Robotica最新文献

Learning robust bipedal running via structured gait and trajectory guidance 通过结构化的步态和轨迹指导学习稳健的两足跑步

4区计算机科学 Q3 ROBOTICS

Robotica

Pub Date : 2026-01-01 DOI: 10.1017/s0263574725103007

Yunpeng Liang, Zhihui Peng, Yanzheng Zhao, Weixin Yan

Abstract Legged robots have demonstrated remarkable potential for dynamic locomotion and terrain adaptability, making them a prominent focus of research. However, achieving robust and agile bipedal running remains challenging due to the complex dynamics of legged locomotion. In this paper, we propose a reinforcement learning framework for robust bipedal running, incorporating a simple reference trajectory generator and an asymmetric actor-critic architecture. The reference generator, based on kinematics, provides diverse trajectory references while preserving key gait characteristics, facilitating efficient policy exploration. To mitigate the simulation-to-reality gap, we extract latent variables encoding environmental and motion information from dual historical observations. Our method simplifies the trajectory generation process while maintaining effective guidance for learning. Extensive simulation and physical experiments demonstrate that, compared to model-based and learning-based baselines, our approach achieves higher agility, more accurate velocity tracking, and stronger disturbance rejection while preserving gait stability. The resulting controller exhibits spring–mass running dynamics that remain robust on both flat and uneven terrains.

摘要腿式机器人在动态运动和地形适应性方面表现出了显著的潜力，成为研究的一个突出热点。然而，由于腿部运动的复杂动力学，实现强健和敏捷的双足跑步仍然具有挑战性。在本文中，我们提出了一个鲁棒双足跑步的强化学习框架，结合了一个简单的参考轨迹生成器和一个不对称的行为者批评架构。参考生成器基于运动学，在保留关键步态特征的同时提供多种轨迹参考，促进有效的策略探索。为了减少模拟与现实的差距，我们从双重历史观察中提取编码环境和运动信息的潜在变量。我们的方法简化了轨迹生成过程，同时保持了对学习的有效指导。大量的仿真和物理实验表明，与基于模型和基于学习的基线相比，我们的方法在保持步态稳定性的同时具有更高的敏捷性，更准确的速度跟踪和更强的抗干扰性。由此产生的控制器显示弹簧质量运行动力学，在平坦和不平坦的地形上都保持稳健。

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引用次数: 0

Human–machine coupling dynamics modeling and adaptive admittance control of lower limb rehabilitation robot 下肢康复机器人人机耦合动力学建模与自适应导纳控制

4区计算机科学 Q3 ROBOTICS

Robotica

Pub Date : 2025-09-24 DOI: 10.1017/s0263574725102300

Chao Gao, Chang Wang, Hui Li, Jianhua Zhang, Xinpeng Du, Jianjun Zhang

Abstract Human–machine compatibility and collaborative control for stroke patients utilizing lower limb rehabilitation robots have attracted considerable research attention. As a highly human–machine-coupled system, ensuring adequate compliance and safety is fundamental to efficient and comfortable rehabilitation. Therefore, this paper first quantifies human–machine contact interactions, proposes a human–machine coupling dynamics modeling method, and identifies the robot’s dynamic inertia parameters and human lower limb parameters. Second, a dual closed-loop controller for the rehabilitation robot is designed. Based on the bottom position control, an adaptive admittance control algorithm is proposed that employs the root-mean-square propagation (RMSprop) algorithm to tune the adaptive gain. In rehabilitation training, the controller can adaptively adjust the admittance parameters according to the human–machine interaction force to achieve responsiveness to the dynamic changes of the human–machine system. The experimental results of the control system show that the human–machine cooperative control performance is significantly improved, the maximum joint angle error is reduced by more than 40.9%, and the maximum human–machine interaction force is reduced by more than 19.4%.

摘要利用下肢康复机器人对脑卒中患者进行人机兼容和协同控制已成为研究热点。作为一个高度人机耦合的系统，确保足够的依从性和安全性是有效和舒适康复的基础。为此，本文首先对人机接触交互进行量化，提出一种人机耦合动力学建模方法，识别机器人的动态惯性参数和人体下肢参数。其次，设计了康复机器人的双闭环控制器。在底部位置控制的基础上，提出了一种采用均方根传播（RMSprop）算法调节自适应增益的自适应导纳控制算法。在康复训练中，控制器可以根据人机交互力自适应调整导纳参数，实现对人机系统动态变化的响应。实验结果表明，该控制系统的人机协同控制性能得到了显著提高，最大关节角误差减小了40.9%以上，最大人机交互力减小了19.4%以上。

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引用次数: 2

Safety supervision framework for legged robots through safety verification and fall protection 通过安全验证和坠落防护的足式机器人安全监管框架

4区计算机科学 Q3 ROBOTICS

Robotica

Pub Date : 2025-04-10 DOI: 10.1017/s026357472500044x

Ming Sun, Gao Yue

Abstract Safety is an essential requirement as well as a major bottleneck for legged robots in the real world. Particularly for learning-based methods, their trial-and-error nature and unexplainable policy have raised widespread concerns. Existing methods usually treat this challenge as a trade-off between safety assurance and task performance. One reason for this drawback stems from the inaccurate inference for the robot’s safety. In this paper, we re-examine the segmentation of the robot’s state space in terms of safety. According to the current state and the prediction of the state transition trajectory, the states of legged robots are classified into safe , recoverable , unsafe , and failure , and a safety verification method is introduced to online infer the robot’s safety. Then, task, recovery, and fall protection policies are trained to ensure the robot’s safety in different states, forming a safety supervision framework independently from the learning algorithm. To validate the proposed method and framework, experiment results are conducted both in the simulation and on the real-world robot, indicating improvements in terms of safety and efficiency.

摘要在现实世界中，安全是腿式机器人的基本要求和主要瓶颈。特别是以学习为基础的方法，它们的试错性质和不可解释的政策引起了广泛的关注。现有方法通常将此挑战视为安全保证和任务性能之间的权衡。造成这种缺陷的一个原因是对机器人安全的推断不准确。在本文中，我们从安全的角度重新审视了机器人状态空间的分割。根据机器人的当前状态和状态转移轨迹的预测，将腿式机器人的状态分为安全、可恢复、不安全和故障，并引入一种安全验证方法在线推断机器人的安全性。然后，训练任务、恢复和跌落保护策略，确保机器人在不同状态下的安全，形成独立于学习算法的安全监督框架。为了验证所提出的方法和框架，在仿真和实际机器人上进行了实验结果，表明在安全性和效率方面有所提高。

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引用次数: 0

Control of stance-leg motion and zero-moment point for achieving perfect upright stationary state of rimless wheel type walker with parallel linkage legs 控制平行联动腿的无轮辋轮式助行器的姿态腿运动和零时刻点，以实现完美的直立静止状态

IF 2.7 4区计算机科学 Q3 ROBOTICS

Robotica

Pub Date : 2024-09-19 DOI: 10.1017/s0263574724001292

Fumihiko Asano, Mizuki Kawai

The authors have studied models and control methods for legged robots without having active ankle joints that can not only walk efficiently but also stop and developed a method for generating a gait that starts from an upright stationary state and returns to the same state in one step for a simple walker with one control input. It was clarified, however, that achieving a perfect upright stationary state including zero dynamics is impossible. Based on the observation, in this paper we propose a novel robotic walker with parallel linkage legs that can return to a perfect stationary standing posture in one step while simultaneously controlling the stance-leg motion and zero-moment point (ZMP) using two control inputs. First, we introduce a model of a planar walker that consists of two eight-legged rimless wheels, a body frame, a reaction wheel, and massless rods and describe the system dynamics. Second, we consider two target control conditions; one is control of the stance-leg motion, and the other is control of the ZMP to stabilize zero dynamics. We then determine the control input based on the two conditions with the target control period derived from the linearized model and consider adding a sinusoidal control input with an offset to correct the resultant terminal state of the reaction wheel. The validity of the proposed method is investigated through numerical simulations.

作者研究了没有活动踝关节的腿部机器人的模型和控制方法，这些机器人不仅能高效行走，还能停止，并开发了一种方法，用于生成一种步态，这种步态从直立静止状态开始，并在一个控制输入的简单步行器中一步返回到相同的状态。然而，要实现包括零动态在内的完美直立静止状态是不可能的。基于这一观点，我们在本文中提出了一种新型并联腿机器人步行器，它能在使用两个控制输入同时控制姿态腿运动和零时刻点（ZMP）的情况下，一步返回到完美的静止站立姿势。首先，我们介绍了一个平面步行器模型，该模型由两个八足无缘轮、一个车身框架、一个反作用轮和无质量杆组成，并描述了系统动力学。其次，我们考虑了两个目标控制条件：一个是对姿态腿运动的控制，另一个是对 ZMP 的控制，以稳定零动态。然后，我们根据线性化模型得出的目标控制周期，确定基于这两个条件的控制输入，并考虑添加带偏移的正弦控制输入，以修正反作用力轮的最终状态。我们通过数值模拟研究了所提方法的有效性。

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引用次数: 0

Trajectory tracking control of a mobile robot using fuzzy logic controller with optimal parameters 使用具有最佳参数的模糊逻辑控制器对移动机器人进行轨迹跟踪控制

IF 2.7 4区计算机科学 Q3 ROBOTICS

Robotica

Pub Date : 2024-09-19 DOI: 10.1017/s0263574724001140

Tesfaye Deme Tolossa, Manavaalan Gunasekaran, Kaushik Halder, Hitendra Kumar Verma, Shyam Sundar Parswal, Nishant Jorwal, Felix Orlando Maria Joseph, Yogesh Vijay Hote

This work investigates the use of a fuzzy logic controller (FLC) for two-wheeled differential drive mobile robot trajectory tracking control. Due to the inherent complexity associated with tuning the membership functions of an FLC, this work employs a particle swarm optimization algorithm to optimize the parameters of these functions. In order to automate and reduce the number of rule bases, the genetic algorithm is also employed for this study. The effectiveness of the proposed approach is validated through MATLAB simulations involving diverse path tracking scenarios. The performance of the FLC is compared against established controllers, including minimum norm solution, closed-loop inverse kinematics, and Jacobian transpose-based controllers. The results demonstrate that the FLC offers accurate trajectory tracking with reduced root mean square error and controller effort. An experimental, hardware-based investigation is also performed for further verification of the proposed system. In addition, the simulation is conducted for various paths in the presence of noise in order to assess the proposed controller’s robustness. The proposed method is resilient against noise and disturbances, according to the simulation outcomes.

这项研究探讨了如何将模糊逻辑控制器（FLC）用于两轮差动驱动移动机器人的轨迹跟踪控制。由于调整 FLC 成员函数的固有复杂性，本研究采用了粒子群优化算法来优化这些函数的参数。为了实现自动化并减少规则库的数量，本研究还采用了遗传算法。通过 MATLAB 仿真验证了所提方法的有效性，仿真涉及多种路径跟踪场景。FLC 的性能与现有控制器进行了比较，包括最小规范解法、闭环逆运动学和基于雅各布转置的控制器。结果表明，FLC 可以提供精确的轨迹跟踪，同时减少均方根误差和控制器的工作量。为了进一步验证所提出的系统，还进行了基于硬件的实验研究。此外，还对存在噪声的各种路径进行了仿真，以评估所提出的控制器的鲁棒性。根据仿真结果，所提出的方法对噪声和干扰具有很强的抵抗力。

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引用次数: 0

High accuracy hybrid kinematic modeling for serial robotic manipulators 串行机器人机械手的高精度混合运动学建模

IF 2.7 4区计算机科学 Q3 ROBOTICS

Robotica

Pub Date : 2024-09-19 DOI: 10.1017/s026357472400136x

Marco Ojer, Ander Etxezarreta, Gorka Kortaberria, Brahim Ahmed, Jon Flores, Javier Hernandez, Elena Lazkano, Xiao Lin

In this study, we present a hybrid kinematic modeling approach for serial robotic manipulators, which offers improved accuracy compared to conventional methods. Our method integrates the geometric properties of the robot with ground truth data, resulting in enhanced modeling precision. The proposed forward kinematic model combines classical kinematic modeling techniques with neural networks trained on accurate ground truth data. This fusion enables us to minimize modeling errors effectively. In order to address the inverse kinematic problem, we utilize the forward hybrid model as feedback within a non-linear optimization process. Unlike previous works, our formulation incorporates the rotational component of the end effector, which is beneficial for applications involving orientation, such as inspection tasks. Furthermore, our inverse kinematic strategy can handle multiple possible solutions. Through our research, we demonstrate the effectiveness of the hybrid models as a high-accuracy kinematic modeling strategy, surpassing the performance of traditional physical models in terms of positioning accuracy.

在这项研究中，我们提出了一种针对串行机器人机械手的混合运动学建模方法，与传统方法相比，这种方法的精度更高。我们的方法将机器人的几何特性与地面实况数据相结合，从而提高了建模精度。所提出的前向运动学模型结合了经典的运动学建模技术和基于精确地面实况数据训练的神经网络。这种融合使我们能够有效地减少建模误差。为了解决逆运动学问题，我们利用前向混合模型作为非线性优化过程中的反馈。与之前的研究不同，我们的计算方法包含了末端效应器的旋转部分，这对于涉及定向的应用（如检测任务）非常有利。此外，我们的逆运动学策略可以处理多种可能的解决方案。通过研究，我们证明了混合模型作为高精度运动学建模策略的有效性，在定位精度方面超越了传统物理模型的性能。

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引用次数: 0

An application of natural matrices to the dynamic balance problem of planar parallel manipulators 自然矩阵在平面平行机械手动态平衡问题中的应用

IF 2.7 4区计算机科学 Q3 ROBOTICS

Robotica

Pub Date : 2024-09-19 DOI: 10.1017/s0263574724001267

Jaime Gallardo-Alvarado

This paper introduces a simplified matrix method for balancing forces and moments in planar parallel manipulators. The method resorts to Newton’s second law and the concept of angular momentum vector, yet it is not necessary to perform the velocity and acceleration analyses, tasks that were normally unavoidable in seminal contributions. With the introduction of natural matrices, the proposed balancing method is independent of the time and the trajectory generated by the moving links of parallel manipulators. The effectiveness of the method is exemplified by balancing two planar parallel manipulators.

本文介绍了一种用于平衡平面平行机械手的力和力矩的简化矩阵方法。该方法采用牛顿第二定律和角动量矢量概念，但无需进行速度和加速度分析，而这些分析通常是开创性文章中不可避免的任务。由于引入了自然矩阵，所提出的平衡方法不受时间和平行机械手运动链路产生的轨迹的影响。该方法的有效性通过平衡两个平面平行机械手得到了体现。

引用次数: 0

3D dynamics and control of a snake robot in uncertain underwater environment 不确定水下环境中蛇形机器人的三维动力学与控制

IF 2.7 4区计算机科学 Q3 ROBOTICS

Robotica

Pub Date : 2024-09-19 DOI: 10.1017/s0263574724000821

Bhavik M. Patel, Santosha K. Dwivedy

The snake robot can be used to monitor and maintain underwater structures and environments. The motion of a snake robot is achieved by lateral undulation which is called the gait pattern of the snake robot. The parameters of a gait pattern need to be adjusted for compensating environmental uncertainties. In this work, 3D motion dynamics of a snake robot for the underwater environment is proposed with vertical motion using the buoyancy variation technique and horizontal motion using lateral undulation. “The neutral buoyant snake robot motion in hypothetical plane and added mass effect is negligible”, these previous assumptions are removed in this work. Two different control algorithms are designed for horizontal and vertical motions. The existing super twisting sliding mode control (STSMC) is used for the horizontal serpentine motion of the snake robot. The control law is designed on a reduced-ordered dynamic system based on virtual holonomic constraints. The vertical motion is achieved by controlling the mass variation using a pump. The water pumps are controlled using the event-based controller or Proportional Derivative (PD) controller. The results of the proposed control technique are verified with various external environmental disturbances and uncertainties to check the robustness of the control approach for various path following cases. Moreover, the results of STSMC scheme are compared with SMC scheme to check the effectiveness of STSMC. The practical implementation of the work is also performed using Simscape Multibody environment where the designed control algorithm is deployed on the virtual snake robot.

蛇形机器人可用于监测和维护水下结构和环境。蛇形机器人通过横向起伏实现运动，这就是蛇形机器人的步态。步态模式的参数需要调整，以补偿环境的不确定性。在这项工作中，提出了水下环境蛇形机器人的三维运动动力学，其中垂直运动采用浮力变化技术，水平运动采用横向起伏技术。由于 "蛇形机器人在假设平面内的中性浮力运动和附加质量效应可以忽略不计"，因此本作品取消了这些先前的假设。针对水平和垂直运动设计了两种不同的控制算法。蛇形机器人的水平蛇形运动采用现有的超扭曲滑动模式控制（STSMC）。该控制法则是在基于虚拟整体约束的减序动态系统上设计的。垂直运动是通过使用水泵控制质量变化来实现的。水泵使用基于事件的控制器或比例微分（PD）控制器进行控制。在各种外部环境干扰和不确定性的情况下，对所提出的控制技术的结果进行了验证，以检查控制方法在各种路径跟踪情况下的鲁棒性。此外，还将 STSMC 方案的结果与 SMC 方案进行了比较，以检验 STSMC 的有效性。工作的实际实施也是在 Simscape 多体环境下进行的，在虚拟蛇形机器人上部署了设计的控制算法。

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引用次数: 0

Artificial neural network-based control of powered knee exoskeletons for lifting tasks: design and experimental validation 基于人工神经网络的动力膝关节外骨骼起重任务控制：设计与实验验证

IF 2.7 4区计算机科学 Q3 ROBOTICS

Robotica

Pub Date : 2024-09-18 DOI: 10.1017/s0263574724001206

Asif Arefeen, Yujiang Xiang

This study introduces a hybrid model that utilizes a model-based optimization method to generate training data and an artificial neural network (ANN)-based learning method to offer real-time exoskeleton support in lifting activities. For the model-based optimization method, the torque of the knee exoskeleton and the optimal lifting motion are predicted utilizing a two-dimensional (2D) human–exoskeleton model. The control points for exoskeleton motor current profiles and human joint angle profiles from cubic B-spline interpolation represent the design variables. Minimizing the square of the normalized human joint torque is considered as the cost function. Subsequently, the lifting optimization problem is tackled using a sequential quadratic programming (SQP) algorithm in sparse nonlinear optimizer (SNOPT). For the learning-based approach, the learning-based control model is trained using the general regression neural network (GRNN). The anthropometric parameters of the human subjects and lifting boundary postures are used as input parameters, while the control points for exoskeleton torque are treated as output parameters. Once trained, the learning-based control model can provide exoskeleton assistive torque in real time for lifting tasks. Two test subjects’ joint angles and ground reaction forces (GRFs) comparisons are presented between the experimental and simulation results. Furthermore, the utilization of exoskeletons significantly reduces activations of the four knee extensor and flexor muscles compared to lifting without the exoskeletons for both subjects. Overall, the learning-based control method can generate assistive torque profiles in real time and faster than the model-based optimal control approach.

本研究介绍了一种混合模型，该模型利用基于模型的优化方法生成训练数据，并利用基于人工神经网络（ANN）的学习方法为起重活动提供实时外骨骼支持。在基于模型的优化方法中，利用二维（2D）人体-外骨骼模型预测膝关节外骨骼的扭矩和最佳提举运动。外骨骼电机电流曲线控制点和立方 B-样条插值法得出的人体关节角度曲线控制点代表设计变量。最小化归一化人体关节扭矩的平方被视为成本函数。随后，利用稀疏非线性优化器（SNOPT）中的顺序二次编程（SQP）算法解决提升优化问题。在基于学习的方法中，使用一般回归神经网络（GRNN）训练基于学习的控制模型。人体的人体测量参数和提升边界姿势作为输入参数，外骨骼扭矩控制点作为输出参数。经过训练后，基于学习的控制模型就能为举重任务实时提供外骨骼辅助扭矩。实验结果与模拟结果之间存在两个测试对象的关节角度和地面反作用力（GRFs）比较。此外，与不使用外骨骼的情况相比，使用外骨骼可显著降低两名受试者膝关节四块伸屈肌的激活率。总之，与基于模型的优化控制方法相比，基于学习的控制方法能更快地实时生成辅助扭矩曲线。

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引用次数: 0

Obstacle avoidance control of UGV based on adaptive-dynamic control barrier function in unstructured terrain 基于自适应动态控制障碍函数的 UGV 在非结构化地形中的避障控制

IF 2.7 4区计算机科学 Q3 ROBOTICS

Robotica

Pub Date : 2024-09-18 DOI: 10.1017/s026357472400122x

Liang Guo, Suyu Zhang, Wenlong Zhao, Jun Liu, Ruijun Liu

The widely used model predictive control of discrete-time control barrier functions (MPC-CBF) has difficulties in obstacle avoidance for unmanned ground vehicles (UGVs) in complex terrain. To address this problem, we propose adaptive dynamic control barrier functions (AD-CBF). AD-CBF is able to adaptively select an extended class of functions of CBF to optimize the feasibility and flexibility of obstacle avoidance behaviors based on the relative positions of the UGV and the obstacle, which in turn improves the obstacle avoidance speed and safety of the MPC algorithm when integrated with MPC. The algorithmic constraints of the CBF employ hierarchical density-based spatial clustering of applications with noise (HDBSCAN) for parameterization of dynamic obstacle information and unscaled Kalman filter (UKF) for trajectory prediction. Through simulations and practical experiments, we demonstrate the effectiveness of the AD-CBF-MPC algorithm in planning optimal obstacle avoidance paths in dynamic environments, overcoming the limitations of the point-by-point feasibility of MPC-CBF.

广泛使用的离散时间控制障碍函数模型预测控制（MPC-CBF）在复杂地形中的无人地面车辆（UGV）避障方面存在困难。为解决这一问题，我们提出了自适应动态控制障碍函数（AD-CBF）。AD-CBF 能够根据 UGV 与障碍物的相对位置，自适应地选择 CBF 的一类扩展函数，优化避障行为的可行性和灵活性，从而提高 MPC 算法与 MPC 集成后的避障速度和安全性。CBF 的算法约束采用基于分层密度的带噪声空间聚类应用（HDBSCAN）对动态障碍物信息进行参数化，并采用无标度卡尔曼滤波器（UKF）进行轨迹预测。通过模拟和实际实验，我们证明了 AD-CBF-MPC 算法在动态环境中规划最优避障路径的有效性，克服了 MPC-CBF 逐点可行性的局限性。

引用次数: 0