OTOプランナ：複雑未知環境における効率的な一度だけ探索するプランナ
OTO Planner: An Efficient Only Travelling Once Exploration Planner for Complex and Unknown Environments
複雑な未知環境での自律探索において、フロンティア更新の選択的機構と閉領域検出による視点評価、視点洗練を組み合わせ、経路の重複を減らして探索時間と移動距離を削減するプランナを提案した論文。
著者: Bo Zhou, Chuanzhao Lu, Yan Pan, Fu Chen
分類: cs.RO, cs.CV
原文アブストラクト
Autonomous exploration in complex and cluttered environments is essential for various applications. However, there are many challenges due to the lack of global heuristic information. Existing exploration methods suffer from the repeated paths and considerable computational resource requirement in large-scale environments. To address the above issues, this letter proposes an efficient exploration planner that reduces repeated paths in complex environments, hence it is called "Only Travelling Once Planner". OTO Planner includes fast frontier updating, viewpoint evaluation and viewpoint refinement. A selective frontier updating mechanism is designed, saving a large amount of computational resources. In addition, a novel viewpoint evaluation system is devised to reduce the repeated paths utilizing the enclosed sub-region detection. Besides, a viewpoint refinement approach is raised to concentrate the redundant viewpoints, leading to smoother paths. We conduct extensive simulation and real-world experiments to validate the proposed method. Compared to the state-of-the-art approach, the proposed method reduces the exploration time and movement distance by 10%-20% and improves the speed of frontier detection by 6-9 times.
