信念空間における統合的な自己位置推定・動作計画・障害物回避アルゴリズム
An Integrated Localisation, Motion Planning and Obstacle Avoidance Algorithm in Belief Space
センサノイズや動作誤差による不確実性を考慮し、ロボットの状態推定と衝突確率の計算を統合した手法を提案。衝突確率の無限級数表現の収束を証明し、打ち切り誤差の上限も導出した。
著者: Antony Thomas, Fulvio Mastrogiovanni, Marco Baglietto
分類: cs.RO, cs.AI
原文アブストラクト
As robots are being increasingly used in close proximity to humans and objects, it is imperative that robots operate safely and efficiently under real-world conditions. Yet, the environment is seldom known perfectly. Noisy sensors and actuation errors compound to the errors introduced while estimating features of the environment. We present a novel approach (1) to incorporate these uncertainties for robot state estimation and (2) to compute the probability of collision pertaining to the estimated robot configurations. The expression for collision probability is obtained as an infinite series and we prove its convergence. An upper bound for the truncation error is also derived and the number of terms required is demonstrated by analyzing the convergence for different robot and obstacle configurations. We evaluate our approach using two simulation domains which use a roadmap-based strategy to synthesize trajectories that satisfy collision probability bounds.
