Multi-AGV's Temporal Memory-Based RRT Exploration in Unknown Environment

• Published in: IEEE robotics and automation letters Vol. 7; no. 4; pp. 9256 - 9263
• Main Authors: Lau, Billy Pik Lik, Ong, Brandon Jin Yang, Loh, Leonard Kin Yung, Liu, Ran, Yuen, Chau, Soh, Gim Song, Tan, U-Xuan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cooperating robots; Exploration; multi-robot systems; Multiple robots; Navigation; Revenue; Robot kinematics; Robot sensing systems; Robots; search and rescue robots; Servers; Task analysis; Terminology; Unknown environments
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2022.3190628

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 <inline-formula><tex-math notation="LaTeX">25.0\,\rm{m}\times \text{54.0}\,\rm{m}</tex-math></inline-formula> (<inline-formula><tex-math notation="LaTeX">\text{1350.0}\,\rm{m}^{2}</tex-math></inline-formula>) area, while the conventional RRT exploration strategy falls short.