Hierarchical optimum control of a novel wheel-legged quadruped

IF 4.3 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Robotics and Autonomous Systems Pub Date : 2024-08-06 DOI:10.1016/j.robot.2024.104775
Rezwan Al Islam Khan , Chenyun Zhang , Yuzhen Pan , Anzheng Zhang , Ruijiao Li , Xuan Zhao , Huiliang Shang
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Abstract

This paper presents an optimal control architecture for Pegasus, a novel quadruped wheel-legged robot with hybrid locomotion capabilities. The proposed control architecture comprises of a hierarchical motion planner and a model predictive controller (MPC) that optimizes motion planning and control in various stages. A command-based motion planner is implemented to map desired robot states to optimal joint positions and velocities. This enables the MPC to seamlessly integrate legged and wheeled locomotion as a single task. The legs are modeled as N-link manipulators, and parallel tracking MPC controllers are implemented to optimize torques. This approach results in improved motion control and comprehensive four-wheel independent steering mechanism maneuvers. The experiments and results demonstrate the practical feasibility and robustness of the proposed control approach, with Pegasus exhibiting stable balancing, precise motion control, and the ability to navigate through challenging paths. Overall, the proposed control architecture provides a promising solution for achieving hybrid locomotion capabilities in quadruped wheel-legged robots.

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新型轮足四足动物的分层优化控制
Pegasus 是一种具有混合运动能力的新型四足轮腿机器人,本文介绍了 Pegasus 的优化控制架构。所提出的控制架构由分层运动规划器和模型预测控制器(MPC)组成,可在不同阶段优化运动规划和控制。基于指令的运动规划器可将所需的机器人状态映射到最佳关节位置和速度。这使得 MPC 能够将腿部和轮式运动无缝整合为一项任务。腿部被建模为 N 连杆操纵器,并采用并行跟踪 MPC 控制器来优化扭矩。这种方法改进了运动控制和全面的四轮独立转向机构操纵。实验和结果证明了所提出的控制方法的实际可行性和鲁棒性,Pegasus 表现出稳定的平衡、精确的运动控制以及在具有挑战性的路径上航行的能力。总之,所提出的控制架构为实现四足轮腿机器人的混合运动能力提供了一种前景广阔的解决方案。
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来源期刊
Robotics and Autonomous Systems
Robotics and Autonomous Systems 工程技术-机器人学
CiteScore
9.00
自引率
7.00%
发文量
164
审稿时长
4.5 months
期刊介绍: Robotics and Autonomous Systems will carry articles describing fundamental developments in the field of robotics, with special emphasis on autonomous systems. An important goal of this journal is to extend the state of the art in both symbolic and sensory based robot control and learning in the context of autonomous systems. Robotics and Autonomous Systems will carry articles on the theoretical, computational and experimental aspects of autonomous systems, or modules of such systems.
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