Robotics and Autonomous Systems最新文献_第10页

A sufficient condition for feature agnostic video stabilization for surveillance robots: A state space approach 监控机器人特征不可知视频稳定的充分条件：一种状态空间方法

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

Robotics and Autonomous Systems

Pub Date : 2025-12-03 DOI: 10.1016/j.robot.2025.105275

Adwaith Vijayakumar, Abhishek Gupta, Leena Vachhani

A camera is a ubiquitous sensor in surveillance robots. Often the camera is subjected to unintended motion from either external factors or inherent dynamics. The intended camera motion induces constant flow vectors for short segments of a video sequence. Video stabilization is the removal of the effect of unintended camera motion from a video sequence. The surveillance robots are often made to operate indoors, where features are scarce for motion estimation rendering traditional video stabilization methods unreliable. Unlike feature/motion extraction methods, investigation in this work is to explore the possibility of stabilizing video while remaining agnostic to the scene. One such possibility is stabilizing the video by manipulating the coefficients of a basis of the signal subspace, the smallest subspace containing the video. We show a sufficient condition for which stabilization can be performed in the signal subspace of an unstabilized video. The sufficient condition is derived by developing a novel state-space model that describes a short-length video sequence as a discrete-time state-space model. The properties of matrices corresponding to the state model are analyzed, and the corresponding results are utilized in deriving the sufficient condition. Simulation videos and experimental videos collected from a UAV validate the proposed sufficient condition.

摄像头是监控机器人中无处不在的传感器。通常，相机会受到来自外部因素或内在动力的意外运动。预期的摄像机运动为视频序列的短片段诱导恒定的流矢量。视频稳定是从视频序列中去除非预期的摄像机运动的影响。监控机器人通常在室内工作，在室内运动估计的特征很少，使得传统的视频稳定方法不可靠。与特征/运动提取方法不同，这项工作的研究是探索稳定视频的可能性，同时保持对场景的不可知论。其中一种可能性是通过操纵信号子空间（包含视频的最小子空间）的基的系数来稳定视频。给出了在不稳定视频的信号子空间中实现稳定的一个充分条件。通过开发一种新的状态空间模型，将短视频序列描述为离散时间状态空间模型，推导出充分条件。分析了状态模型所对应的矩阵的性质，并利用相应的结果推导了状态模型的充分条件。从无人机上采集的仿真视频和实验视频验证了所提出的充分条件。

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引用次数: 0

Energy harvesting aware path planning for ambiently-powered multi-robot systems 环境动力多机器人系统能量收集感知路径规划

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

Robotics and Autonomous Systems

Pub Date : 2025-12-02 DOI: 10.1016/j.robot.2025.105260

Mohmmadsadegh Mokhtari , Bram Vanderborght , Jeroen Famaey

Autonomous multi-robot systems are increasingly deployed in energy-variable environments where sustained operation depends on efficient energy harvesting. Traditional path-planning methods overlook ambient energy variability and inter-robot coordination, reducing overall efficiency. This paper introduces Hierarchically Conditioned Multi-Agent Reinforcement Learning with Meta-Differential Evolution (H-CMARL-DE) for energy harvesting-aware multi-robot path planning. The method centralizes only meta-level parameters priority order, shaping weights, and subgoal settings, while policy learning and execution remain decentralized, ensuring scalability and safety. Implemented in ROS–Gazebo, the system operates in a closed-loop observe–plan–act cycle, enabling robots to coordinate via hierarchical time–space reservations while adapting to dynamic obstacles and energy fields. Simulations demonstrate up to 240% improvement in energy-harvesting efficiency with minimal increase in path length compared to non-energy-harvesting-aware approaches, confirming H-CMARL-DE’s robustness and adaptability for long-term cooperative operation in resource-constrained environments.

自主多机器人系统越来越多地部署在能量可变的环境中，在这些环境中，持续的操作依赖于有效的能量收集。传统的路径规划方法忽略了环境能量的可变性和机器人之间的协调，降低了整体效率。针对能量收集感知多机器人路径规划问题，提出了基于元差分进化的分层条件多智能体强化学习（H-CMARL-DE）方法。该方法只集中元级参数优先顺序、塑造权重和子目标设置，而策略学习和执行仍然分散，确保了可扩展性和安全性。在ROS-Gazebo中实现，该系统以闭环观察-计划-行动循环运行，使机器人能够通过分层时空保留进行协调，同时适应动态障碍物和能量场。仿真表明，与非能量收集感知方法相比，能量收集效率提高了240%，路径长度增加最少，证实了H-CMARL-DE在资源受限环境下长期合作运行的鲁棒性和适应性。

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引用次数: 0

A Multi-UAV unfixed scale task assignment method with uncertainty based on deep Q network 基于深度Q网络的不确定多无人机不固定尺度任务分配方法

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

Robotics and Autonomous Systems

Pub Date : 2025-12-02 DOI: 10.1016/j.robot.2025.105269

Wenqi Ding, Jia Song, Jingwei Yu

Task assignment is a fundamental aspect in optimizing the efficiency of multiple unmanned aerial vehicle (Multi-UAV) systems. This paper presents a data-driven approach for addressing the Multi-UAV unfixed scale task assignment problem (MUSTA) in the presence of uncertain conditions. Firstly, we propose a task allocation model that incorporates uncertainty, thereby simulating the penetration process that may result in losses for unmanned aerial vehicles, and comprehensively evaluating the algorithm’s effectiveness. Secondly, we refine the task allocation strategy in network processing to effectively handle scenarios with variable-scale configurations of both UAVs and targets. By devising appropriate allocation rules, a Q-network can be trained to approximate allocation strategies suitable for diverse UAV-task scales, eliminating the need for network structure reinitialization and training for variable scale problems. Leveraging the characteristics of the allocation problem under study, we design a state–action space and a reward function, and simulation results confirm that this approach not only efficiently provides relatively optimal solutions for different large-scale problems but also acquires allocation strategies that closely resemble real-world application scenarios through training in uncertain environments.

任务分配是优化多无人机系统效率的一个基本方面。提出了一种数据驱动的方法来解决不确定条件下多无人机不固定尺度任务分配问题。首先，提出了一种考虑不确定性的任务分配模型，模拟了无人机突防可能造成损失的过程，并对算法的有效性进行了综合评价。其次，我们改进了网络处理中的任务分配策略，以有效地处理无人机和目标都具有变尺度配置的场景。通过设计适当的分配规则，q -网络可以训练出适合不同无人机任务规模的近似分配策略，从而消除了网络结构重新初始化和对变规模问题的训练。利用所研究分配问题的特点，设计了状态-动作空间和奖励函数，仿真结果表明，该方法不仅能有效地为不同大规模问题提供相对最优的解决方案，而且通过不确定环境下的训练，获得了与现实应用场景非常相似的分配策略。

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引用次数: 0

Deterministic delay-aware reinforcement learning 确定性延迟感知强化学习

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

Robotics and Autonomous Systems

Pub Date : 2025-12-02 DOI: 10.1016/j.robot.2025.105271

Sathira Dilshan Bataduwaarachchi , Zoran Najdovski , Hieu Trinh , Chee Peng Lim , Van Thanh Huynh

Reinforcement Learning (RL) has effectively paved the way in achieving robotic control during the past decade. As a result, the avenue of integrating RL-powered robotic control and teleoperation has caught the attention of researchers. Every RL framework involves the basis of suitable observation and action communication between the environment and the agent, and the involvement of teleoperation can introduce random time delays within the said communication process. Achieving robotic control under such constraints remains an untapped area in the domain of reinforcement learning. We take the initiative to achieve the goal of robotic control while handling delays in the RL setting based on a fitting Markov Decision Process (MDP) structure. Our algorithm will learn a deterministic policy and can tackle control environments, especially robotic manipulation environments, using observations with proprioceptive information. We methodically present the theoretical adjustments based on an existing dominant off-policy algorithm to express the algorithm’s competency with proof of convergence. We perform experimentations with DeepMind Control Suite, illustrating significant results showing the algorithm’s capabilities in learning complex environments powered by delay-aware RL.

在过去的十年中，强化学习（RL）有效地为实现机器人控制铺平了道路。因此，将rl驱动的机器人控制和远程操作相结合的途径引起了研究人员的注意。每个强化学习框架都涉及到环境与智能体之间适当的观察和行动通信的基础，遥操作的参与会在上述通信过程中引入随机的时间延迟。在这种约束下实现机器人控制仍然是强化学习领域的一个未开发的领域。我们主动实现机器人控制的目标，同时处理基于拟合马尔可夫决策过程（MDP）结构的RL设置中的延迟。我们的算法将学习一个确定性策略，并可以处理控制环境，特别是机器人操作环境，使用本体感知信息的观察。我们系统地提出了基于现有的优势离策略算法的理论调整，以表达算法的收敛性证明。我们使用DeepMind Control Suite进行了实验，结果显示了该算法在学习由延迟感知强化学习驱动的复杂环境中的能力。

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引用次数: 0

Decentralized formation control, collision avoidance and global connectivity maintenance using non-smooth barrier functions 分散式编队控制、避碰和使用非平滑屏障函数的全局连通性维护

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

Robotics and Autonomous Systems

Pub Date : 2025-12-01 DOI: 10.1016/j.robot.2025.105272

Pranjal Bhatia , Sayan Basu Roy , P.B. Sujit , Luis Mejias Alvarez , Aaron McFadyen

This paper presents a decentralized control framework for multi-agent systems that simultaneously addresses formation control, global connectivity maintenance, and collision avoidance. To achieve these multiple objectives, we employ control barrier functions (CBFs), with each function tailored to a specific safety constraint. However, the combination of multiple CBFs inherently introduces non-smoothness in the system, posing challenges for real-time control. To address this, we adopt non-smooth barrier functions (NBFs), which enable the integration of multiple safety constraints in a decentralized manner while systematically avoiding critical non-smooth regions. The proposed method allows agents to maintain a desired formation, avoid inter-agent and obstacle collisions, and preserve global connectivity without requiring centralized coordination. The results are validated using mathematical analysis and simulations.

本文提出了一个多智能体系统的分散控制框架，该框架同时解决了编队控制、全局连接维护和避免碰撞的问题。为了实现这些多重目标，我们采用了控制屏障函数（cbf），每个函数都针对特定的安全约束进行了定制。然而，多个cbf的组合固有地引入了系统的非平滑性，给实时控制带来了挑战。为了解决这个问题，我们采用了非光滑屏障函数（nbf），它可以以分散的方式集成多个安全约束，同时系统地避免关键的非光滑区域。该方法允许智能体在不需要集中协调的情况下保持理想的队形，避免智能体间和障碍物碰撞，并保持全局连通性。通过数学分析和仿真验证了结果。

引用次数: 0

A generic task model and control strategy to support learning, robust control, and generalization of contact-rich manipulation tasks 一个通用的任务模型和控制策略，以支持学习、鲁棒控制和丰富的接触操作任务的泛化

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

Robotics and Autonomous Systems

Pub Date : 2025-12-01 DOI: 10.1016/j.robot.2025.105270

Ali Mousavi Mohammadi, Maxim Vochten, Erwin Aertbeliën, Joris De Schutter

Learning and controlling contact-rich tasks are based on a task model that is built from motion and wrench data collected from demonstration. In previous work, the procedure for choosing a proper reference frame to express the learned signals is often implicit, requires expert insight, or starts from proposed frame candidates. Furthermore, the exact location of the geometric features in the world or tool that determine this frame may be uncertain during task demonstration or execution. This article presents an automatic method to derive the optimal reference frame for the task model directly from recorded motion and wrench data. This frame maximizes decoupling between the learned signals, enabling the design of a decoupled task controller. The method is founded on screw theory, entirely probabilistic and requires no hyperparameters. The method is generic as it works regardless of whether the task involves translation, rotation, force, or moment, or any combination thereof. While other control approaches could be used, we propose a generic, decoupled, constraint-based and velocity-resolved controller to work in tandem with the derived task model. It involves a small number of task-dependent control parameters that need to be tuned. The derivation of the task model, the robustness of the controller, and the task generalization capabilities of their combination were validated for various tasks involving contact between rigid objects, including surface following and manipulation of articulated objects. The results show a good agreement between derived and assumed expert task frames as well as the effectiveness and versatility of the overall approach.

学习和控制丰富的接触任务是基于一个任务模型，该模型是由运动和扳手数据从演示中收集而来。在以前的工作中，选择合适的参考框架来表达学习到的信号的过程通常是隐含的，需要专家的洞察力，或者从提出的候选框架开始。此外，在任务演示或执行期间，确定该框架的几何特征在世界或工具中的确切位置可能是不确定的。本文提出了一种直接从记录的运动和扳手数据中自动导出任务模型最优参考系的方法。该框架最大限度地解耦了学习到的信号，使解耦任务控制器的设计成为可能。该方法建立在螺旋理论的基础上，完全是概率性的，不需要超参数。该方法是通用的，因为无论任务是否涉及平移、旋转、力或力矩，或它们的任何组合，它都可以工作。虽然可以使用其他控制方法，但我们提出了一种通用的、解耦的、基于约束的和速度分辨的控制器来与派生的任务模型协同工作。它涉及少量需要调优的任务相关控制参数。任务模型的推导、控制器的鲁棒性以及它们组合的任务泛化能力在涉及刚性物体接触的各种任务中得到了验证，包括表面跟踪和铰接物体的操作。结果表明，导出的专家任务框架和假设的专家任务框架之间有很好的一致性，以及整体方法的有效性和通用性。

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引用次数: 0

Fast analytical path planning method for mill reline manipulator in confined environment 受限环境下磨线机械臂快速解析路径规划方法

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

Robotics and Autonomous Systems

Pub Date : 2025-11-29 DOI: 10.1016/j.robot.2025.105266

Mingyuan Wang , Yujun Xue , Jishun Li , Shuai Li , Yunhua Bai

Currently, few studies focus on the mill reline manipulator’s path planning, which is a special industrial equipment working inside grinding mill. Based on the geometric analysis and space vector method, this paper proposed a fast analytical path planning method (FAPP) for mill reline manipulator in confined environment. First, the signed distance field and sparse check points are designed for collision detection. Second, the quintic polynomial function is introduced to obtain the path pre-planning sequence. Third, to adjust the collision-containing path sequence, three collision avoidance strategies are proposed to calculate the analytical collision-free paths. Furthermore, the sample dataset is also established according to the distribution of liner installation. Effectiveness, comparison, and robustness experiments are carried out to verify the superiority of the proposed method. In terms of computational efficiency, energy cost, and motion smoothness, the proposed FAPP achieves outstanding performance and can be further improved even under non-ideal conditions.

磨线机械手是一种工作在磨机内部的特殊工业设备，目前对其路径规划研究较少。基于几何分析和空间矢量法，提出了一种密闭环境下磨线机械手的快速解析路径规划方法。首先，设计了带符号距离域和稀疏检查点进行碰撞检测；其次，引入五次多项式函数得到路径预规划序列；第三，为了调整包含碰撞的路径序列，提出了三种避碰策略来计算解析无碰撞路径。在此基础上，根据尾管安装的分布，建立了样本数据集。通过有效性、对比和鲁棒性实验验证了该方法的优越性。在计算效率、能量成本和运动平滑性方面，所提出的FAPP取得了出色的性能，即使在非理想条件下也可以进一步改进。

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引用次数: 0

Prior-Backbone-Prediction cascade depth completion framework for robotic grasp oriented to transparent objects 面向透明目标的机器人抓取先验-主干预测级联深度补全框架

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

Robotics and Autonomous Systems

Pub Date : 2025-11-28 DOI: 10.1016/j.robot.2025.105268

Hongkun Tian , Shuo Liu , Shuai Liu , Jun Li , Ling Tong , Yi Man

Transparent objects are frequent manipulation objects for industrial production and manufacturing systems. The texture-less characteristics of transparent objects lead to insignificant RGB image features and incomplete and inaccurate Depth images, which significantly challenge robotic grasping detection and potentially dangerous for independent and safe manipulation in situations such as industrial production and manufacturing system. Reconstruction of depth data by fusing RGB-Depth (RGB-D) data through depth completion is the mainstream solution as a pre-processing robot-acquired data. Therefore, the quality, as well as the efficiency of depth completion, determines the effectiveness of the robot grasping transparent objects. However, the no-prior processing used in existing studies leads to an unimpressive depth completion. In addition, existing studies employ one-way prediction results and do not address the issue of image distortion. In general, current studies are not ideal in balancing accuracy and efficiency. To this end, we propose a Prior-Backbone-Prediction (PBP) cascade lightweight depth completion framework, named PBP framework. Prior: the proposed Reverse Texture Prior Guidance strategy guides the backbone network to focus on texture-less regions better. Backbone: the proposed Pyramid Interactive Residual Backbone network is used for in-depth feature learning. Prediction: the proposed consistency-difference Joint Prediction strategy maximizes the accuracy of prediction results and reduces distortion. The dual validation is performed on both public datasets and robotic platforms. PBP achieves state-of-the-art performance on the well-known TransCG dataset with a model size of only 17.2 M. The success rate of the Aubo i5 robot platform is improved by 44 % based on GG-CNN to grasp transparent objects.

透明对象是工业生产和制造系统中常用的操作对象。透明物体的无纹理特征导致RGB图像特征不明显，深度图像不完整和不准确，这给机器人抓取检测带来了极大的挑战，并对工业生产和制造系统等情况下的独立安全操作带来了潜在的危险。通过深度完井融合RGB-Depth （RGB-D）数据重建深度数据是机器人采集数据预处理的主流解决方案。因此，深度完成的质量和效率决定了机器人抓取透明物体的有效性。然而，现有研究中使用的无先验处理导致深度完成效果不佳。此外，现有的研究采用单向预测结果，没有解决图像失真的问题。总的来说，目前的研究在平衡准确性和效率方面并不理想。为此，我们提出了一个Prior-Backbone-Prediction （PBP）级联轻量级深度完井框架，命名为PBP框架。先验：提出的反向纹理先验指导策略可以更好地引导骨干网关注无纹理的区域。骨干网：提出的金字塔交互残差骨干网用于深度特征学习。预测：提出的一致性-差异联合预测策略使预测结果的准确性最大化，减少了失真。双重验证在公共数据集和机器人平台上进行。PBP在著名的TransCG数据集上达到了最先进的性能，模型大小仅为17.2 M.基于GG-CNN的Aubo i5机器人平台抓取透明物体的成功率提高了44%。

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引用次数: 0

AUV decision-making in bistatic sonar target tracking via deep reinforcement learning 基于深度强化学习的双基地声纳目标跟踪中的AUV决策

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

Robotics and Autonomous Systems

Pub Date : 2025-11-26 DOI: 10.1016/j.robot.2025.105262

Weicong Zhan , Yu Tian , Feng Zheng , Qiming Sang , Jiancheng Yu

The maneuvering decisions of an Autonomous Underwater Vehicle (AUV) equipped with a sonar receiver are critical for acquiring informative measurements, thereby enhancing target tracking accuracy and maintaining persistent track continuity. This study investigates a bistatic sonar configuration comprising a stationary acoustic source and a mobile AUV carrying a linear sonar array, with a focus on real-time generation of optimized heading commands for the AUV. To address the practical limitations of conventional model-based strategies under stringent decision-time constraints, a deep reinforcement learning (DRL) framework is employed to derive an efficient decision policy. Implemented using the Proximal Policy Optimization algorithm, the DRL policy integrates with a target tracker by utilizing estimated target states and associated uncertainties as inputs. A reward function based on the Optimal Sub-Pattern Assignment metric is designed to guide policy learning. To train the policy, a comprehensive simulation environment is developed, incorporating target properties, acoustic propagation characteristics, and bistatic measurement models. Once trained, the DRL-based policy effectively guides the AUV in real-time to establish favorable bistatic configurations and enhance tracking performance. Comparative Monte Carlo simulations against a state-of-the-art model-based receding horizon control method demonstrate that the DRL-based approach yields a 14.5 % average improvement in tracking accuracy. Detailed analyses of AUV behaviors under the learned policy, along with robustness and generalization evaluations, underscore the practical potential of the proposed approach for real-world deployment in bistatic sonar target tracking scenarios.

装备声呐接收器的自主水下航行器（AUV）的机动决策对于获取信息测量，从而提高目标跟踪精度和保持持久的跟踪连续性至关重要。本研究研究了一种双基地声纳配置，包括一个固定声源和一个携带线性声纳阵列的移动AUV，重点是为AUV实时生成优化的航向命令。为了解决传统的基于模型的策略在严格的决策时间约束下的实际局限性，采用深度强化学习（DRL）框架来推导有效的决策策略。DRL策略使用近端策略优化算法实现，通过利用估计的目标状态和相关的不确定性作为输入，与目标跟踪器集成。设计了一个基于最优子模式分配度量的奖励函数来指导策略学习。为了训练该策略，开发了一个综合仿真环境，包括目标特性、声传播特性和双基地测量模型。经过训练后，基于drl的策略可以有效地实时引导AUV建立有利的双基地配置，提高跟踪性能。蒙特卡罗仿真与最先进的基于模型的后退地平线控制方法的对比表明，基于drl的方法在跟踪精度方面平均提高了14.5%。对学习策略下AUV行为的详细分析，以及鲁棒性和泛化评估，强调了该方法在双基地声纳目标跟踪场景中实际部署的实际潜力。

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引用次数: 0

Physics-informed LSTM-based delay compensation framework for high-fidelity teleoperation of wheeled mobile robots 基于物理信息lstm的轮式移动机器人高保真遥操作延迟补偿框架

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

Robotics and Autonomous Systems

Pub Date : 2025-11-26 DOI: 10.1016/j.robot.2025.105265

Ahmad Abubakar , Yahya Zweiri , Abdel Gafoor Haddad , Mubarak Yakubu , Ruqayya Alhammadi , Lakmal Seneviratne

Bilateral teleoperation of wheeled mobile robots (WMRs) traversing soft terrains with interaction force feedback is crucial for applications like lunar exploration, as it help regulates travel reduction and path deviation. However, network-induced communication delays present a challenge in achieving high-fidelity closed-loop integration, degrading slippage awareness and command-tracking performance. To address this, a physics-informed long short-term memory (PiLSTM)-based predictor framework is proposed to predicts intended commands and anticipated slippage feedback in WMR teleoperation systems under large delays. Conventional model-free predictors often demonstrate significant prediction errors when compensating for large delays in complex dynamics systems due to their limited adaptability and generalizability. In contrast, the proposed PiLSTM predictor integrates an LSTM network with time-delay dynamics and a feedback mechanism to better capture strong nonlinear and temporal dynamics for improved adaptability and generalizability while ensuring accurate prediction. A series of human-in-the-loop experiments for a track-following task were conducted to validate the effectiveness of the PiLSTM framework in compensating for the large delays, similar to those encountered in lunar exploration. Its performance was compared against first-order time-delay (FOD) and wave variable transformation (WV) approaches for delay compensation. The results demonstrate the robustness of the PiLSTM framework across various test scenarios, achieving up to 29.7% improvement in prediction accuracy compared to the FOD framework. Furthermore, the proposed PiLSTM-based teleoperation system achieved significantly improved tracking performance and force transparency compared to FOD- and WV-based systems, ensuring higher fidelity and more robust closed-loop integration. These findings highlight the PiLSTM framework’s adaptability and generalizability for complex teleoperation systems. A supplementary video is available at https://youtu.be/dMVLEV7qTvE.

轮式移动机器人（WMRs）在软地形上的双向遥操作与交互力反馈对于月球探测等应用至关重要，因为它有助于调节行程减少和路径偏差。然而，网络引起的通信延迟对实现高保真闭环集成提出了挑战，降低了滑动感知和命令跟踪性能。为了解决这个问题，提出了一种基于物理信息的长短期记忆（PiLSTM）预测框架，用于预测大延迟下WMR远程操作系统的预期命令和预期滑动反馈。传统的无模型预测器由于其有限的适应性和泛化性，在补偿复杂动力学系统的大延迟时往往表现出显著的预测误差。相比之下，本文提出的PiLSTM预测器集成了具有时滞动力学和反馈机制的LSTM网络，可以更好地捕获强非线性和时间动力学，从而在保证准确预测的同时提高适应性和泛化性。为了验证PiLSTM框架在补偿大延迟（类似于月球探测中遇到的延迟）方面的有效性，进行了一系列轨道跟踪任务的人在环实验。将其性能与一阶时延（FOD）和波动变量变换（WV）方法进行了比较。结果证明了PiLSTM框架在各种测试场景中的鲁棒性，与FOD框架相比，预测精度提高了29.7%。此外，与基于FOD和wv的系统相比，所提出的基于pilstm的远程操作系统实现了显著改善的跟踪性能和力透明度，确保了更高的保真度和更强大的闭环集成。这些发现突出了PiLSTM框架对复杂远程操作系统的适应性和通用性。补充视频可在https://youtu.be/dMVLEV7qTvE上获得。

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引用次数: 0