用于足式机器人安全轨迹规划与控制的收敛型iLQR
Convergent iLQR for Safe Trajectory Planning and Control of Legged Robots
AI总结：
针对足式机器人在欠驱动域与混合碰撞事件中局部反馈控制难从扰动恢复的问题，将最坏情况扰动分析融入iLQR框架，生成的收敛轨迹抗扰性更强、控制能耗更低。
摘要：
为了执行高动态、高敏捷的机动动作，足式机器人通常会在欠驱动域（例如足部离地阶段）停留一段时间，在此期间系统对自身加速度的控制能力有限，且在切换到新域（例如足部触地）之前的时间也受到约束。同时，这些切换会瞬时改变系统状态，可能导致扰动被映射到离目标轨迹任意远的位置。这些特性使得局部反馈控制器难以在系统经历欠驱动域和混合碰撞事件的过程中有效从扰动中恢复。为解决这一问题，我们利用表征扰动沿混合轨迹演化的基本解矩阵及其2-范数（代表扰动的最坏情况增长）。本文将最坏情况扰动分析用于明确推理混合轨迹的跟踪性能，并将其纳入iLQR（迭代线性二次调节器）框架，在考虑LQR（线性二次调节器）跟踪控制器下轨迹闭环收敛性的同时优化轨迹。与传统方法生成的轨迹相比，所生成的收敛轨迹能更有效地从扰动中恢复，对大扰动的鲁棒性更强，且消耗的反馈控制能量更少。
英文摘要：
In order to perform highly dynamic and agile maneuvers, legged robots typically spend time in underactuated domains (e.g. with feet off the ground) where the system has limited command of its acceleration and a constrained amount of time before transitioning to a new domain (e.g. foot touchdown). Meanwhile, these transitions can instantaneously change the system's state, possibly causing perturbations to be mapped arbitrarily far away from the target trajectory. These properties make it difficult for local feedback controllers to effectively recover from disturbances as the system evolves through underactuated domains and hybrid impact events. To address this, we utilize the fundamental solution matrix that characterizes the evolution of perturbations through a hybrid trajectory and its 2-norm, which represents the worst-case growth of perturbations. In this paper, the worst-case perturbation analysis is used to explicitly reason about the tracking performance of a hybrid trajectory and is incorporated in an iLQR framework to optimize a trajectory while taking into account the closed-loop convergence of the trajectory under an LQR tracking controller. The generated convergent trajectories recover more effectively from perturbations, are more robust to large disturbances, and use less feedback control effort than trajectories generated with traditional methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
