確率的相補性制約による接触可能性と軌道最適化のロバスト性の両立
Mediating between Contact Feasibility and Robustness of Trajectory Optimization through Chance Complementarity Constraints
地形形状や摩擦の不確かさを確率制約とリスク感度目的関数で扱い、接触を伴うロボット動作計画でロバスト性と制約充足のトレードオフを調整できる手法を提案した。
著者: Luke Drnach, John Z. Zhang, Ye Zhao
分類: cs.RO
原文アブストラクト
As robots move from the laboratory into the real world, motion planning will need to account for model uncertainty and risk. For robot motions involving intermittent contact, planning for uncertainty in contact is especially important, as failure to successfully make and maintain contact can be catastrophic. Here, we model uncertainty in terrain geometry and friction characteristics, and combine a risk-sensitive objective with chance constraints to provide a trade-off between robustness to uncertainty and constraint satisfaction with an arbitrarily high feasibility guarantee. We evaluate our approach in two simple examples: a push-block system for benchmarking and a single-legged hopper. We demonstrate that chance constraints alone produce trajectories similar to those produced using strict complementarity constraints; however, when equipped with a robust objective, we show the chance constraints can mediate a trade-off between robustness to uncertainty and strict constraint satisfaction. Thus, our study may represent an important step towards reasoning about contact uncertainty in motion planning.
