Robotics and Autonomous Systems最新文献_第8页

Multi-robot smooth path planning considering local motion control based on TEB-VO 基于TEB-VO局部运动控制的多机器人平滑路径规划

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2025-12-15 DOI: 10.1016/j.robot.2025.105304

Liming Wang , Wen Ma , Gedong Jiang , Chaoqing Min , Nuogang Sun , Xuesong Mei

Current global multi-robot path planning methods generally assume that agents strictly follow the planned paths during execution. However, factors such as slippage, delays, path deviations, and speed or acceleration constraints prevent robots from accurately following these paths, potentially leading to deadlocks or new conflicts. To address the issue of poor path feasibility in current multi-robot path planning methods, this paper proposes an integrated global path planning and local motion control algorithm for multi-robot, which considers both global optimal path planning and local flexible motion control. First, for global path planning, an improved SH-FEECBS (Smooth Heuristic Flexible Explicit Estimation Conflict-Based Search) method is introduced to reduce the number of turns and enhance path smoothness, thereby lowering the corrective burden on the subsequent local controller. Second, for local motion control, the TEB-VO (Time-Elastic Band Velocity Obstacle) method is proposed, integrating the velocity obstacle method to enable flexible robot control to provide constraint-aware, real-time avoidance and robust tracking. A series of experiments were designed and conducted to evaluate the performance of the proposed global path planning algorithm, local motion control algorithm, and their integrated approach in multi-robot path planning. The results show measurable gains which is SH-FEECBS improves path smoothness by 5 % and has fewer turns, TEB-VO increases the minimum safety distance by 10 %, and the integrated system enhances average-speed consistency by 20 %. Experimental results demonstrate that the proposed path planning and motion control methods outperform traditional approaches in terms of smoothness, obstacle avoidance, and dynamic motion control performance, effectively improving the operational efficiency and stability of multi-robot systems.

目前的全局多机器人路径规划方法一般假设智能体在执行过程中严格遵循规划的路径。然而，诸如滑移、延迟、路径偏差以及速度或加速度限制等因素会阻止机器人准确地遵循这些路径，从而可能导致死锁或新的冲突。针对当前多机器人路径规划方法路径可行性差的问题，提出了一种综合考虑全局最优路径规划和局部柔性运动控制的多机器人全局路径规划和局部运动控制算法。首先，在全局路径规划方面，引入改进的SH-FEECBS （Smooth Heuristic Flexible Explicit Estimation Conflict-Based Search）方法，减少了路径的转弯数，增强了路径的平滑性，从而降低了后续局部控制器的纠偏负担。其次，针对局部运动控制，提出了TEB-VO （Time-Elastic Band Velocity Obstacle，时间弹性带速度障碍）方法，结合速度障碍方法，使柔性机器人控制具有约束感知、实时回避和鲁棒跟踪能力。设计并进行了一系列实验，以评估所提出的全局路径规划算法、局部运动控制算法及其集成方法在多机器人路径规划中的性能。结果表明，SH-FEECBS的增益可使路径平滑度提高5%，转弯数减少，TEB-VO的最小安全距离提高10%，平均速度一致性提高20%。实验结果表明，所提出的路径规划和运动控制方法在平滑性、避障性和动态运动控制性能方面优于传统方法，有效地提高了多机器人系统的运行效率和稳定性。

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引用次数: 0

A shape prediction method for deformable linear object with natural curvature 具有自然曲率的可变形线性物体的形状预测方法

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2025-12-15 DOI: 10.1016/j.robot.2025.105302

Hang Zhou , Qi Lu , Jinwu Qian

Accurately predicting the shapes of Deformable Linear Object (DLO) is essential for enhancing robotic manipulation tasks, particularly when DLO exhibits non-zero natural curvature. Natural curvature describes the intrinsic rest configuration of a DLO in the absence of external forces and includes both curvature and torsion. Misidentifying these properties can severely degrade model accuracy. In this paper, we propose a method for predicting the shape of DLO that estimates the equivalent Young’s modulus

Y_{e}

and natural curvature, using the Discrete Elastic Rod (DER) physical model. This approach eliminates the need for manually measuring material properties. The process starts with acquiring a smoothed point cloud of the DLO. Then, an optimization technique is used to identify

Y_{e}

and the natural curvature, which are input into the DER model to minimize discrepancies between the predicted and observed DLO shapes. Testing with both synthetic datasets and real-world DLO data demonstrates the accuracy and robustness of our method. This approach can be integrated into DLO manipulation workflows, enhancing the predictive accuracy of the model.

准确预测可变形线性物体（DLO）的形状对于增强机器人操作任务至关重要，特别是当DLO呈现非零自然曲率时。自然曲率描述了DLO在没有外力的情况下的内在静止结构，包括曲率和扭转。错误地识别这些属性会严重降低模型的准确性。在本文中，我们提出了一种预测DLO形状的方法，该方法使用离散弹性棒（DER）物理模型估计等效杨氏模量Ye和自然曲率。这种方法消除了手动测量材料性能的需要。该过程从获取DLO的平滑点云开始。然后，使用优化技术识别Ye和自然曲率，并将其输入到DER模型中，以最小化预测和观测到的DLO形状之间的差异。对合成数据集和实际DLO数据的测试证明了我们的方法的准确性和鲁棒性。该方法可以集成到DLO操作工作流程中，提高模型的预测精度。

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引用次数: 0

CRESTA: A Cognitivist Robot Execution framework for Semantic-driven Task Awareness 一个语义驱动任务感知的认知主义机器人执行框架

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2025-12-15 DOI: 10.1016/j.robot.2025.105303

Damiano Gasperini , Luca Muratore , Nikos Tsagarakis

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.

自主机器人的发展仍然需要一个全面的任务执行感知框架，使其能够在未知的动态环境中产生自主行为和响应。情境感知旨在通过有效地结合关键能力，如推理、规划、将行动影响投射到未来状态，以及对周围环境的感知和理解，增强机器人在任务执行过程中的自主性和适应性。在这项工作中，我们提出了CRESTA，一个新的认知主义框架，用于语义驱动的任务意识，解决感知，导航和操纵动态环境的复杂挑战。CRESTA的目标是实现有效的机器人感知，这依赖于感知到的环境语义，以及在线规划、推理和监控的组合使用，同时还能够从任务级故障中恢复。它被设计为一组在线模块，用于(a)收集和分析多传感器数据以及更新世界模型描述，(b)实时决策和任务状态监测，以及(c)执行每个动作。该框架具有高度模块化和可配置性，可用于各种机器人系统，旨在适应各种机器人平台和任务。在这项工作中，CRESTA框架的详细描述伴随着演示任务来展示其在CENTAURO机器人和定制6自由度机械手上的功能。在讨论的实验结果中，CRESTA引导机器人打开门或导航和操纵杠杆，同时通过调整其动作参数从失败中恢复过来。

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引用次数: 0

A composite motion planning scheme based on time-varying recurrent neural network for mobile robot manipulators 基于时变递归神经网络的移动机器人复合运动规划方案

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2025-12-12 DOI: 10.1016/j.robot.2025.105298

Xitong Gao , Luwen Yang , Zhijun Zhang

In order to solve the double ended (end-effector and mobile platform) motion planning problem of mobile robot manipulator, a composite motion planning (CMP) scheme based on time-varying recurrent neural network is proposed and analyzed. In traditional schemes, motion planning of the end-effector is common, the route of the platform is generated from the end-trajectory calculation. However, in realistic tasks, the end trajectory and platform route are often independent to each other. To do so, kinematic models of the double ended are first derived in detail and formulated as equality constraints, respectively. Secondly, the posture constraint and combined physical constraint are designed and formulated as an equation and inequality constraint, respectively. Then, the CMP scheme is proposed and formulated as a constrained quadratic programming problem. Thirdly, the optimal solution of the quadratic programming problem is obtained by the designed time-varying recurrent neural network. Finally, experiments verify that the proposed CMP scheme can simultaneously plan the double ended of the mobile manipulator.

为解决移动机器人机械臂双端（末端执行器和移动平台）运动规划问题，提出并分析了一种基于时变递归神经网络的复合运动规划方案。在传统的方案中，末端执行器的运动规划是常见的，平台的路线是由末端轨迹计算生成的。然而，在现实任务中，终点轨迹和平台路线往往是相互独立的。为此，首先详细推导了双端机构的运动模型，并分别将其表述为等式约束。其次，将姿态约束和组合物理约束分别设计为方程约束和不等式约束；然后，提出了CMP格式，并将其表述为约束二次规划问题。第三，利用设计的时变递归神经网络求出二次规划问题的最优解。最后，实验验证了所提出的CMP方案能够同时规划移动机械臂的双端。

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引用次数: 0

Uncertainty Aware-Predictive Control Barrier Functions: Safer human–robot interaction through probabilistic motion forecasting 不确定性感知-预测控制障碍函数：通过概率运动预测实现更安全的人机交互

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2025-12-11 DOI: 10.1016/j.robot.2025.105291

Lorenzo Busellato , Federico Cunico , Diego Dall’Alba , Marco Emporio , Andrea Giachetti , Riccardo Muradore , Marco Cristani

To enable flexible, high-throughput automation in settings where people and robots share workspaces, collaborative robotic cells must reconcile stringent safety guarantees with the need for responsive and effective behavior. A dynamic obstacle is the stochastic, task-dependent variability of human motion: when robots fall back on purely reactive or worst-case envelopes, they brake unnecessarily, stall task progress, and tamper with the fluidity that true Human–Robot Interaction (HRI) demands. In recent years, learning-based human-motion prediction has rapidly advanced, although most approaches produce worst-case scenario forecasts that often do not treat prediction uncertainty in a well-structured way, resulting in over-conservative planning algorithms, limiting their flexibility. This paper introduces Uncertainty-Aware Predictive Control Barrier Functions (UA-PCBFs), a unified framework that fuses probabilistic human hand motion forecasting with the formal safety guarantees of Control Barrier Functions (CBFs). In contrast to CBFs and other variants, our framework allows for a dynamic adjustment of the safety margin thanks to the human motion uncertainty estimation provided by the deep-learning forecasting module. Thanks to the awareness of prediction uncertainty, UA-PCBFs empower collaborative robots with a deeper understanding of future human states, facilitating more fluid and intelligent interactions through informed motion planning. Our key contribution is the first integration of epistemic prediction uncertainty directly into predictive CBFs, dynamically adjusting safety margins based on forecast confidence without assumptions about uncertainty evolution. We validate UA-PCBFs through comprehensive real-world experiments with an increasing level of realism, including automated setups (to perform exactly repeatable motions) with a robotic hand and direct human–robot interactions (to validate promptness, usability, and human confidence). Relative to state-of-the-art HRI architectures, UA-PCBFs show better performance in task-critical metrics, significantly reducing the number of violations of the robot’s safe space during interaction with respect to the state-of-the-art. Data and code will be released upon acceptance.

为了在人和机器人共享工作空间的环境中实现灵活、高吞吐量的自动化，协作机器人单元必须协调严格的安全保证与响应和有效行为的需求。动态障碍是人类运动的随机、与任务相关的可变性：当机器人回到纯粹的反应性或最坏情况时，它们会不必要地刹车，拖延任务进度，并破坏真正的人机交互（HRI）所要求的流动性。近年来，基于学习的人体运动预测迅速发展，尽管大多数方法产生的最坏情况预测通常没有以良好的结构方式处理预测的不确定性，导致过于保守的规划算法，限制了它们的灵活性。本文介绍了不确定性感知预测控制障碍函数（UA-PCBFs），这是一个将概率手部运动预测与控制障碍函数（CBFs）的形式安全保证融合在一起的统一框架。与cbf和其他变体相比，我们的框架允许动态调整安全裕度，这要归功于深度学习预测模块提供的人体运动不确定性估计。由于对预测不确定性的认识，UA-PCBFs使协作机器人能够更深入地了解未来的人类状态，通过知情的运动规划促进更流畅和智能的交互。我们的主要贡献是首次将认知预测不确定性直接集成到预测cbf中，在不考虑不确定性演变的情况下，基于预测置信度动态调整安全边际。我们通过全面的现实世界实验来验证UA-PCBFs，这些实验具有越来越高的真实感，包括机器人手的自动设置（执行精确的可重复运动）和直接的人机交互（验证及时性，可用性和人类信心）。相对于最先进的HRI架构，UA-PCBFs在关键任务指标上表现出更好的性能，显著减少了机器人在与最先进技术交互过程中对安全空间的侵犯次数。数据和代码将在验收后发布。

{"title":"Uncertainty Aware-Predictive Control Barrier Functions: Safer human–robot interaction through probabilistic motion forecasting","authors":"Lorenzo Busellato , Federico Cunico , Diego Dall’Alba , Marco Emporio , Andrea Giachetti , Riccardo Muradore , Marco Cristani","doi":"10.1016/j.robot.2025.105291","DOIUrl":"10.1016/j.robot.2025.105291","url":null,"abstract":"<div><div>To enable flexible, high-throughput automation in settings where people and robots share workspaces, collaborative robotic cells must reconcile stringent safety guarantees with the need for responsive and effective behavior. A dynamic obstacle is the stochastic, task-dependent variability of human motion: when robots fall back on purely reactive or worst-case envelopes, they brake unnecessarily, stall task progress, and tamper with the fluidity that true Human–Robot Interaction (HRI) demands. In recent years, learning-based human-motion prediction has rapidly advanced, although most approaches produce worst-case scenario forecasts that often do not treat prediction uncertainty in a well-structured way, resulting in over-conservative planning algorithms, limiting their flexibility. This paper introduces Uncertainty-Aware Predictive Control Barrier Functions (UA-PCBFs), a unified framework that fuses probabilistic human hand motion forecasting with the formal safety guarantees of Control Barrier Functions (CBFs). In contrast to CBFs and other variants, our framework allows for a dynamic adjustment of the safety margin thanks to the human motion uncertainty estimation provided by the deep-learning forecasting module. Thanks to the awareness of prediction uncertainty, UA-PCBFs empower collaborative robots with a deeper understanding of future human states, facilitating more fluid and intelligent interactions through informed motion planning. Our key contribution is the first integration of epistemic prediction uncertainty directly into predictive CBFs, dynamically adjusting safety margins based on forecast confidence without assumptions about uncertainty evolution. We validate UA-PCBFs through comprehensive real-world experiments with an increasing level of realism, including automated setups (to perform exactly repeatable motions) with a robotic hand and direct human–robot interactions (to validate promptness, usability, and human confidence). Relative to state-of-the-art HRI architectures, UA-PCBFs show better performance in task-critical metrics, significantly reducing the number of violations of the robot’s safe space during interaction with respect to the state-of-the-art. Data and code will be released upon acceptance.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"197 ","pages":"Article 105291"},"PeriodicalIF":5.2,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790304","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

Collision prediction using plan learning in mixed human–robot work cells 基于计划学习的人-机器人混合工作单元碰撞预测

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2025-12-11 DOI: 10.1016/j.robot.2025.105292

Luca Geretti , Stefano Centomo , Michele Boldo , Enrico Martini , Nicola Bombieri , Davide Quaglia , Tiziano Villa

In mixed human–robot work cells the emphasis is traditionally on collision avoidance to circumvent injuries and production down times. In this paper we discuss how long in advance a collision can be predicted given the behavior of a robotic arm and the current occupancy of both the robot and the human. The behavior of the robot is a sequence of predefined operations that constitute its plan, each one with a given trajectory. However, we do not know the exact trajectory or the plan a priori. Under the assumption that the plan has a cyclic character, we propose an approach to learn it in real time from state samples and use the resulting model to estimate the time before a collision. The pose of the human is obtained by a multi-camera inference application based on neural networks at the edge to preserve privacy and enforce scalability. The occupancy of the manipulator and of the human are modeled through the composition of segments which overcomes the traditional “virtual cage” and can be adapted to different human beings and robots. The system has been implemented in a real factory scenario to demonstrate its readiness regarding both industrial constraints and computational complexity.

在混合人机工作单元中，传统的重点是避免碰撞，以避免伤害和生产停机时间。在本文中，我们讨论了在给定机械臂的行为和机器人和人的当前占用情况下，可以提前多长时间预测碰撞。机器人的行为是一系列预定义的操作，这些操作构成了机器人的计划，每个操作都有一个给定的轨迹。然而，我们不知道确切的轨迹或先验计划。在假设计划具有循环特性的前提下，我们提出了一种从状态样本中实时学习计划的方法，并使用得到的模型来估计碰撞前的时间。人体姿态由基于神经网络的多相机推理应用程序在边缘获得，以保护隐私和增强可扩展性。通过节段的组成来模拟机械手和人的占用，克服了传统的“虚拟笼子”，可以适应不同的人和机器人。该系统已在一个真实的工厂场景中实现，以证明其在工业约束和计算复杂性方面的就绪性。

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引用次数: 0

Multi-goal path planning for robot-aided transcranial magnetic stimulation 机器人辅助经颅磁刺激的多目标路径规划

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2025-12-11 DOI: 10.1016/j.robot.2025.105294

Lixia Wang , Qing Tang , Haoyang Xing , Jiarui Dong

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.

经颅磁刺激（transcranial magnetic stimulation， TMS）自提出以来，在脑部疾病的治疗等领域得到了广泛的应用。机器人辅助TMS治疗确保了治疗定位的准确性和耐久性。然而，机器人辅助疗法的使用也带来了潜在的碰撞和路径规划方面的挑战。由于冗余自由度（DOF）的存在，机器人在执行每次给定的激励时具有无限种构型。在TMS治疗中，涉及到访问多个目标点并返回初始位置的问题是一个带邻域的广义旅行商问题（GTSPN）和无碰撞路径规划问题的结合。提出了一种基于概率路线图（PRM）和A*算法的全局方法。为了安全访问目标点，该方法在球体上均匀采样。引入了一种特殊的最近邻定义和边的延迟碰撞查询来加速路线图的构建。然后，利用改进的a *算法和多目标启发式算法，通过所有目标点快速搜索全局路径。最后，提出局部研究与全局研究相结合的策略，得到最终的无碰撞路径。利用典型的经颅磁刺激治疗系统模型，在仿真和物理平台上进行了实验。结果表明，本文提出的算法可以有效避免碰撞，并在短时间内为TMS治疗提供最优规划结果。

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引用次数: 0

Multi-objective optimization for dimensional synthesis of tendon placement and structural design for energy-efficient and feasible static workspace in continuum robots 面向连续体机器人节能可行静态工作空间的肌腱布置尺寸综合和结构设计多目标优化

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2025-12-11 DOI: 10.1016/j.robot.2025.105300

Mohammad Jabari , Carmen Visconte , Giuseppe Quaglia , Med Amine Laribi

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.

肌腱驱动连续体机器人（tdcr）面临着能源效率和静态性能之间的关键权衡，以导航受限环境，这是医疗和工业应用中的一个挑战。本研究提出了一个双目标优化框架，以增强两节段TDCR的肌腱放置和尺寸合成，每节段有7个椎间盘和4个肌腱。利用基于分段常曲率（PCC）理论的动静态模型和多目标遗传算法（MOGA），优化了径向肌腱距离和角偏移量。这些解决方案减少了30%的机械工作量，并扩大了3 - 5%的工作空间，通过100个随机肌腱力样本进行了验证。结果为提高TDCR在微创手术和工业检查中的性能提供了实用指导。

引用次数: 0

A survey on theories and applications for multi-robot cooperative hunting 多机器人协同狩猎理论与应用综述

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2025-12-11 DOI: 10.1016/j.robot.2025.105296

Jianjun Ni , Yonghao Zhao , Ziru Zhang , Chunyan Ke , Simon X. Yang

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.

多机器人协同狩猎问题因其在监视、搜救和军事等领域的潜在应用而受到广泛关注。近年来，在这一领域提出了许多优秀的解决方案。因此，本文对多机器人协同狩猎领域的最新进展和挑战进行了全面的综述。该调查提供了该领域关键要素的概述，例如机器人类型、团队组成、系统架构、通信机制、合作模式等。此外，分析了多机器人协同狩猎中的关键问题，包括感知与环境理解、任务分配、编队控制、路径规划、轨迹跟踪和决策。然后，对这些问题的代表性理论和方法进行了总结。最后，对可能的解决方案进行了讨论，并提出了该领域未来的研究方向。

引用次数: 0

Beyond coverage path planning: Can UAV swarms perfect scattered regions inspections? 超越覆盖路径规划：无人机群能否完善分散区域巡检？

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2025-12-10 DOI: 10.1016/j.robot.2025.105297

Socratis Gkelios , Savvas D. Apostolidis , Pavlos Ch. Kapoutsis , Elias B. Kosmatopoulos , Athanasios Ch. Kapoutsis

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 GitHub¹ and the collected and processed data from the real-world experiments are all hosted on Zenodo². Finally, this on-line platform³ allows the interested readers to interact with the mUDAI method and generate their own multi-UAV FISR missions.

无人驾驶飞行器（uav）通过提供一种更安全、更高效、更灵活的替代传统方法，彻底改变了检查任务。然而，电池的限制往往限制了它们的有效性，这就需要开发优化的飞行路径和数据收集技术。虽然覆盖路径规划（CPP）等现有方法可以确保全面的数据收集，但它们可能效率低下，特别是在检查多个未连接的感兴趣区域（roi）时。介绍了离散区域快速检测（FISR）问题，提出了一种新的解决方案——多无人机离散区域检测（mUDAI）方法。引入的方法实现了双重优化程序，用于计算最佳图像捕获位置和最有效的无人机轨迹，平衡数据分辨率和操作时间，最大限度地减少冗余数据收集和资源消耗。mUDAI方法旨在对分散的roi进行快速、有效的检查，使其成为安全基础设施评估、农业检查和紧急现场评估等应用的理想选择。模拟评估和实际部署相结合，用于验证和量化该方法提高操作效率的能力，同时保持高质量的数据捕获，证明其在实际操作中的有效性。mUDAI方法的开源Python实现可以在GitHub1上找到，从真实世界的实验中收集和处理的数据都托管在Zenodo2上。最后，这个在线平台3允许感兴趣的读者与mUDAI方法进行交互，并生成自己的多无人机FISR任务。

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引用次数: 0