Title: Design of unmanned ground vehicle (UGV) path tracking controller based on reinforcement learning

Authors: Islam A. Hassan; Tamer Attia; H. Ragheb; A.M. Sharaf

Addresses: Department of Mechanical Engineering, Military Technical Collage, Kobry Elkobbah, Cairo, 11766, Egypt ' Department of Mechanical Engineering, Military Technical Collage, Kobry Elkobbah, Cairo, 11766, Egypt ' Department of Mechanical Engineering, Military Technical Collage, Kobry Elkobbah, Cairo, 11766, Egypt ' Department of Mechanical Engineering, Military Technical Collage, Kobry Elkobbah, Cairo, 11766, Egypt

Abstract: This paper presents a unmanned ground vehicles (UGV) path tracking controller based on deep reinforcement learning (DRL), where a double deep Q-network (DDQN) algorithm is employed to train a deep neural network (DNN) for controlling the UGV to follow the desired path. The advantage of DDQN over deep Q-network (DQN) is that the DDQN uses two NNs, where one is working as a controller to generate actions for controlling the UGV, while the other is the target network to estimate the future rewards. The path tracking UGV kinematic is presented to determine the deviated distance and orientation between the UGV's pose and the desired path. White noise was added to the UGV wheels' speed for evaluating the robustness of the proposed controller. The simulation results illustrate that the trained controller enables the UGV to follow the desired trajectory in the presence of noisy actuation with high accuracy.

Keywords: UGV path tracking; DRL; deep reinforcement learning; DDQN; double deep Q-network; DNN; deep neural network.

DOI: 10.1504/IJHVS.2023.134320

International Journal of Heavy Vehicle Systems, 2023 Vol.30 No.5, pp.577 - 587

Received: 15 Aug 2022
Received in revised form: 22 Sep 2022
Accepted: 22 Sep 2022
Published online: 18 Oct 2023 *

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