Navigation of Three Cooperative Object-Transportation Robots Using a Multistage Evolutionary Fuzzy Control Approach

• Published in: IEEE transactions on cybernetics Vol. 52; no. 5; pp. 3606 - 3619
• Main Authors: Juang, Chia-Feng, Lu, Chia-Hao, Huang, Chen-An
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ant colony optimization; Barriers; Collision avoidance; Cooperative robots; data-driven fuzzy systems; Evolution; Fuzzy control; Mobile robots; multiobjective evolutionary fuzzy control; Multiple objective analysis; Navigation; object transportation robots; Robot kinematics; robot navigation; Robots; Transportation; Unknown environments
• ISSN: 2168-2267; 2168-2275
• DOI: 10.1109/TCYB.2020.3015960

This article proposes a new multistage evolutionary fuzzy control configuration and navigation of three-wheeled robots cooperatively carrying an overhead object in unknown environments. Based on the divide-and-conquer technique, this article proposes a stage-by-stage evolutionary obstacle boundary following (OBF) fuzzy control of each of the three robots through multiobjective continuous ant colony optimization. In the first stage, a set of evolutionary nondominated fuzzy controllers (FCs) for a single robot (a leader robot) in the execution of the OBF behavior is learned. In the second stage, a follower robot is controlled by two evolutionary FCs in combination with a switched compensation FC so that the leader and follower robots can cooperatively transport an object while executing the OBF behavior along obstacles containing corners with right angles. In the third stage, the third robot functions as an accompanying robot and is learned to enter into a predicted triangular formation with the leader-follower robots to transport a larger object while executing the OBF behavior. In the navigation of the three object-transportation robots, a new cooperative behavior supervisor is proposed to coordinate the learned OBF behavior and a target seeking behavior. Successful navigations in simulations and experiments verify the effectiveness of the multistage evolutionary fuzzy control approach and navigation scheme.