Centroidal-momentum-based trajectory generation for legged locomotion
Centroidal-momentum-based trajectory generation for legged locomotion
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DOI：
10.1016/j.mechatronics.2020.102364
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发表时间：
2020-06-01
期刊：
影响因子：
3.3
通讯作者：
Park, Hae-Won
中科院分区：
文献类型：
--
作者：
Li, Chuanzheng;Ding, Yanran;Park, Hae-Won
本文提出了一种基于机器人质心动量（CM）规划动态腿式运动的轨迹优化框架，该质心动量是机器人质心（CoM）处所有连杆动量的聚合。这个新框架是围绕由贝塞尔多项式参数化的地面反作用力 (GRF) 驱动的 CM 动态模型构建的。由于CM动力学的简单形式，可以通过直接积分GRF的贝塞尔多项式来获得机器人CM的闭式解。还可以使用质心动量矩阵 (CMM) 根据机器人的广义坐标和速度计算 CM。对于动态可行的运动，这些 CM 值应该匹配，从而为所提出的轨迹优化框架提供同等的约束。利用直接搭配方法在运动学和动力学约束下同时获得可行的 GRF 和关节轨迹。由于公式中 GRF 的参数化，CM 的闭式解可以减少轨迹优化解中搭配方法引起的数值误差，这对于应用于实际机器人系统时可靠的跟踪控制至关重要。使用所提出的框架，在模拟中获得了腿式机器人的跳跃轨迹。该算法在平面机器人试验台上进行了实验验证，证明了该方法在生成腿式机器人动态运动方面的有效性。
This paper presents a trajectory optimization framework for planning dynamic legged locomotion based on a robot's centroidal momentum (CM), which is the aggregation of all the links' momenta at the robot's Center of Mass (CoM). This new framework is built around CM dynamic model driven by Ground Reaction Forces (GRFs) parameterized with Bezier polynomials. Due to the simple form of CM dynamics, the closed-form solution of the robot's CM can be obtained by directly integrating the Bezier polynomials of GRFs. The CM can be also calculated from the robot's generalized coordinates and velocities using Centroidal Momentum Matrices (CMM). For dynamically feasible motions, these CM values should match, thereby providing equally constraints for the proposed trajectory optimization framework. Direct collocation methods are utilized to obtain feasible GRFs and joint trajectories simultaneously under kinematic and dynamic constraint. With the closed-form solutions of CM due to the parameterization of GRFs in the formulation, numerical error induced by collocation methods in the solution of trajectory optimization can be reduced, which is crucial for reliable tracking control when applied to real robotic systems. Using the proposed framework, jumping trajectories of legged robots are obtained in the simulation. Experimental validation of the algorithm is performed on a planar robot testbed, proving the effectiveness of the proposed method in generating dynamic motions of the legged robots.