連続体ロボット制御のためのニューラル配置距離関数
Neural Configuration Distance Function for Continuum Robot Control
連続体ロボットの形状をリンクごとの距離場を学習して運動学チェーンで統合するN-CEDFとして表現し、MPPI制御に組み込むことで動的障害物環境でも安全な軌道生成を可能にした。
著者: Kehan Long, Hardik Parwana, Georgios Fainekos, Bardh Hoxha, Hideki Okamoto, Nikolay Atanasov
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
This paper presents a novel method for modeling the shape of a continuum robot as a Neural Configuration Euclidean Distance Function (N-CEDF). By learning separate distance fields for each link and combining them through the kinematics chain, the learned N-CEDF provides an accurate and computationally efficient representation of the robot's shape. The key advantage of a distance function representation of a continuum robot is that it enables efficient collision checking for motion planning in dynamic and cluttered environments, even with point-cloud observations. We integrate the N-CEDF into a Model Predictive Path Integral (MPPI) controller to generate safe trajectories for multi-segment continuum robots. The proposed approach is validated for continuum robots with various links in several simulated environments with static and dynamic obstacles.
