Design and Experimental Validation of Deep Reinforcement Learning-Based Fast Trajectory Planning and Control for Mobile Robot in Unknown Environment

• Published in: IEEE transaction on neural networks and learning systems Vol. 35; no. 4; pp. 1 - 15
• Main Authors: Chai, Runqi, Niu, Hanlin, Carrasco, Joaquin, Arvin, Farshad, Yin, Hujun, Lennox, Barry
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Approximation; Artificial neural networks; Collision avoidance; Computer simulation; Control algorithms; Control theory; Deep learning; Deep reinforcement learning (DRL); Exploration; Machine learning; Maneuvers; mobile robot; Mobile robots; motion control; Motion planning; Movement; Neural networks; Noise measurement; noisy prioritized experience replay (PER); optimal motion planning; Planning; recurrent neural network; Reinforcement; Robot control; Robot dynamics; Robot sensing systems; Robots; Tracking; Training; Trajectory; Trajectory control; Trajectory optimization; Trajectory planning; unexpected obstacles; Unknown environments; Waypoints
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2022.3209154

This article is concerned with the problem of planning optimal maneuver trajectories and guiding the mobile robot toward target positions in uncertain environments for exploration purposes. A hierarchical deep learning-based control framework is proposed which consists of an upper level motion planning layer and a lower level waypoint tracking layer. In the motion planning phase, a recurrent deep neural network (RDNN)-based algorithm is adopted to predict the optimal maneuver profiles for the mobile robot. This approach is built upon a recently proposed idea of using deep neural networks (DNNs) to approximate the optimal motion trajectories, which has been validated that a fast approximation performance can be achieved. To further enhance the network prediction performance, a recurrent network model capable of fully exploiting the inherent relationship between preoptimized system state and control pairs is advocated. In the lower level, a deep reinforcement learning (DRL)-based collision-free control algorithm is established to achieve the waypoint tracking task in an uncertain environment (e.g., the existence of unexpected obstacles). Since this approach allows the control policy to directly learn from human demonstration data, the time required by the training process can be significantly reduced. Moreover, a noisy prioritized experience replay (PER) algorithm is proposed to improve the exploring rate of control policy. The effectiveness of applying the proposed deep learning-based control is validated by executing a number of simulation and experimental case studies. The simulation result shows that the proposed DRL method outperforms the vanilla PER algorithm in terms of training speed. Experimental videos are also uploaded, and the corresponding results confirm that the proposed strategy is able to fulfill the autonomous exploration mission with improved motion planning performance, enhanced collision avoidance ability, and less training time.