Robotics and Autonomous Systems最新文献_第4页

HEXmorph: Fault tolerance against single and dual rotor failure using geometric morphing on hexacopter HEXmorph：对单转子和双转子故障使用几何变形的六旋翼的容错

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2025-06-19 DOI: 10.1016/j.robot.2025.105047

Jun Kiat Tan , Archit Krishna Kamath , Karanjot Singh, Mir Feroskhan

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

引用次数: 0

A degree of flowability for virtual tubes 虚拟管的流动程度

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2025-06-18 DOI: 10.1016/j.robot.2025.105108

Quan Quan, Shuhan Huang, Kai-Yuan Cai

引用次数: 0

Formal and scalable multi-robot coordination methods for long horizon tasks with time uncertainty 具有时间不确定性的长视界任务的形式化可扩展多机器人协调方法

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2025-06-18 DOI: 10.1016/j.robot.2025.105103

Carlos Azevedo , Pedro U. Lima

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

引用次数: 0

Fire front path planning and tracking control of Uncrewed Aerial Vehicles using deep reinforcement learning 基于深度强化学习的无人机火力前沿路径规划与跟踪控制

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2025-06-12 DOI: 10.1016/j.robot.2025.105076

Mona Raoufi , Akbar Telikani , Tieling Zhang , Jun Shen

引用次数: 0

Efficient path planning for a dexterous arm–hand in complex environments 复杂环境下灵巧手臂的有效路径规划

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2025-06-12 DOI: 10.1016/j.robot.2025.105086

Wenpei Fan , Yaonan Wang , Wenrui Chen , Licheng Liu , Conghui Tang , Xin Li , Mingjie Dong

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

引用次数: 0

Optimal contractor for triangle constraint 三角约束下最优承包商

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2025-06-11 DOI: 10.1016/j.robot.2025.105079

Algassimou Diallo , Sebastien Lagrange , Remy Guyonneau , Daouda Niang Diatta , Sebastien Lahaye

引用次数: 0

Task and Motion Planning for grasping targets with object rearrangement in confined workspaces 受限工作空间中物体重排抓取目标的任务与运动规划

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2025-06-11 DOI: 10.1016/j.robot.2025.105091

Jinhwi Lee , ChangHwan Kim

{"title":"Task and Motion Planning for grasping targets with object rearrangement in confined workspaces","authors":"Jinhwi Lee , ChangHwan Kim","doi":"10.1016/j.robot.2025.105091","DOIUrl":"10.1016/j.robot.2025.105091","url":null,"abstract":"<div><div>We propose a planning algorithm for rearranging objects with a robot manipulator to grasp a target object within a cluttered environment. Our algorithm accounts for challenges posed by the high density of surrounding objects, which might render some objects undetectable by sensors and make it difficult to find collision-free paths for prehensile manipulation. The main focus is to optimize the efficiency of the overall task by minimizing the number of rearrangement actions to relocate obstacles. To achieve this, our algorithm generates a sequential order of object rearrangement using a combination of prehensile and non-prehensile grasping skills. By leveraging a search-based approach, our algorithm finds a rearrangement plan with the minimum number of actions among multiple solutions. In scenarios involving non-prehensile manipulation, we employ a heuristic function to evaluate the friction between objects and the table, aiding in the decision-making process. Furthermore, our algorithm incorporates the concept of invisible space as a probability calculation, allowing the search method to find currently undetectable objects by sensors. Through extensive experimentation in a real-world setting, we demonstrate the effectiveness of our algorithm in reducing the number of rearrangement actions required, thus showcasing its practicality and performance.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"193 ","pages":"Article 105091"},"PeriodicalIF":4.3,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298197","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}

引用次数: 0

NNPP: A learning-based heuristic model for accelerating optimal path planning on uneven terrain NNPP：一种基于学习的启发式模型，用于加速不平坦地形上的最优路径规划

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2025-06-10 DOI: 10.1016/j.robot.2025.105084

Yiming Ji, Yang Liu, Guanghu Xie, Boyu Ma, Zongwu Xie, Baoshi Cao

{"title":"NNPP: A learning-based heuristic model for accelerating optimal path planning on uneven terrain","authors":"Yiming Ji, Yang Liu, Guanghu Xie, Boyu Ma, Zongwu Xie, Baoshi Cao","doi":"10.1016/j.robot.2025.105084","DOIUrl":"10.1016/j.robot.2025.105084","url":null,"abstract":"<div><div>Intelligent autonomous path planning is essential for enhancing the exploration efficiency of mobile robots operating in uneven terrains like planetary surfaces and off-road environments. In this paper, we propose the NNPP model for computing the heuristic region, enabling foundation algorithms like <span><math><msup><mrow><mi>A</mi></mrow><mrow><mo>⋆</mo></mrow></msup></math></span> to find the optimal path solely within this reduced search space, effectively decreasing the search time. The NNPP model learns semantic information about start and goal locations, as well as map representations, from numerous pre-annotated optimal path demonstrations, and produces a probabilistic distribution over each pixel representing the likelihood of it belonging to an optimal path on the map. More specifically, the paper computes the traversal cost for each grid cell from the slope, roughness and elevation difference obtained from the digital elevation model. Subsequently, the start and goal locations are encoded using a Gaussian distribution and different location encoding parameters are analyzed for their effect on model performance. After training, the NNPP model is able to accelerate path planning on novel maps. Experiments demonstrate that the heuristic region generated by the NNPP model achieves a 3<span><math><mo>×</mo></math></span>speedup for optimal path planning under identical hardware conditions. Moreover, the NNPP model’s advantage becomes more pronounced as the size of the map increases.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"193 ","pages":"Article 105084"},"PeriodicalIF":4.3,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272376","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}

引用次数: 0

Partial camera calibration from a single circle 部分相机校准从一个单一的圆

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2025-06-10 DOI: 10.1016/j.robot.2025.105083

Marco Compagnoni, Viviana Desantis, Daniele Ugo Leonzio, Stefano Tubaro, Marco Marcon

{"title":"Partial camera calibration from a single circle","authors":"Marco Compagnoni, Viviana Desantis, Daniele Ugo Leonzio, Stefano Tubaro, Marco Marcon","doi":"10.1016/j.robot.2025.105083","DOIUrl":"10.1016/j.robot.2025.105083","url":null,"abstract":"<div><div>The accurate spatial location of a camera, in terms of both position and rotation, is essential in numerous tasks, such as robot navigation, indoor georeferencing for egocentric motion analysis, and estimation of the trajectory for manned and unmanned aerial vehicles. Precise localization requires estimating all six degrees of freedom (DoF) of a rigid body in space, which includes the three spatial coordinates (x, y, and z) and the three rotation angles (yaw, pitch, and roll). Typically, planar targets whose positions are known a priori are used to achieve this task. However, the accurate estimation of all 6 DoF depends on a recognizable target that can be identified from different distances, under different illuminations, and using cameras with different resolutions and focal lengths.</div><div>In many cases, it is enough to identify the target’s distance and height relative to its lying plane. For instance, a drone that needs to land in a predetermined area delimited by specific horizontal signage needs only to know the distance from the target and the UAV’s altitude to plan a landing trajectory accurately. Therefore, a simple circular target signage can be defined, which is easy to be identified even from far positions and under conditions of poor illumination. In this article, we propose a robust method to identify a circular target and accurately estimate the distance and altitude of the camera relative to the target lying plane. The circular signage is identified using the method of Euclidean invariants of the quadric surfaces. Finally, we present a set of simulations and real-life measurements to evaluate the accuracy and robustness of the proposed method.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"193 ","pages":"Article 105083"},"PeriodicalIF":4.3,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280953","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}

引用次数: 0

Error modeling and parameter identification of a 5-DOF hybrid robot considering angular transmission error 考虑角传动误差的五自由度混合动力机器人误差建模与参数辨识

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2025-06-09 DOI: 10.1016/j.robot.2025.105080

Jinyin Zhou , Bin Zhu , Jun Wu , Yanling Tian

{"title":"Error modeling and parameter identification of a 5-DOF hybrid robot considering angular transmission error","authors":"Jinyin Zhou , Bin Zhu , Jun Wu , Yanling Tian","doi":"10.1016/j.robot.2025.105080","DOIUrl":"10.1016/j.robot.2025.105080","url":null,"abstract":"<div><div>The forms of angular transmission error are complex and diverse, and it is difficult to derive the error model theoretically. Therefore, the geometric error and angular transmission error are generally studied respectively by neglecting the interference of these errors. In this paper, a novel mixed error model(MEM) combining the fitting model and the theoretical model is derived, which is formed by adding the mixed angular transmission error model to the geometric error model in the form of joint extension positioning error. The mixed angular transmission error model is derived based on the fitting angular transmission error and geometric error interference analysis in uniaxial experiments. In the identification process, the angular transmission error under uniaxial experimental measurement is first measured and fitted, and then the geometric error and angular transmission error are identified simultaneously based on the MEM. A 5-DOF hybrid robot is used as an example to verify the error modeling method and parameter identification process. Based on the error modeling method and parameter identification scheme, the robot’s motion error is dropped by more than 50% compared with the geometric error identification scheme.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"193 ","pages":"Article 105080"},"PeriodicalIF":4.3,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255137","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}

引用次数: 0