未知環境におけるマルチAGVの時間的記憶に基づくRRT探索
Multi-AGV's Temporal Memory-based RRT Exploration in Unknown Environment
複数ロボットの協調探索で生じる重複を防ぐため、フロンティアの適応的な割当期間と記憶共有を用いたTM-RRT探索戦略を提案し、実環境で有効性を示した。
著者: Billy Pik Lik Lau, Brandon Jin Yang Ong, Leonard Kin Yung Loh, Ran Liu, Chau Yuen, Gim Song Soh, U-Xuan Tan
分類: cs.RO, cs.MA
原文アブストラクト
With the increasing need for multi-robot for exploring the unknown region in a challenging environment, efficient collaborative exploration strategies are needed for achieving such feat. A frontier-based Rapidly-Exploring Random Tree (RRT) exploration can be deployed to explore an unknown environment. However, its' greedy behavior causes multiple robots to explore the region with the highest revenue, which leads to massive overlapping in exploration process. To address this issue, we present a temporal memory-based RRT (TM-RRT) exploration strategy for multi-robot to perform robust exploration in an unknown environment. It computes adaptive duration for each frontier assigned and calculates the frontier's revenue based on the relative position of each robot. In addition, each robot is equipped with a memory consisting of frontier assigned and share among fleets to prevent repeating assignment of same frontier. Through both simulation and actual deployment, we have shown the robustness of TM-RRT exploration strategy by completing the exploration in a 25.0m x 54.0m (1350.0m2) area, while the conventional RRT exploration strategy falls short.
