具有接触位置不确定性的腿足机器人多接触随机预测控制
Multi-contact Stochastic Predictive Control for Legged Robots with Contact Locations Uncertainty
AI总结：
提出面向腿足机器人的SNMPC全身轨迹优化方法，通过动力学加性不确定性和接触位置机会约束提升安全性，四足机器人蒙特卡洛实验实现100%成功率。
摘要：
在不确定性下进行轨迹优化，对于与环境发生接触的机器人而言是一个具有挑战性的问题。由于估计误差、控制缺陷，以及用于控制的规划模型与真实机器人动力学之间的模型不匹配，这类不确定性不可避免。这会导致控制策略可能在非预期位置发生接触，从而违反接触位置约束，并进而产生不安全的运动规划。本工作利用随机非线性模型预测控制（SNMPC）解决鲁棒运动动力学全身轨迹优化问题，在模型动力学中考虑加性不确定性，并将接触位置机会约束表示为机器人完整运动学的函数。我们在接触位置约束满足（安全性）方面，展示了相对于经典非线性MPC（NMPC），将SNMPC用于全身轨迹优化的优势。我们针对四足机器人在小型垫脚石上执行敏捷小跑和跳跃运动开展了大量蒙特卡洛仿真，在这些场景中接触位置满足至关重要。结果表明，SNMPC能够以100%的成功率安全执行所有运动，而NMPC在全部运动中失败48.3%。
英文摘要：
Trajectory optimization under uncertainties is a challenging problem for robots in contact with the environment. Such uncertainties are inevitable due to estimation errors, control imperfections, and model mismatches between planning models used for control and the real robot dynamics. This induces control policies that could violate the contact location constraints by making contact at unintended locations, and as a consequence leading to unsafe motion plans. This work addresses the problem of robust kino-dynamic whole-body trajectory optimization using stochastic nonlinear model predictive control (SNMPC) by considering additive uncertainties on the model dynamics subject to contact location chance-constraints as a function of robot's full kinematics. We demonstrate the benefit of using SNMPC over classic nonlinear MPC (NMPC) for whole-body trajectory optimization in terms of contact location constraint satisfaction (safety). We run extensive Monte-Carlo simulations for a quadruped robot performing agile trotting and bounding motions over small stepping stones, where contact location satisfaction becomes critical. Our results show that SNMPC is able to perform all motions safely with 100% success rate, while NMPC failed 48.3% of all motions.
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