接触位置の不確かさを考慮した脚式ロボットの多接触確率的予測制御
Multi-contact Stochastic Predictive Control for Legged Robots with Contact Locations Uncertainty
接触位置の不確かさを確率制約として扱うSNMPCを提案し、四足ロボットの跳躍・走行でNMPCより安全な軌道生成を実現した。
著者: Ahmad Gazar, Majid Khadiv, Andrea Del Prete, Ludovic Righetti
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
