Fastron：オンライン学習による衝突判定プロキシモデルと能動学習戦略
Fastron: An Online Learning-Based Model and Active Learning Strategy for Proxy Collision Detection
カーネルパーセプトロンと能動学習を組み合わせ、環境変化に逐次適応するC空間衝突判定プロキシモデルを提案し、従来の衝突チェッカーより大幅に高速な判定を実現した。
著者: Nikhil Das, Naman Gupta, Michael Yip
分類: cs.RO
原文アブストラクト
We introduce the Fastron, a configuration space (C-space) model to be used as a proxy to kinematic-based collision detection. The Fastron allows iterative updates to account for a changing environment through a combination of a novel formulation of the kernel perceptron learning algorithm and an active learning strategy. Our simulations on a 7 degree-of-freedom arm indicate that proxy collision checks may be performed at least 2 times faster than an efficient polyhedral collision checker and at least 8 times faster than an efficient high-precision collision checker. The Fastron model provides conservative collision status predictions by padding C-space obstacles, and proxy collision checking time does not scale poorly as the number of workspace obstacles increases. All results were achieved without GPU acceleration or parallel computing.
