, we first reduce parameters by clipping channels in the backbone network through a dependency graph based pruning method. Additionally, we introduce the Simple Attention Module (SimAM) into the backbone network to derive excellent three-dimensional attention weights without adding additional parameters during computation. Furthermore, we utilize the ghost module to reconstruct the feature fusion network by using simple linear operations to process feature maps, which enhances the network performance in feature extraction while further compressing the model. Experiments show that our model achieves a 15.56 % improvement in mean Average Precision (mAP) while reducing the count of model parameters by 55 % compared to the original EfficientDet-D0 model, and balancing lightweight and accuracy compared to the majority of current models.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"189 ","pages":"Article 104960"},"PeriodicalIF":4.3,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143580442","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":"Virtual attention points: Bridging human movement characteristics and dexterous robot motion generation","authors":"Abed Soleymani,&nbsp;Yafei Ou,&nbsp;Xingyu Li,&nbsp;Mahdi Tavakoli","doi":"10.1016/j.robot.2025.104946","DOIUrl":"10.1016/j.robot.2025.104946","url":null,"abstract":"<div><div>In this study, we introduce Virtual Attention Points (VAPs) as a novel technique for characterizing the essence of dexterous human movements through mathematical encoding. This method focuses on pivotal points to capture movement dynamics, resulting in the generation of versatile and human-like motions for robotic systems. The proposed method inspired by the idea of human movement primitives (MPs) generates an interpretable low-dimensional representation for a given complex movement based on a new encoding basis function. Our approach achieves a remarkable 97% improvement in encoding accuracy for dexterous demonstrations with agile maneuvers and sharp turns, surpassing existing MP-based methods, enhancing the precision of fine manipulation and elevating the fidelity of encoded actions to the human movement. The precise replication of crucial poses and underlying behaviors highlights the efficacy of our approach in faithfully capturing the intricacies of expert human demonstrations. Our approach also generates a meaningful and interpretable representation of each demonstration, which encapsulates the skills-related features for performance assessment purposes. We propose a novel trajectory cloning algorithm that minimally warps various movement demonstrations such that starting and end points of motions will be manipulated to desired locations. Our work holds transformative potential, especially in enhancing surgical training and autonomous surgical systems, where precision and human-like dexterity are paramount. As surgical operations necessitate dexterous trajectories to execute specific functional tasks like suturing, we implement the proposed method to assess its performance in surgical skills and autonomous surgery tasks.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"188 ","pages":"Article 104946"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548654","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 port water navigation solution based on priority sampling SAC: Taking Yantai port environment as an example","authors":"Yiming Zhao ,&nbsp;Fenglei Han ,&nbsp;Duanfeng Han ,&nbsp;Xiao Peng ,&nbsp;Wangyuan Zhao ,&nbsp;Guihua Xia","doi":"10.1016/j.robot.2025.104956","DOIUrl":"10.1016/j.robot.2025.104956","url":null,"abstract":"<div><div>Navigating port waters is challenging due to obstacles and regulated buoys, making traditional algorithms insufficient. Maritime Autonomous Surface Ships (MASS) must comply with strict International Association of Marine Aids and Lighthouse Authorities (IALA) regulations. This study presents an innovative navigation system integrated into an intelligent research ship, utilizing a Soft Actor-Critic (SAC) approach for decision-making. We train a navigation model in a custom Unity3D simulation that includes the IALA buoy system, employing a novel prioritization method to improve sample efficiency. Results show effective navigation in simulated environments, validated by real-ship testing at Yantai Port. This research enhances port navigation strategies and promotes the intellectualization of maritime operations by improving onboard decision-making and information processing.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"188 ","pages":"Article 104956"},"PeriodicalIF":4.3,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548656","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":"Development of a new path-planning algorithm for lattice based self-reconfigurable modular robots with pivoting cube shaped modules","authors":"Halil İbrahim Dokuyucu ,&nbsp;Nurhan Gürsel Özmen","doi":"10.1016/j.robot.2025.104955","DOIUrl":"10.1016/j.robot.2025.104955","url":null,"abstract":"<div><div>In this study, a new path-planning algorithm named “Jellyfish Pump Algorithm (JPA)” for the self-reconfiguration of lattice-based self-reconfigurable modular robots (SRMRs) is presented. The JPA is inspired by the shape changing behavior of a jellyfish during its motion. This motion always satisfies the structural balance of the jellyfish with the help of adaptable and periodic shape changing actions. The proposed approach tries to confirm a physically balanced transformation process of the SRMRs considering external effects such as the gravity. The aim is to conserve the balance by employing a static plus shaped core structure of the robot body during the self-reconfiguration. The mobile modules are allowed to move around this core structure between initial and final configurations. The pivoting cube model is used as the abstraction method of the introduced algorithm. The comparison between pivoting and sliding cube models is presented considering actual world implementation aspects of SRMRs. The JPA is developed as a modification to the well-known self-reconfiguration algorithm of Melt Sort Grow. The JPA allows the robot to reach the final configuration by melting the initial configuration into a balanced intermediate phase having a plus shaped structure instead of a line configuration. The physical balance of the robot is satisfied at each step of the self-reconfiguration process. Appropriate simulations using generic 3D initial configurations have validated the proposed algorithm. Extreme cases such as locomotion and bridge formation are tested with the proposed algorithm considering the robustness and applicability. The time complexity of the JPA is <span><math><mrow><mi>O</mi><mo>(</mo><msup><mrow><mi>n</mi></mrow><mn>2</mn></msup><mo>)</mo></mrow></math></span> for <span><math><mi>n</mi></math></span> modules, whereas the balance restrictions enforce the algorithm to generate number of moves less than the square of number of mobile modules. The proposed algorithm was compared with a validated Melt Sort Grow algorithm considering number of moves and time complexity, and the efficiency of the algorithm was verified.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"188 ","pages":"Article 104955"},"PeriodicalIF":4.3,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548655","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}