A LiDAR Based Mobile Area Decision Method for TLS-DQN: Improving Control for AAV Mobility

Abstract

The Deep Q-Network (DQN) is one of the deep reinforcement learning algorithms, which uses deep neural network structure to estimate the Q-value in Q-learning. In the previous work, we designed and implemented a DQN-based Autonomous Aerial Vehicle (AAV) testbed and proposed a Tabu List Strategy based DQN (TLS-DQN). In this paper, we propose a LiDAR Based Mobile Area Decision Method for TLS-DQN to improve the control for AAV Mobility. The evaluation results show that the proposed method makes a good decision for the destination and mobile area based on LiDAR.