In the paper, Neural Q Learning algorithm (NQL) was involved to solve the obstacle avoidance problem for UAV path planning. Q learning was good at online learning and BP network provided excellent function approximation. The combination of two methods can provided UAV a collision-free trajectory in unknown environment. Through several simulations, the proposed algorithm could gain better performance and gain higher success rate than classic Q-learning (CQL). Besides, this method was extended for deep reinforcement learning, such as DQN, which is more suitable for practical applications.
Neural Q Learning Algorithm based UAV Obstacle Avoidance
2018-08-01
181825 byte
Conference paper
Electronic Resource
English
Obstacle avoidance using neural networks
Tema Archive | 1990
|Obstacle avoidance using fuzzy neural networks
Tema Archive | 1998
|European Patent Office | 2021
|European Patent Office | 2020
|Chassis obstacle avoidance system and obstacle avoidance method
European Patent Office | 2023
|