解析的逆運動学を変数変換に用いた最適化ベース逆運動学の新定式化
A Framework for Combining Optimization-Based and Analytic Inverse Kinematics
解析的逆運動学の解を変数変換として利用することで、衝突回避などの非凸制約を含む最適化ベース逆運動学を解きやすくする新手法を提案し、複数のソルバーで成功率の向上を実証した。
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著者: Thomas Cohn, Lihan Tang, Alexandre Amice, Russ Tedrake
分類: cs.RO
原文アブストラクト
Analytic and optimization methods for solving inverse kinematics (IK) problems have been deeply studied throughout the history of robotics. The two strategies have complementary strengths and weaknesses, but developing a unified approach to take advantage of both methods has proved challenging. A key challenge faced by optimization approaches is the complicated nonlinear relationship between the joint angles and the end-effector pose. When this must be handled concurrently with additional nonconvex constraints like collision avoidance, optimization IK algorithms may suffer high failure rates. We present a new formulation for optimization IK that uses an analytic IK solution as a change of variables, and is fundamentally easier for optimizers to solve. We test our methodology on three popular solvers, representing three different paradigms for constrained nonlinear optimization. Extensive experimental comparisons demonstrate that our new formulation achieves higher success rates than the old formulation and baseline methods across various challenging IK problems, including collision avoidance, grasp selection, and humanoid stability.
