FSMP: 複雑大規模3次元環境の高速自律探査のためのフロンティアサンプリング混合プランナ
FSMP: A Frontier-Sampling-Mixed Planner for Fast Autonomous Exploration of Complex and Large 3-D Environments
フロンティア検出と決定論的サンプリングによるロードマップを組み合わせ、2段階の経路計画で複雑・大規模3次元環境を高速に探査するMAV向けフレームワークを提案した。
著者: Shiyong Zhang, Xuebo Zhang, Qianli Dong, Ziyu Wang, Haobo Xi, Jing Yuan
分類: cs.RO
原文アブストラクト
In this paper, we propose a systematic framework for fast exploration of complex and large 3-D environments using micro aerial vehicles (MAVs). The key insight is the organic integration of the frontier-based and sampling-based strategies that can achieve rapid global exploration of the environment. Specifically, a field-of-view-based (FOV) frontier detector with the guarantee of completeness and soundness is devised for identifying 3-D map frontiers. Different from random sampling-based methods, the deterministic sampling technique is employed to build and maintain an incremental road map based on the recorded sensor FOVs and newly detected frontiers. With the resulting road map, we propose a two-stage path planner. First, it quickly computes the global optimal exploration path on the road map using the lazy evaluation strategy. Then, the best exploration path is smoothed for further improving the exploration efficiency. We validate the proposed method both in simulation and real-world experiments. The comparative results demonstrate the promising performance of our planner in terms of exploration efficiency, computational time, and explored volume.
