不確実な環境認識下でのオフロード自律走行のためのマルチサンプル長距離経路計画
Multi-Sample Long Range Path Planning under Sensing Uncertainty for Off-Road Autonomous Driving
不確実な観測のもとでオフロード車両が長距離経路を動的に再計画する手法DREAMSを提案し、DARPA RACERの実データで従来の決定化手法より走行時間と衝突コストの両面で優れることを示した。
著者: Matt Schmittle, Rohan Baijal, Brian Hou, Siddhartha Srinivasa, Byron Boots
分類: cs.RO
原文アブストラクト
We focus on the problem of long-range dynamic replanning for off-road autonomous vehicles, where a robot plans paths through a previously unobserved environment while continuously receiving noisy local observations. An effective approach for planning under sensing uncertainty is determinization, where one converts a stochastic world into a deterministic one and plans under this simplification. This makes the planning problem tractable, but the cost of following the planned path in the real world may be different than in the determinized world. This causes collisions if the determinized world optimistically ignores obstacles, or causes unnecessarily long routes if the determinized world pessimistically imagines more obstacles. We aim to be robust to uncertainty over potential worlds while still achieving the efficiency benefits of determinization. We evaluate algorithms for dynamic replanning on a large real-world dataset of challenging long-range planning problems from the DARPA RACER program. Our method, Dynamic Replanning via Evaluating and Aggregating Multiple Samples (DREAMS), outperforms other determinization-based approaches in terms of combined traversal time and collision cost. https://sites.google.com/cs.washington.edu/dreams/
