AAAI Publications, The Thirty-Third International Flairs Conference

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Model-Free Reinforcement Learning Approach for Leader-Follower Formation Using Nonholonomic Mobile Robots
Md Suruz Miah, Amr Elhussein, Fazel Keshtkar, Mohammed Abouheaf

Last modified: 2020-05-08


In this paper, we present a novel model-free reinforcement learning approach for solving a conventional leader-follower problem using autonomous wheeled mobile robots. Specifically, the proposed learning approach will determine the linear velocity and the steering angle (control actions) of a follower robot so that it can follow the time-varying motion trajectory of a leader robot. The setup of the online adaptive learning mechanism does not rely on any dynamical or kinematic parameters, i.e., ``model-free'', of the considered car-like robots. Bellman's principle of optimality is employed to approximate the reward of the control actions determined by the proposed model-free adaptive learning algorithm. A set of computer experiments has been conducted to evaluate the performance of the proposed algorithm under various unplanned leader-trajectories.

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