基于实时运动学预测和全身控制的两足机器人深蹲运动

IF 1.5 Q3 AUTOMATION & CONTROL SYSTEMS IET Cybersystems and Robotics Pub Date : 2022-12-20 DOI:10.1049/csy2.12073
Wenhan Cai, Qingkai Li, Songrui Huang, Hongjin Zhu, Yong Yang, Mingguo Zhao
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引用次数: 0

摘要

下蹲是双足机器人的基本动作,在跳跃或拾取物体等机器人动作中至关重要。由于两足机器人固有的复杂动力学特性,完美的下蹲运动需要高性能的运动规划和控制算法。标准的学术解决方案将模型预测控制(MPC)与全身控制(WBC)相结合,这通常是计算成本高且难以在计算资源有限的实际机器人上实现的。提出了一种考虑即将到来的参考运动轨迹并将其与基于二次规划(QP)的WBC相结合的实时运动预测方法。由于WBC处理机器人的完整动力学和各种约束,因此RKP只需要在机器人的任务空间中采用线性运动学并对期望的加速度进行软约束。从而大大降低了导出的闭式RKP的计算成本。在一个重载双足机器人上对该方法进行了仿真验证。机器人进行快速和大幅度的蹲下运动,这需要接近极限的扭矩输出。与传统的基于qp的WBC方法相比,该方法具有较强的粗规划适应性,大大减少了用户对机器人运动规划的干扰。此外,与MPC一样,该方法可以提前准备即将到来的运动,但所需的计算时间要少得多。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Squat motion of a bipedal robot using real-time kinematic prediction and whole-body control

Squatting is a basic movement of bipedal robots, which is essential in robotic actions like jumping or picking up objects. Due to the intrinsic complex dynamics of bipedal robots, perfect squatting motion requires high-performance motion planning and control algorithms. The standard academic solution combines model predictive control (MPC) with whole-body control (WBC), which is usually computationally expensive and difficult to implement on practical robots with limited computing resources. The real-time kinematic prediction (RKP) method is proposed, which considers upcoming reference motion trajectories and combines it with quadratic programming (QP)-based WBC. Since the WBC handles the full robot dynamics and various constraints, the RKP only needs to adopt the linear kinematics in the robot's task space and to softly constrain the desired accelerations. Then, the computational cost of derived closed-form RKP is greatly reduced. The RKP method is verified in simulation on a heavy-loaded bipedal robot. The robot makes rapid and large-amplitude squatting motions, which require close-to-limit torque outputs. Compared with the conventional QP-based WBC method, the proposed method exhibits high adaptability to rough planning, which implies much less user interference in the robot's motion planning. Furthermore, like the MPC, the proposed method can prepare for upcoming motions in advance but requires much less computation time.

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来源期刊
IET Cybersystems and Robotics
IET Cybersystems and Robotics Computer Science-Information Systems
CiteScore
3.70
自引率
0.00%
发文量
31
审稿时长
34 weeks
期刊最新文献
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