基于密度函数的四足机器人安全运动规划
Safe Motion Planning for Quadruped Robots Using Density Functions
AI总结：
本研究针对四足机器人运动规划问题，提出基于密度函数的分层规划框架，结合高层反馈密度规划器、MPC轨迹优化与低层PID控制，通过仿真验证了方法的有效性并开源了代码。
摘要：
本文提出了一种基于密度函数的四足机器人运动规划算法。我们将运动问题分解为高层密度规划器和模型预测控制器（MPC）。由于密度函数可通过占据率概念进行物理解释，因此用安全约束表示环境十分直观，基于密度构建规划问题也更易操作。所提方法将机器人简化为积分器系统，高层规划以反馈形式呈现，通过解析构建的密度函数公式化表达。随后我们使用MPC优化参考轨迹，其中采用低层PID控制器获取力矩级控制。整个框架在仿真中实现，验证了我们的反馈式密度规划器适用于腿式运动。该工作的实现代码可在https://github.com/AndrewZheng-1011/legged_planner获取。
英文摘要：
This paper presents a motion planning algorithm for quadruped locomotion based on density functions. We decompose the locomotion problem into a high-level density planner and a model predictive controller (MPC). Due to density functions having a physical interpretation through the notion of occupancy, it is intuitive to represent the environment with safety constraints. Hence, there is an ease of use to constructing the planning problem with density. The proposed method uses a simplified model of the robot into an integrator system, where the high-level plan is in a feedback form formulated through an analytically constructed density function. We then use the MPC to optimize the reference trajectory, in which a low-level PID controller is used to obtain the torque level control. The overall framework is implemented in simulation, demonstrating our feedback density planner for legged locomotion. The implementation of work is available at \url{https://github.com/AndrewZheng-1011/legged_planner}
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