多速率非线性模型预测控制用于四足机器人的墙壁支撑双足运动
Multi-Rate Nonlinear Model Predictive Control for Wall-Supported Bipedal Locomotion of Quadrupedal Robots
AI总结：
提出基于多速率非线性模型预测控制的分层框架，实现四足机器人在受限环境中借助墙壁进行混合双足运动，通过接触点与质心轨迹联合优化提升动态稳定性，仿真验证成功率比传统方法高2.9倍。
摘要：
本文提出了一种基于多速率非线性模型预测控制（MR-NMPC）的新型分层规划与控制框架，使四足机器人能够在受限环境中借助墙壁支撑执行混合双足运动。这种运动的实时轨迹优化面临重大挑战，因为控制器必须同时规划接触点以及机器人质心和方向的连续轨迹，同时考虑机器人非线性动力学、单边接触约束、欠驱动以及动力学的切换特性。在控制框架的高层，提出了MR-NMPC，它使用单刚体动力学模型动态规划接触点的离散时间轨迹以及质心和方向的连续时间轨迹。通过将接触点规划纳入多速率最优控制框架，该方法相比启发式落脚点策略增强了动态稳定性。在控制框架的低层，基于虚拟约束和二次规划的非线性全身控制器强制执行全阶动力学并跟踪MR-NMPC参考。通过大量数值仿真验证了所提方法，展示了Unitree A1四足机器人在粗糙地形和外部扰动下在受限环境中的鲁棒墙壁支撑双足运动。对比分析表明，所提出的MR-NMPC在高速通过不规则地形时，成功率比采用启发式落脚点策略的传统MPC高2.9倍。
英文摘要：
This paper presents a novel layered planning and control framework based on multi-rate nonlinear model predictive control (MR-NMPC) that enables quadrupedal robots to perform hybrid bipedal locomotion with wall-assisted support in constrained environments. Real-time trajectory optimization for this locomotion presents significant challenges, as the controller must simultaneously plan for both the contact points and the continuous trajectories of the robot's center of mass (CoM) and orientation within the robot's nonlinear dynamics while accounting for unilateral contact constraints, underactuation, and the switching nature of the robot's dynamics. At the high level of the control framework, an MR-NMPC is proposed, which dynamically plans both the discrete-time trajectories of the contact points and the continuous-time trajectories of the CoM and orientation, using a single rigid body (SRB) dynamics model. By incorporating contact-point planning within the multi-rate optimal control framework, this approach enhances dynamic stability compared to heuristic foot placement strategies. At the low level of the control framework, a nonlinear whole-body controller (WBC) based on virtual constraints and a quadratic program enforces full-order dynamics and tracks the MR-NMPC references. The proposed approach is validated through extensive numerical simulations demonstrating the robust wall-assisted bipedal locomotion of a Unitree A1 quadrupedal robot on rough terrains and under external disturbances in a constrained environment. Comparative analysis shows that the proposed MR-NMPC achieves a 2.9 times higher success rate compared to conventional MPC with heuristic-based foot placement strategies in negotiating irregular terrain at high speeds.
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。
