非凸領域監視のためのマルチロボット協調カバレッジ経路計画のスケーラブル手法
Scalable Coverage Path Planning of Multi-Robot Teams for Monitoring Non-Convex Areas
飛行禁止区域などの不連続領域を含む対象エリアで、複数ロボットの作業負荷を均衡させつつミッション完了時間を短縮するカバレッジ経路計画アルゴリズムSCoPPを提案し、150台規模でも2分未満で計算できるスケーラビリティを示した。
著者: Leighton Collins, Payam Ghassemi, Ehsan T. Esfahani, David Doermann, Karthik Dantu, Souma Chowdhury
分類: cs.RO, cs.MA
原文アブストラクト
This paper presents a novel multi-robot coverage path planning (CPP) algorithm - aka SCoPP - that provides a time-efficient solution, with workload balanced plans for each robot in a multi-robot system, based on their initial states. This algorithm accounts for discontinuities (e.g., no-fly zones) in a specified area of interest, and provides an optimized ordered list of way-points per robot using a discrete, computationally efficient, nearest neighbor path planning algorithm. This algorithm involves five main stages, which include the transformation of the user's input as a set of vertices in geographical coordinates, discretization, load-balanced partitioning, auctioning of conflict cells in a discretized space, and a path planning procedure. To evaluate the effectiveness of the primary algorithm, a multi-unmanned aerial vehicle (UAV) post-flood assessment application is considered, and the performance of the algorithm is tested on three test maps of varying sizes. Additionally, our method is compared with a state-of-the-art method created by Guasella et al. Further analyses on scalability and computational time of SCoPP are conducted. The results show that SCoPP is superior in terms of mission completion time; its computing time is found to be under 2 mins for a large map covered by a 150-robot team, thereby demonstrating its computationally scalability.
