Real-time synthesis of legged locomotion maneuvers in challenging industrial settings is still an open problem, requiring simultaneous determination of footsteps locations several steps ahead while generating whole-body motions close to the robot's limits. State estimation and perception errors impose the practical constraint of fast re-planning motions in a model predictive control (MPC) framework. We first observe that the computational limitation of perceptive locomotion pipelines lies in the combinatorics of contact surface selection. Re-planning contact locations on selected surfaces can be accomplished at MPC frequencies (50-100 Hz). Then, whole-body motion generation typically follows a reference trajectory for the robot base to facilitate convergence. We propose removing this constraint to robustly address unforeseen events such as contact slipping, by leveraging a state-of-the-art whole-body MPC (Croccodyl). Our contributions are integrated into a complete framework for perceptive locomotion, validated under diverse terrain conditions, and demonstrated in challenging trials that push the robot's actuation limits, as well as in the ICRA 2023 quadruped challenge simulation.

翻译：在具有挑战性的工业环境中实时合成腿部运动策略仍是一个开放性问题，需要同步确定数步之后的落脚位置，同时生成接近机器人极限的全身运动。状态估计与感知误差对模型预测控制（MPC）框架下的快速重规划运动提出了实际约束。我们首先观察到，感知运动管道的计算瓶颈在于接触面选择的组合优化问题。在选定表面上重规划接触位置可在MPC频率（50-100 Hz）下完成。此外，全身运动生成通常需跟随机器人基座的参考轨迹以确保收敛性。我们提出移除这一约束，通过利用最先进的全身MPC（Croccodyl）来鲁棒处理接触滑动等突发事件。我们的贡献被整合为一个完整的感知运动框架，在多种地形条件下得到验证，并在挑战机器人驱动极限的严苛试验以及ICRA 2023四足机器人挑战赛仿真中完成演示。