Robotics and Autonomous Systems最新文献_第5页

Curriculum-guided deep reinforcement learning with fuzzy rewards for autonomous push-grasp manipulation in cluttered environments 基于模糊奖励的课程引导深度强化学习在混乱环境下的自主推握操作

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

Robotics and Autonomous Systems

Pub Date : 2026-04-01 Epub Date: 2025-12-31 DOI: 10.1016/j.robot.2025.105325

Chih-Yung Huang , Adhan Efendi , Ying-Chun Wang

Robotic manipulation in cluttered environments requires robust coordination of pushing and grasping to overcome occlusions, constrained grasp geometries, and uncertain object interactions. This study presents a curriculum-guided deep reinforcement learning framework that jointly redesigns the training distribution, state abstraction, and reward structure for autonomous push–grasp manipulation. A depth-aware grasp potential module constructs a geometric affordance map that prioritizes feasible top-layer grasp opportunities, guiding the agent toward collision-free rearrangement behaviors. A fuzzy logic–based reward mechanism integrates changes in graspable area and grasp Q-values into a continuous shaping signal, addressing sparse feedback and stabilizing learning. A stage-wise curriculum with proportion-controlled difficulty progression gradually increases clutter density and object difficulty, enabling progressive acquisition of coordinated push–grasp skills. Extensive evaluations across randomized clutter, structured challenge scenarios, and real-world experiments on previously unseen and semi-transparent objects show that the proposed framework consistently outperforms VPG-based and grasp-quality baselines in grasp success and action efficiency. These results demonstrate the effectiveness of coupling curriculum design, depth-aware grasp prioritization, and fuzzy reward shaping for robust manipulation in complex, cluttered settings.

在混乱的环境中，机器人操作需要强大的推动和抓取协调，以克服遮挡、受限的抓取几何形状和不确定的物体相互作用。本研究提出了一个课程导向的深度强化学习框架，该框架共同重新设计了自主推握操作的训练分布、状态抽象和奖励结构。深度感知抓取潜力模块构建几何功能映射，优先考虑可行的顶层抓取机会，引导智能体进行无碰撞重排行为。基于模糊逻辑的奖励机制将可抓握面积和抓握q值的变化整合为一个连续的成形信号，解决了稀疏反馈和稳定学习的问题。一个阶段明智的课程与比例控制的难度进展逐渐增加杂乱密度和对象的难度，使逐步获得协调推抓技能。对随机杂波、结构化挑战场景的广泛评估，以及对以前看不见和半透明物体的真实世界实验表明，所提出的框架在抓取成功率和动作效率方面始终优于基于vpg和抓取质量基线。这些结果证明了耦合课程设计、深度感知掌握优先级和模糊奖励塑造在复杂、混乱环境下的鲁棒操作的有效性。

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引用次数: 0

AdaRRT: A novel adaptive path planning algorithm for mobile robots in complex terrain environments AdaRRT：一种复杂地形环境下移动机器人自适应路径规划算法

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

Robotics and Autonomous Systems

Pub Date : 2026-04-01 Epub Date: 2026-01-12 DOI: 10.1016/j.robot.2026.105354

Yuqing Chen , Haotong Yan , Shiyu Li , Shunqing Yang , Huosheng Hu

Path planning for mobile robots in complex environments has attracted increasing attention. However, the classical RRT and its variants still suffer from low sampling efficiency, unsafe paths close to obstacles, and susceptibility to local optima. Recently proposed methods, such as Adapted-RRT, QS-RRT*, and PQ-RRT*, partially address these issues but rely on complex operators and parameter tuning, leading to high computational costs and poor real-time suitability. To address these limitations, we propose an Adaptive RRT (AdaRRT). Its qualitative advantages include adaptive exploration via dynamic sampling adjustment, improved safety through obstacle-avoiding growth, enhanced robustness with local-optima escape, and smoother paths by tangent-based optimization. Quantitatively, simulations show that AdaRRT reduces search time by up to 91.14%, shortens path length by 20-33%, and decreases node count by over 70%. Real-world experiments further validate its efficiency and safety. Overall, AdaRRT outperforms existing methods in efficiency, safety, and robustness, offering a practical solution for autonomous navigation in complex environments.

复杂环境下移动机器人的路径规划问题越来越受到人们的关注。然而，经典的RRT及其变体仍然存在采样效率低、靠近障碍物的路径不安全、易受局部最优的影响等问题。最近提出的方法，如adaptive - rrt、QS-RRT*和PQ-RRT*，部分解决了这些问题，但依赖于复杂的算子和参数调优，导致计算成本高，实时性差。为了解决这些限制，我们提出了自适应RRT （AdaRRT）。它的定性优势包括通过动态采样调整进行自适应探索，通过避障生长提高安全性，通过局部最优逃逸增强鲁棒性，以及通过切线优化实现路径平滑。定量仿真结果表明，AdaRRT算法的搜索时间缩短了91.14%，路径长度缩短了20-33%，节点数减少了70%以上。实际实验进一步验证了该方法的有效性和安全性。总的来说，AdaRRT在效率、安全性和鲁棒性方面优于现有方法，为复杂环境下的自主导航提供了实用的解决方案。

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引用次数: 0

RPV-Map: A novel Reflection-Planes Voxel Map for glass and mirror reconstruction on SLAM System RPV-Map：一种用于SLAM系统玻璃和镜子重建的反射平面体素图

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

Robotics and Autonomous Systems

Pub Date : 2026-04-01 Epub Date: 2025-12-19 DOI: 10.1016/j.robot.2025.105308

Hao-Tien Yeh , Jun-Jie Hu , Fu-Hao Chang , Kuan-Ting Lin , Li-Chen Fu

This paper presents a novel reflection-plane voxel map approach that effectively detects and reconstructs reflective objects during SLAM by integrating geometric and visual information from 3D LiDAR and camera. The method first estimates rough reflection planes from the voxel map and extracts 3D boundary and surface points through intensity-based point cloud classification. Using 2D Alpha Shape boundary extraction and multi-view reflection mask validation, we achieve reliable geometric feature extraction. Our proposed plane optimization method considers weighted distributions of different point types and employs boundary refinement to improve reconstruction accuracy. Experimental results demonstrate that our approach outperforms existing methods in terms of plane accuracy, reconstruction completeness, and point cloud classification while achieving real-time performance.

本文提出了一种新的反射平面体素图方法，通过集成三维激光雷达和相机的几何信息和视觉信息，有效地检测和重建SLAM过程中的反射物体。该方法首先从体素图中估计粗略的反射平面，然后通过基于强度的点云分类提取三维边界和表面点。通过二维Alpha Shape边界提取和多视点反射掩模验证，实现了可靠的几何特征提取。我们提出的平面优化方法考虑了不同点类型的加权分布，并采用边界细化来提高重建精度。实验结果表明，该方法在实现实时性的同时，在平面精度、重建完整性和点云分类方面优于现有方法。

引用次数: 0

Reference-guided image inpainting via progressive feature interaction and reconstruction for mobile robots with binocular cameras 基于渐进式特征交互与重构的双目移动机器人参考引导图像绘制

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

Robotics and Autonomous Systems

Pub Date : 2026-04-01 Epub Date: 2025-12-26 DOI: 10.1016/j.robot.2025.105320

Jingyi Liu , Hengyu Li , Hang Liu , Shaorong Xie , Jun Luo

Image inpainting is a critical technique for recovering missing information caused by camera soiling on mobile robots. However, most existing learning-based methods still struggle to handle damaged images with complex semantic environments and diverse hole patterns, primarily because of the insufficient acquisition and inadequate fusion of scene-consistent prior cues for damaged images. To address this limitation, we propose a novel reference-guided image inpainting network (

{RGI}^{2} N

) for mobile robots equipped with binocular cameras, which employs adjacent camera images as inpainting guidance and fuses its prior information via progressive feature interaction to reconstruct damaged regions. Specifically, a back-projection-based feature interaction module (FIM) is proposed to align the features of the reference and damaged images, thereby capturing the contextual information of the reference image for inpainting. Additionally, a content reconstruction module (CRM) based on residual learning and channel attention is presented to selectively aggregate interactive features for reconstructing missing details. Building upon these two modules, we further devise a progressive feature interaction and reconstruction module (PFIRM) that organizes multiple FIM-CRM pairs into a stepwise structure, enabling the progressive fusion of multiscale contextual information derived from both the damaged and reference images. Moreover, a feature refinement module (FRM) is developed to interact with low-level fine-grained features and refine the reconstructed details. Extensive evaluations conducted on the public ETHZ dataset and our self-built MII dataset demonstrate that

{RGI}^{2} N

outperforms other state-of-the-art approaches and produces high-quality inpainting results on real soiled data.

图像补漆是修复移动机器人因相机污染而导致的信息缺失的关键技术。然而，大多数基于学习的方法仍然难以处理具有复杂语义环境和多种孔洞模式的受损图像，主要原因是对受损图像的场景一致性先验线索的获取和融合不足。为了解决这一限制，我们提出了一种新的参考引导图像修复网络（RGI2N），用于配备双目摄像机的移动机器人，该网络采用相邻摄像机图像作为修复引导，并通过渐进特征交互融合其先验信息来重建受损区域。具体而言，提出了一种基于反投影的特征交互模块（FIM），将参考图像和受损图像的特征对齐，从而捕获参考图像的上下文信息进行修复。此外，提出了一个基于残差学习和通道关注的内容重构模块（CRM），选择性地聚合交互特征以重建缺失的细节。在这两个模块的基础上，我们进一步设计了一个渐进式特征交互和重建模块（PFIRM），该模块将多个FIM-CRM对组织成一个逐步结构，从而实现来自损坏图像和参考图像的多尺度上下文信息的渐进式融合。此外，开发了特征细化模块（FRM），与底层细粒度特征交互，对重构细节进行细化。对公共ETHZ数据集和我们自建的MII数据集进行的广泛评估表明，RGI2N优于其他最先进的方法，并在实际污染数据上产生高质量的喷漆结果。

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引用次数: 0

Hybrid attention-guided RRT*: Learning spatial sampling priors for accelerated path planning 混合注意引导RRT*：学习空间采样先验加速路径规划

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

Robotics and Autonomous Systems

Pub Date : 2026-04-01 Epub Date: 2026-01-09 DOI: 10.1016/j.robot.2026.105338

Asmaa Loulou , Mustafa Unel

Sampling-based planners such as RRT* are widely used for motion planning in high-dimensional and complex environments. However, their reliance on uniform sampling often leads to slow convergence and inefficiency, especially in scenarios with narrow passages or long-range dependencies. To address this, we propose HAGRRT*, a Hybrid Attention-Guided RRT* algorithm that learns to generate spatially informed sampling priors. Our method introduces a new neural architecture that fuses multi-scale convolutional features with a lightweight cross-attention mechanism, explicitly conditioned on the start and goal positions. These features are decoded via a DPT-inspired module to produce 2D probability maps that guide the sampling process. Additionally, we propose an obstacle-aware loss function that penalizes disconnected and infeasible predictions which further encourages the network to focus on traversable, goal-directed regions. Extensive experiments on both structured (maze) and unstructured (forest) environments show that HAGRRT* achieves significantly faster convergence and improved path quality compared to both classical RRT* and recent deep-learning guided variants. Our method consistently requires fewer iterations and samples and is able to generalize across varying dataset types. On structured scenarios, our method achieves an average reduction of 39.6% in the number of samples and an average of 24.4% reduction in planning time compared to recent deep learning methods. On unstructured forest maps, our method reduces the number of samples by 71.5%, and planning time by 81.7% compared to recent deep learning methods, and improves the success rate from 67% to 93%. These results highlight the robustness, efficiency, and generalization ability of our approach across a wide range of planning environments.

基于采样的规划器，如RRT*，广泛用于高维和复杂环境中的运动规划。然而，它们对统一采样的依赖往往导致缓慢的收敛和低效率，特别是在狭窄通道或长期依赖的情况下。为了解决这个问题，我们提出了HAGRRT*，这是一种混合注意引导RRT*算法，它可以学习生成空间信息采样先验。我们的方法引入了一种新的神经结构，它融合了多尺度卷积特征和轻量级的交叉注意机制，明确地以起点和目标位置为条件。这些特征通过dpt启发的模块解码，以产生指导采样过程的二维概率图。此外，我们提出了一个障碍感知损失函数，该函数惩罚断开和不可行的预测，从而进一步鼓励网络关注可遍历的目标导向区域。在结构化（迷宫）和非结构化（森林）环境中进行的大量实验表明，与经典RRT*和最近的深度学习引导变体相比，HAGRRT*实现了显著更快的收敛和更好的路径质量。我们的方法始终需要更少的迭代和样本，并且能够跨不同的数据集类型进行推广。在结构化场景中，与最近的深度学习方法相比，我们的方法平均减少了39.6%的样本数量，平均减少了24.4%的规划时间。在非结构化森林地图上，与目前的深度学习方法相比，我们的方法减少了71.5%的样本数量，减少了81.7%的规划时间，并将成功率从67%提高到93%。这些结果突出了我们的方法在广泛的规划环境中的鲁棒性、效率和泛化能力。

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引用次数: 0

Adaptive artificial potential field method for small autonomous vehicles 小型自动驾驶汽车自适应人工势场法

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

Robotics and Autonomous Systems

Pub Date : 2026-04-01 Epub Date: 2026-01-23 DOI: 10.1016/j.robot.2026.105364

Kemal Ihsan Kilic, Aurélien Desoeuvres, Casper Bak Pedersen, Alex Elkjær Vasegaard, Peter Nielsen

Artificial Potential Field (APF) is one of the path planning and obstacle avoidance methods used for its simplicity and effectiveness. The goal’s attractive force and the obstacles’ repulsive forces are modeled and considered to act upon the vehicle or robot. However, in practice, the classical APF faces challenges like local minima. We propose to study several enhancements and their combinations in order to show how they can create even more efficient algorithms to overcome these challenges. Those enhancements include tangential force, inertia-inspired force, and a local minima detection (l.m.d.) and reaction scheme by adding virtual obstacles and dynamically changing coefficients. All of them keep the methods as lightweight as possible while improving the classical APF. The tangential force provides smoother paths and avoids local minima cases. The dynamic change of APF’s parameters, coupled with the addition of virtual obstacles when detecting local minima, provides an efficient way to escape them. The inertia-inspired force can be used to smooth the trajectory when only obstacles in front of the vehicle are taken into account. We defined performance metrics to assess the path completion, path quality, and processing time to compare the proposed enhancements with the base case of classical APF. We benchmarked the proposed methods in different environments for a holonomic robot and a simplified bicycle. The proposed adaptive APF with inertial force extension completed 87.5% of the tests while the classical APF completed only 43.8% of them. On the other side, tangential versions of APF reduce the path length deviation by 8% and the curvature by 20% in simple cases. The code is available on: https://github.com/Glawal/APFproject/tree/paper1.

人工势场（Artificial Potential Field， APF）是一种简单有效的路径规划和避障方法。对目标的吸引力和障碍物的排斥力进行建模，并考虑它们对车辆或机器人的作用。然而，在实际应用中，经典的有源滤波器面临着局部最小值等挑战。我们建议研究几种增强及其组合，以展示它们如何创建更有效的算法来克服这些挑战。这些改进包括切向力，惯性激励力，以及通过添加虚拟障碍物和动态变化系数的局部最小检测（l.m.d.）和反应方案。所有这些方法都在改进经典APF的同时尽可能地保持轻量级。切向力提供了更平滑的路径，避免了局部极小的情况。APF参数的动态变化，加上在检测局部极小值时添加虚拟障碍物，提供了一种有效的逃避方法。在只考虑前方障碍物的情况下，利用惯性激励可以使飞行器的轨迹平滑。我们定义了性能指标来评估路径完成、路径质量和处理时间，以将所提出的增强与经典APF的基本情况进行比较。我们在一个完整机器人和一个简化的自行车的不同环境中对所提出的方法进行了基准测试。提出的惯性力扩展自适应APF完成了87.5%的测试，而经典APF仅完成了43.8%的测试。另一方面，切向版本的APF在简单情况下减少了8%的路径长度偏差和20%的曲率。代码可在https://github.com/Glawal/APFproject/tree/paper1上获得。

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引用次数: 0

Robust predefined-time target surrounding control for multiple underactuated unmanned surface vessels 多艘欠驱动无人水面舰艇鲁棒预定义时间目标周围控制

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

Robotics and Autonomous Systems

Pub Date : 2026-04-01 Epub Date: 2025-12-18 DOI: 10.1016/j.robot.2025.105309

Xuecheng Zhou , Xuehong Tian , Qingqun Mai , Jing Zhang , Haitao Liu

A robust predefined-time targeted surrounding fault-tolerant controller is proposed in this paper for multiple underactuated unmanned surface vessels (USVs) to rotate uniformly around a target vessel with all errors converging in a predefined time. First, a predefined-time distributed state observer (PTDSO) is designed to observe the states of the target USV, which are passed to other USVs through the communication topology. Second, the case of input saturation is considered, a saturation function is constructed to constrain the control signals, and a predefined-time auxiliary dynamic system is constructed to compensate for the controller. Similarly, the velocities of the USVs are indirectly constrained by constraining the virtual control signals. Third, to enhance the robustness of the controller, the disturbances to which the USV is subjected are divided into two parts: general disturbances and strong disturbances. A predefined-time adaptive fuzzy neural network (FNN) is designed to approximate general environmental disturbances and model uncertainties and actuator faults, and a predefined-time

H_{\infty}

algorithm is designed to suppress strong disturbances. Finally, the stability analysis proves that all the signals within the closed-loop system can converge within a predefined time. Numerical simulations demonstrate the effectiveness of the proposed algorithm.

提出了一种鲁棒的预定义时间目标周围容错控制器，用于多艘欠驱动无人水面舰艇（usv）围绕目标船舶均匀旋转，并在预定义时间内收敛所有误差。首先，设计了一个预定义时间分布式状态观测器（PTDSO）来观察目标USV的状态，并通过通信拓扑将其传递给其他USV。其次，考虑了输入饱和的情况，构造了一个饱和函数来约束控制信号，并构造了一个预定义时间的辅助动态系统来补偿控制器。同样，通过约束虚拟控制信号间接地约束了无人潜航器的速度。第三，为了增强控制器的鲁棒性，将USV所受到的干扰分为一般干扰和强干扰两部分。设计了一种预定义时间自适应模糊神经网络（FNN）来逼近一般环境干扰并对不确定性和执行器故障进行建模，设计了一种预定义时间H∞算法来抑制强干扰。最后，稳定性分析证明了闭环系统内的所有信号都能在预定义的时间内收敛。数值仿真验证了该算法的有效性。

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引用次数: 0

Global navigation-local operation: Pose optimization and multi-sensory guidance strategy for autonomous mobile manipulators 全局导航-局部操作：自主移动机械臂位姿优化与多感官制导策略

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

Robotics and Autonomous Systems

Pub Date : 2026-04-01 Epub Date: 2025-12-20 DOI: 10.1016/j.robot.2025.105307

Dianfan Zhang , Yujie Zhu , Shuhong Cheng , Chao Zhang , Zedai Wang , Shijun Zhang

This paper presents a novel control method for integrating global navigation and local operation in an autonomous mobile operating system (AMOS). A pose optimization method based on null-space projection is proposed to enable seamless switching between global navigation and local operation. Additionally, a control strategy that combines both visual perception and force feedback for effective guidance during local operations is introduced. Firstly, utilizing projected gradient, the task space is partitioned into primary and null spaces, and secondary task functions are designed to optimize and constrain the pose while preserving the primary task. Secondly, a 2-stage guidance strategy for shaft hole insertion, incorporating visual perception and force control techniques, is presented. This strategy involves visual guidance for point cloud registration and fine adjustment using force sensing. To address transient and steady-state errors in the system, a finite-time prescribed performance super-twisting algorithm (PPSTA) controller is proposed and its stability is proven using Lyapunov theory. Simulation and experimental results validate the effectiveness of the proposed method in achieving both navigation and manipulation tasks for mobile manipulators with high performance and robustness.

提出了一种集成自主移动操作系统（AMOS）全局导航和局部操作的新型控制方法。提出了一种基于零空间投影的姿态优化方法，实现了全局导航与局部操作的无缝切换。此外，还介绍了一种结合视觉感知和力反馈的控制策略，用于在局部操作中进行有效制导。首先，利用投影梯度将任务空间划分为主任务空间和零任务空间，设计副任务函数，在保留主任务的前提下对姿态进行优化和约束；其次，提出了一种结合视觉感知和力控技术的两阶段井眼插入制导策略。该策略包括使用力传感对点云配准和精细调整进行视觉引导。为了解决系统的暂态和稳态误差，提出了一种有限时间规定性能的超扭转算法（PPSTA）控制器，并利用李雅普诺夫理论证明了其稳定性。仿真和实验结果验证了该方法在实现移动机械臂导航和操纵任务方面的有效性，并具有良好的鲁棒性和高性能。

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引用次数: 0

Vision-guided gripping process with minimizing folding for flexible fabric materials by integrating a sequential optimization algorithm and FEM analysis 结合序列优化算法和有限元分析的视觉引导柔性织物材料最小折叠夹持过程

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

Robotics and Autonomous Systems

Pub Date : 2026-04-01 Epub Date: 2026-01-07 DOI: 10.1016/j.robot.2026.105349

Minh Khang Ngo , Chanhee Won , HyunKyo Lim , Dae Young Lim , Than Trong Khanh Dat , Jonghun Yoon

Automatic fabric handling in garment manufacturing presents significant challenges due to the soft, deformable, and highly variable nature of textile materials. This paper proposes an integrated robotic fabric gripping system capable of reliably identifying and manipulating fabric parts with minimal folding. The system comprises an industrial robotic arm, four needle grippers mounted on an adjustable jig mechanism, and a high-precision 3D vision camera for real-time fabric detection. A key contribution of this work is a sequential optimization model that determines four optimal gripping points for each fabric pattern, based on material characterization, deformation criteria, and folding definitions derived from experiments and finite element simulations. These points are mapped relative to CAD data and stored for retrieval. A vision-based matching algorithm then aligns real-time image inputs with CAD templates to localize the fabric piece and recover the precomputed optimal gripping points, which are transmitted to the robot for autonomous execution. Quantitative evaluations demonstrate that the proposed approach significantly reduces fabric folding and enhances the reliability of robotic garment handling, representing a substantial step toward fully automated garment production.

由于纺织材料的柔软，可变形和高度可变的性质，服装制造中的自动织物处理提出了重大挑战。提出了一种集成的机器人织物抓取系统，该系统能够以最小的折叠量可靠地识别和操纵织物零件。该系统包括一个工业机械臂、安装在可调夹具机构上的四个抓针器和一个用于实时织物检测的高精度3D视觉相机。这项工作的一个关键贡献是一个顺序优化模型，该模型基于材料特性、变形标准和从实验和有限元模拟中得出的折叠定义，确定每种织物图案的四个最佳夹紧点。这些点相对于CAD数据进行映射并存储以供检索。然后，基于视觉的匹配算法将实时图像输入与CAD模板对齐，以定位织物片并恢复预先计算的最佳抓取点，这些点将传输给机器人进行自主执行。定量评估表明，所提出的方法显著减少了织物折叠，提高了机器人服装处理的可靠性，代表着向全自动服装生产迈出了实质性的一步。

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引用次数: 0

Obstacle crossing in revolute and prismatic knee underactuated biped robots 旋转和移动膝关节欠驱动双足机器人的越障研究

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

Robotics and Autonomous Systems

Pub Date : 2026-04-01 Epub Date: 2026-01-08 DOI: 10.1016/j.robot.2026.105340

Krishnendu Roy , R. Prasanth Kumar

Obstacle crossing is an important ability in biped and humanoid robots that are designed to traverse unstructured terrain. We consider the problem of determining the maximum (a) height, (b) width, (c) cross-sectional area, (d) thin vertical barrier height, and (e) square area of the obstacle that an underactuated biped robot with point-feet can cross while walking slowly. Two different biped robot configurations are compared for obstacle crossing: revolute knee and prismatic knee. The path needed to overcome the obstacle without touching it is determined with the help of binary occupancy grid in the sagittal plane and using genetic algorithm based maximization for each of the five cases, considering thin links as well as thick links for the biped robots. The determined collision free path for obstacle crossing is implemented as a trajectory and demonstrated in dynamic simulation in Mujoco simulation environment. In order to control the position of zero moment point (ZMP) and the ground projection of center of mass for stability, a reaction wheel in the torso is utilized. It is observed that increasing the thicknesses of the biped robot links in general has an effect of reducing the maximum size of the obstacle that can be crossed. Further, prismatic knee biped robot performs better than revolute knee biped robot in crossing large obstacles, especially with thick links. Experiments on a prismatic-knee biped robot further validate the results of GA and MuJoCo simulations.

越障是两足机器人和类人机器人在穿越非结构化地形时的一项重要能力。我们考虑的问题是确定(a)高度，(b)宽度，(c)横截面积，(d)薄垂直障碍物高度，(e)点足双足机器人在缓慢行走时可以穿过的障碍物的平方面积。比较了两种不同的双足机器人构型：旋转膝关节和移动膝关节。通过矢状面上的二元占用网格，结合两足机器人的细连杆和粗连杆，利用基于遗传算法的最大化方法，确定了在不接触障碍物的情况下克服障碍物所需的路径。确定的无碰撞过障路径以轨迹形式实现，并在Mujoco仿真环境中进行了动态仿真验证。为了控制零力矩点的位置和质心的地面投影以保持稳定性，在躯干上设置了反作用轮。我们观察到，一般来说，增加双足机器人连杆的厚度会减小可穿越障碍物的最大尺寸。此外，移动膝关节双足机器人在穿越大型障碍物，特别是粗连杆障碍物时，表现优于旋转膝关节双足机器人。在棱镜膝关节双足机器人上的实验进一步验证了遗传算法和MuJoCo仿真的结果。

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引用次数: 0