通过模型预测控制让四足车转弯时主动倾斜，提升高速稳定性。
Racing a Wheeled Quadruped: Active Load Transfer Mitigation via Model Predictive Control
- 用模型预测控制实时计算最佳倾斜角度，减少侧向载荷转移。
- 实验显示侧向载荷比降低44%，最快圈速提升8.7%。
- 适合做高速自主竞速机器人控制的研究者参考。
本文提出一种分层控制框架，结合模型预测控制（MPC）与强化学习（RL），实现对轮式四足机器人在自主竞速中的主动侧倾控制，以管理横向载荷转移。该框架包含离线生成的时间最优赛道、在线运行的MPC规划器（主动最小化横向载荷转移比LTR），以及直接部署在16个执行器上的全身强化学习低层策略。MPC基于单位树Go2-W平台的车辆动力学自行车模型，其腿部执行器作为主动悬架，膝关节产生抗侧倾力矩实现转向倾斜。物理赛道实验表明，主动侧倾控制使平均LTR降低44%，最快圈速提升8.7%，峰值横向加速度能力提高21.3%至1.98 $m/s^2$，在非倾斜基线控制器失效范围内仍保持高稳定性。补充代码与视频见 https://github.com/meisman-ucb/go2w-roll-control-mpc
原文摘要 · Abstract (English)
This paper presents a hierarchical control framework using model predictive control (MPC) and reinforcement learning (RL) for active roll control to manage lateral load transfer during autonomous racing of a wheeled quadruped. The framework integrates offline time-optimal raceline generation, an online MPC planner that actively minimizes the lateral Load Transfer Ratio (LTR), and a low-level, whole-body RL policy deployed directly onto the robot's 16 actuators. The MPC is based on a vehicle dynamics bicycle model of the Unitree Go2-W platform. The robot's leg actuators act as active suspension where knee joints generate anti-roll torque to bank into turns. Physical track experiments demonstrate that active roll control reduces mean LTR by up to 44%, improves the fastest lap time by 8.7%, and boosts peak lateral acceleration capability by 21.3% to 1.98 $m/s^2$, maintaining robust high-speed stability beyond the range of a non-tilting baseline controller. Supplementary code and video can be found at https://github.com/meisman-ucb/go2w-roll-control-mpc
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