Robotics and Autonomous Systems最新文献

{"title":"Phase-independent Dynamic Movement Primitives with applications to human–robot co-manipulation and time optimal planning","authors":"Giovanni Braglia,&nbsp;Davide Tebaldi,&nbsp;Luigi Biagiotti","doi":"10.1016/j.robot.2025.105120","DOIUrl":"10.1016/j.robot.2025.105120","url":null,"abstract":"<div><div>Dynamic Movement Primitives (DMP) are an established and efficient method for encoding robotic tasks that require adaptation based on reference motions. Typically, the nominal trajectory is obtained through Programming by Demonstration (PbD), where the robot learns a task via kinesthetic guidance and reproduces it in terms of both geometric path and timing law. Modifying the duration of the execution in standard DMPs is achieved by adjusting a time constant in the model.</div><div>This paper introduces a novel approach to fully decouple the geometric information of a task from its temporal information using an algorithm called spatial sampling, which allows parameterizing the demonstrated curve by its arc-length. This motivates the use of the name Geometric DMP (GDMP) for the proposed DMP approach. The proposed spatial sampling algorithm guarantees the regularity of the demonstrated curve and ensures a consistent projection of the human force throughout the task in a human-in-the-loop scenario. GDMP exhibits phase independence, as its phase variable is no longer constrained to the demonstration’s timing law, enabling a wide range of applications, including phase optimization problems and human-in-the-loop applications. Firstly, a minimum task duration optimization problem subject to velocity and acceleration constraints is formulated. The decoupling of path and speed in GDMP allows to achieve optimal time duration without violating the constraints. Secondly, GDMP is validated in a human-in-the-loop application, providing a theoretical passivity analysis and an experimental stability evaluation in co-manipulation tasks. Finally, GDMP is compared with other DMP architectures available in the literature, both for the phase optimization problem and experimentally with reference to an insertion task and a simulated welding task, showcasing the enhanced performance of GDMP with respect to other solutions.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"194 ","pages":"Article 105120"},"PeriodicalIF":4.3,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686544","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":"Regional pole placement-based robust lateral controller for autonomous ground vehicles considering uncertainty","authors":"Md. Abdur Rahim ,&nbsp;Mohammad Rokonuzzaman ,&nbsp;Ahmad Abu Alqumsan ,&nbsp;Adetokunbo Arogbonlo","doi":"10.1016/j.robot.2025.105121","DOIUrl":"10.1016/j.robot.2025.105121","url":null,"abstract":"<div><div>Autonomous ground vehicles (AGVs) often face challenges in maintaining tracking accuracy and stability due to uncertainties and external factors, such as variations in road surface friction and wind. These factors, particularly at higher speeds, significantly hinder the ability to achieve the desired stability and tracking performance. To address these challenges, we propose a novel robust lateral controller (RLC) by exploiting the <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> synthesis technique, considering uncertain cornering stiffness, with linear matrix inequality (LMI)-based regional pole placement constraints (RPPC). The proposed regional pole placement constraints-based robust lateral controller (RPPC-RLC) with uncertainty is designed to be robust against variations in road conditions and external disturbances, ensuring the desired path-tracking accuracy and vehicle stability. A state-feedback control law is employed using a nonlinear vehicle dynamics model to develop LMIs as performance conditions. Additionally, we utilised RPPC technique to precisely refine the controller gain for ensuring precise stability and robust performance in the presence of uncertainties and active disturbances. The proposed controller’s effectiveness is rigorously examined under different road conditions, various AGV speeds, and both the presence and absence of wind disturbances, while cornering stiffness was considered an uncertain parameter. The performance of the controller was also compared with several commonly used controllers, such as the model predictive controller (MPC), <span><math><msub><mrow><mi>H</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span>, conventional robust controller, and the Linear Quadratic Regulator (LQR). The results demonstrate that the proposed controller outperforms these alternatives in terms of minimising the lateral position error and heading error, based on different statistical parameters. Furthermore, we validated the controller’s performance in a MATLAB/Simulink environment using a 14-degree-of-freedom complex vehicle model. Finally, the proposed RPPC-RLC with uncertainty exhibited efficient tracking performance and maintained the stability of the AGV under varying road conditions and wind disturbances at different speeds, even at high speeds.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"194 ","pages":"Article 105121"},"PeriodicalIF":4.3,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679721","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":"Trajectory planning system for bimanual robots: Achieving efficient collision-free manipulation","authors":"Francisco José Martínez-Peral ,&nbsp;Jorge Borrell Méndez ,&nbsp;Dennis Mronga ,&nbsp;José Vicente Segura-Heras ,&nbsp;Carlos Perez-Vidal","doi":"10.1016/j.robot.2025.105118","DOIUrl":"10.1016/j.robot.2025.105118","url":null,"abstract":"<div><div>Pick-and-place operations are non-value-added activities but essential in many industrial processes. Some of these operations must be performed by dual-arm robots, which represent new challenges in terms of collision-avoidance due to the use of a shared workspace. This work addresses these two issues by proposing a Task and Motion Architecture (TMA) designed to optimize pick-and-place tasks, ensuring efficient and safe operation through collision-free movements. This architecture consists of two interconnected sublayers, the Task Planner (TP) and the Global Motion Planner (GMP). The TP calculates the optimal sequence of operations, minimizing the total execution time and guaranteeing a collision-free sequence. The GMP plans the trajectories of the robotic arms using predefined motion strategies and following the calculated optimal sequence. This work presents a novel solution for enhancing the efficiency of robot coordination in real-world settings by integrating an intercommunicated TP and MP. Results from simulations demonstrate improved task efficiency, reduced operational times, and successful collision avoidance between robots.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"194 ","pages":"Article 105118"},"PeriodicalIF":4.3,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679719","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":"NMPC design for a self-aligning compliant gait rehabilitation robot","authors":"Yinan Jin ,&nbsp;Tanishka Goyal ,&nbsp;Prashant K. Jamwal ,&nbsp;Roland Goecke ,&nbsp;Mergen H. Ghayesh ,&nbsp;Shahid Hussain","doi":"10.1016/j.robot.2025.105128","DOIUrl":"10.1016/j.robot.2025.105128","url":null,"abstract":"<div><div>The application of robotic devices in rehabilitation is proliferating. Such devices’ mechanism design, actuation, and control strategy are essential for effective and successful rehabilitation treatment. This paper investigates the effectiveness of a self-aligning mechanism for a multi-DOFs (Degrees of Freedom) rehabilitation robot. The actuation is provided by lightweight albeit powerful Pneumatic Muscle Actuators (PMA). Although the mechanism design and the actuation system provide a safe, secure, and efficient platform for rehabilitation, they increase the complexity of the system modeling and, subsequently, the control system’s design. Furthermore, the mechanism has three active and five passive DOFs, which further increase the intricacies of system identification. Hence, this paper presents an autodidactic approach to identify the system dynamics using the Koopman operator. The learned operator is then integrated with the Nonlinear Model Predictive Controller (NMPC) to guide the robot along the predefined path while adapting to the nonlinear dynamics of the physical human-robot interaction. Finally, the rehabilitation robot and the control scheme were experimentally validated with healthy human subjects. The results demonstrate that the NMPC controller could successfully manipulate the gait rehabilitation robot with the subject to achieve the desired orientation during the entire gait cycle.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"194 ","pages":"Article 105128"},"PeriodicalIF":4.3,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633103","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":"LiDAR odometry method based on multi-scale fusion and semantic enhancement","authors":"Yibin Ye ,&nbsp;Yang Ren ,&nbsp;Yiming Fan ,&nbsp;Yiyou Liang ,&nbsp;Hui Zeng","doi":"10.1016/j.robot.2025.105093","DOIUrl":"10.1016/j.robot.2025.105093","url":null,"abstract":"<div><div>In this paper, we propose an end-to-end deep learning-based LiDAR odometry framework addressing key challenges such as point cloud information loss, density variability, and dynamic scene uncertainty. By directly using raw point clouds, our method avoids dimensionality reduction loss and introduces a light-weight geometrically adaptive convolution to improve feature extraction based on local geometric structures. Additionally, a multi-scale fusion and semantic enhancement strategy is employed to incorporate semantic context and optimize pose estimation from coarse to fine. Experimental results on the KITTI dataset show that our approach is competitive with existing methods in accuracy and robustness.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"194 ","pages":"Article 105093"},"PeriodicalIF":4.3,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144653083","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":"Design and optimization of a novel pneumatic translational manipulator with independent constraints","authors":"Tao Wang,&nbsp;Ying Zhao,&nbsp;Bo Wang","doi":"10.1016/j.robot.2025.105125","DOIUrl":"10.1016/j.robot.2025.105125","url":null,"abstract":"<div><div>Pneumatic parallel translation robots offer several advantages in complex industrial production environments, such as a simple structure, high cleanliness, and excellent safety. This paper presents a novel split-type translational manipulator structure 3S<u>P</u>S+3CPR. The independent restraining limbs (3-CPR) effectively constrain undesired degrees of freedom at the platform's end without compromising the kinematic properties of the manipulator. This paper provides kinematic, workspace analysis, and performance evaluation, and furthermore, proposes a dimensional optimization scheme for the manipulator based on motion/force transmission performance and geometric constraints. Prototype experiments have been conducted to validate the rationality and efficacy of the overall design. Compared to traditional integrated drive-constrained translational mechanisms, this pure linear transmission design features simple analysis, flexible design, and fewer associated parameters. Additionally, using cylinders as actuators endows the manipulator with rapid response characteristics, making it suitable for industrial applications requiring fast translation.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"194 ","pages":"Article 105125"},"PeriodicalIF":4.3,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662512","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":"Conform to grip: Joint reaction force-driven adaptive variable admittance control of biped climbing robots","authors":"Haifei Zhu,&nbsp;Pengcheng Ye,&nbsp;Jiongyu Tan,&nbsp;Weinan Chen,&nbsp;Tao Zhang,&nbsp;Yisheng Guan","doi":"10.1016/j.robot.2025.105105","DOIUrl":"10.1016/j.robot.2025.105105","url":null,"abstract":"<div><div>Current biped climbing robots encounter persistent challenges in aligning their grippers with structural elements like poles, regardless of teleoperation or perception-based control implementations. To overcome this limitation, we present a joint reaction force-driven adaptive variable admittance control framework that enables autonomous compliant alignment with enhanced precision. The proposed method utilizes unintentional gripper-pole contact-induced joint reaction forces to drive alignment through a variable damping admittance controller. Damping parameters are adaptively regulated through proportional-derivative control law based on real-time joint reaction force errors. By establishing zero reference reaction force, the system concurrently accomplishes dual objectives: gripper pose alignment and reaction force minimization. Experimental validation confirms that our framework significantly enhances alignment efficiency and gripping reliability without requiring explicit gripper-pole pose detection. This methodology proves particularly effective for robotic systems transitioning between open-chain and closed-chain configurations while resolving inherent joint conflicts.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"194 ","pages":"Article 105105"},"PeriodicalIF":4.3,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604738","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":"Quantum neural network-based inverse kinematics of a six-jointed industrial robotic arm","authors":"Mehdi Fazilat,&nbsp;Nadjet Zioui","doi":"10.1016/j.robot.2025.105123","DOIUrl":"10.1016/j.robot.2025.105123","url":null,"abstract":"<div><div>This research examines the potential of quantum-inspired neural networks (QNNs) for solving the inverse kinematics of robotic arms, focusing on the six-degree-of-freedom ABB IRB140 robot. Traditional inverse kinematics approaches face challenges such as non-unique solutions and computational complexity, especially with increasing degrees of freedom. While artificial neural networks (ANNs) have shown promise, they require further improvements, particularly in terms of quantum computing integration. This study introduces a quantum-inspired activation function to multi-layer perceptron neural networks. We compared ANNs and QNNs with and without singularity avoidance, finding that QNNs significantly outperformed ANNs in mean absolute error (MAE), achieving a 15.60 % lower MAE in singularity-free models and a 16.67 % lower MAE in singularity-avoidance models. The QNNs demonstrated superior precision, with a position error of 1.64 mm and an orientation error of 0.00179 radians when avoiding singularities. These results highlight the potential of QNNs to enhance the precision, efficiency, and performance of robotic arm manipulation. Quantum computing offers advantages including parallelism, quantum entanglement, and quantum annealing, which contribute to the QNNs’ superior performance. Overall, this study represents a practical contribution to robotics and quantum computing, paving the way for future research into applying quantum principles to neural network models for robotics.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"194 ","pages":"Article 105123"},"PeriodicalIF":4.3,"publicationDate":"2025-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604739","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":"On-policy and Off-policy Q-learning algorithms with policy iteration for two-wheeled inverted pendulum systems","authors":"Ba Quoc Anh Nguyen ,&nbsp;Ngoc Trung Dang ,&nbsp;Thanh Tung Le ,&nbsp;Phuong Nam Dao","doi":"10.1016/j.robot.2025.105111","DOIUrl":"10.1016/j.robot.2025.105111","url":null,"abstract":"<div><div>This article delves into the investigation of On-policy and Off-policy Q-learning algorithms for controlling two-wheeled inverted pendulum (TWIP) robots in situations where knowledge about the dynamic system is uncertain. Both on-policy and off-policy Q-learning algorithms ensure optimal and model-free control by employing a data collection approach without the knowledge of model. The On-policy algorithm performs real-time data collection, continuously gathering data and iteratively calculating a new control policy until it converges to the optimal value. In contrast, the Off-policy algorithm collects data only once and applies it to the system after completing the learning process. To enhance computational efficiency and minimize the amount of data required, the TWIP system is divided into two Sub-systems. These Sub-systems consist of smaller system matrices that can be controlled independently. This division reduces the data collection burden and accelerates the calculation speed of the algorithms. The utilization of Off-policy techniques proves to be advantageous in developing algorithms with data efficiency and achieving higher accuracy. The influence of probing noise on the Q-function is comprehensively considered in both proposed algorithms. By utilizing a single data set and eliminating the influence of noise, the Off-policy techniques enhance algorithm performance. Finally, the article presents simulation results of the TWIP system to validate the effectiveness of the two proposed control schemes.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"193 ","pages":"Article 105111"},"PeriodicalIF":4.3,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523056","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":"HighwayLLM: Decision-making and navigation in highway driving with RL-informed language model","authors":"Mustafa Yildirim,&nbsp;Barkin Dagda,&nbsp;Vinal Asodia,&nbsp;Saber Fallah","doi":"10.1016/j.robot.2025.105114","DOIUrl":"10.1016/j.robot.2025.105114","url":null,"abstract":"<div><div>Autonomous driving is a complex task which requires advanced decision making and control algorithms. Understanding the rationale behind the autonomous vehicles’ decision is crucial to ensure their safe and effective operation on highway driving. This study presents a novel approach, <em>HighwayLLM</em>, which harnesses the reasoning capabilities of large language models (LLMs) to predict the future waypoints for ego-vehicle’s navigation. Our approach also utilizes a pre-trained Reinforcement Learning (RL) model to serve as a high-level planner, making decisions on appropriate meta-level actions. The HighwayLLM combines the output from the RL model and the current state information to make safe, collision-free, and explainable predictions for the next states, thereby constructing a trajectory for the ego-vehicle. Subsequently, a PID-based controller guides the vehicle to the waypoints predicted by the LLM agent. This integration of LLM with RL and PID enhances the decision-making process, provides interpretability for highway autonomous driving and reduces the number of collisions compared to the baseline method.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"193 ","pages":"Article 105114"},"PeriodicalIF":4.3,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523181","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}