モンテカルロ視点品質レンダリングによる空中・地上ロボット協調探査
Coordinated Aerial-Ground Robot Exploration via Monte-Carlo View Quality Rendering
地上・空中ロボットチームが大規模未知環境を効率的に探査するため、フロンティア分布とモンテカルロ視点品質レンダリング、自動微分可能な情報利得指標を組み合わせた枠組みを提案した。
著者: Di Deng, Zhefan Xu, Wenbo Zhao, Kenji Shimada
分類: cs.RO
原文アブストラクト
We present a framework for a ground-aerial robotic team to explore large, unstructured, and unknown environments. In such exploration problems, the effectiveness of existing exploration-boosting heuristics often scales poorly with the environments' size and complexity. This work proposes a novel framework combining incremental frontier distribution, goal selection with Monte-Carlo view quality rendering, and an automatic-differentiable information gain measure to improve exploration efficiency. Simulated with multiple complex environments, we demonstrate that the proposed method effectively utilizes collaborative aerial and ground robots, consistently guides agents to informative viewpoints, improves exploration paths' information gain, and reduces planning time.
