Multiple mini-robots navigation using a collaborative multiagent reinforcement learning framework. (2nd July 2020)

• Record Type: Journal Article
• Authors: Chaysri, Piyabhum; Blekas, Konstantinos; Vlachos, Kostas
• Is Part Of: Advanced robotics. Volume 34:Number 13(2020)
• Page Start: 902
• Page End: 916
• Publication Date: 2020-07-02
• Subjects: Reinforcement learning -- multi-agents -- mini-robots -- autonomous navigation -- moving obstacles avoidance; Robotics -- Periodicals; Robotics -- Japan -- Periodicals; Robotics; Japan; Periodicals; 629.89205
• DOI: 10.1080/01691864.2020.1757507
• Languages: English
• ISSNs: 0169-1864

Abstract

Abstract : In this work we investigate the use of a reinforcement learning (RL) framework for the autonomous navigation of a group of mini-robots in a multi-agent collaborative environment. Each mini-robot is driven by inertial forces provided by two vibration motors that are controlled by a simple and efficient low-level speed controller. The action of the RL agent is the direction of each mini-robot, and it is based on the position of each mini-robot, the distance between them and the sign of the distance gradient between each mini-robot and the nearest one. Each mini-robot is considered a moving obstacle that must be avoided by the others. We propose suitable state space and reward function that result in an efficient collaborative RL framework. The classical and the double Q-learning algorithms are employed, where the latter is considered to learn optimal policies of mini-robots that offers more stable and reliable learning process. A simulation environment is created, using the ROS framework, that include a group of four mini-robots. The dynamic model of each mini-robot and of the vibration motors is also included. Several application scenarios are simulated and the results are presented to demonstrate the performance of the proposed approach. GRAPHICAL ABSTRACT: