Walking control of humanoid robots based on improved footstep planner and whole-body coordination controller.

IF 2.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Frontiers in Neurorobotics Pub Date : 2025-02-21 eCollection Date: 2025-01-01 DOI:10.3389/fnbot.2025.1538979
Xiangji Wang, Wei Guo, Siyu Yin, Sen Zhang, Fusheng Zha, Mantian Li, Pengfei Wang, Xiaolin Li, Lining Sun
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引用次数: 0

Abstract

High-speed walking is fundamental for humanoid robots to quickly reach the work site in emergency scenarios. According to biological studies, the coordinated motion of the arms and waist can significantly enhance walking speed and stability in humans. However, existing humanoid robot walking control frameworks predominantly focus on leg control, often overlooking the utilization of upper body joints. In this paper, a novel walking control framework combining the improved footstep planner and the whole-body coordination controller is proposed, aiming to improve the humanoid robot's tracking accuracy of desired speeds and its dynamic walking capability. First, we analyze the issues in traditional footstep planners based on Linear Inverted Pendulum and Model Predictive Control (LIP-MPC). By reconstructing the footstep optimization problem during walking using the Center-of-Mass (CoM) position, we propose an improved footstep planner to enhance the control accuracy of the desired walking speed in humanoid robots. Next, based on biological research, we define a coordinated control strategy for the arms and waist during walking. Specifically, the waist increases the robot's step length, while the arms counteract disturbance momentum and maintain balance. Based on the aforementioned strategy, we design a whole-body coordination controller for the humanoid robot. This controller adopts a novel hierarchical design approach, in which the dynamics and motion controllers for the upper and lower body are modeled and managed separately. This helps avoid the issue of poor control performance caused by multi-task coupling in traditional whole-body controllers. Finally, we integrate these controllers into a novel walking control framework and validate it on the simulation prototype of the humanoid robot Dexbot. Simulation results show that the proposed framework significantly enhances the maximum walking capability of the humanoid robot, demonstrating its feasibility and effectiveness.

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基于改进脚步规划和全身协调控制器的仿人机器人行走控制。
高速行走是仿人机器人在紧急情况下快速到达工作地点的基础。根据生物学研究,手臂和腰部的协调运动可以显著提高人类的行走速度和稳定性。然而,现有的仿人机器人行走控制框架主要侧重于腿部控制,往往忽略了对上肢关节的利用。为了提高仿人机器人对期望速度的跟踪精度和动态行走能力,提出了一种将改进的脚步规划器与全身协调控制器相结合的步行控制框架。首先,分析了基于线性倒立摆和模型预测控制(LIP-MPC)的传统足迹规划存在的问题。通过利用质心位置重构仿人机器人行走过程中的步态优化问题,提出了一种改进的步态规划方法,以提高仿人机器人对期望行走速度的控制精度。其次,在生物学研究的基础上,我们定义了行走过程中手臂和腰部的协调控制策略。具体来说,腰部增加了机器人的步长,而手臂抵消了干扰动量并保持平衡。基于上述策略,设计了仿人机器人的全身协调控制器。该控制器采用了一种新颖的分层设计方法,将上下体的动力学和运动控制器分别建模和管理。这有助于避免传统全身控制器中多任务耦合导致的控制性能差的问题。最后,我们将这些控制器集成到一个新的步行控制框架中,并在仿人机器人Dexbot的仿真样机上进行了验证。仿真结果表明,该框架显著提高了仿人机器人的最大行走能力,验证了该框架的可行性和有效性。
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来源期刊
Frontiers in Neurorobotics
Frontiers in Neurorobotics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCER-ROBOTICS
CiteScore
5.20
自引率
6.50%
发文量
250
审稿时长
14 weeks
期刊介绍: Frontiers in Neurorobotics publishes rigorously peer-reviewed research in the science and technology of embodied autonomous neural systems. Specialty Chief Editors Alois C. Knoll and Florian Röhrbein at the Technische Universität München are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neural systems include brain-inspired algorithms (e.g. connectionist networks), computational models of biological neural networks (e.g. artificial spiking neural nets, large-scale simulations of neural microcircuits) and actual biological systems (e.g. in vivo and in vitro neural nets). The focus of the journal is the embodiment of such neural systems in artificial software and hardware devices, machines, robots or any other form of physical actuation. This also includes prosthetic devices, brain machine interfaces, wearable systems, micro-machines, furniture, home appliances, as well as systems for managing micro and macro infrastructures. Frontiers in Neurorobotics also aims to publish radically new tools and methods to study plasticity and development of autonomous self-learning systems that are capable of acquiring knowledge in an open-ended manner. Models complemented with experimental studies revealing self-organizing principles of embodied neural systems are welcome. Our journal also publishes on the micro and macro engineering and mechatronics of robotic devices driven by neural systems, as well as studies on the impact that such systems will have on our daily life.
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