一种高效的四足机器人运动规划与控制框架
An Efficient Optimal Planning and Control Framework For Quadrupedal Locomotion
AI总结：
本文提出一种高效的动态规划框架，用于四足机器人的最优规划与控制，通过多级优化方法高效求解切换时间和连续控制输入，并采用连续时间约束LQR算法优化前馈与反馈控制器。
摘要：
本文提出了一种高效的动态规划框架，用于四足机器人最优规划与控制。首先将问题表述为切换系统的最优控制问题。然后提出多级优化方法，以求得最优切换时间和最优连续控制输入。通过此方案，分解优化可能比综合方法更高效。最后，我们提出一种连续时间约束LQR算法，同时优化前馈和反馈控制器，具有O(n)的时间复杂度。为了验证我们的方法，我们在四足机器人上展示了框架的性能。我们选择质心动力学和完整运动学公式作为切换系统模型，其中切换时间以及接触力和关节速度针对不同的运动任务（如跨越间隙、行走和小跑）进行优化。
英文摘要：
In this paper, we present an efficient Dynamic Programing framework for optimal planning and control of legged robots. First we formulate this problem as an optimal control problem for switched systems. Then we propose a multi--level optimization approach to find the optimal switching times and the optimal continuous control inputs. Through this scheme, the decomposed optimization can potentially be done more efficiently than the combined approach. Finally, we present a continuous-time constrained LQR algorithm which simultaneously optimizes the feedforward and feedback controller with $O(n)$ time-complexity. In order to validate our approach, we show the performance of our framework on a quadrupedal robot. We choose the Center of Mass dynamics and the full kinematic formulation as the switched system model where the switching times as well as the contact forces and the joint velocities are optimized for different locomotion tasks such as gap crossing, walking and trotting.
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