1. Autonomous quadrotor obstacle avoidance based on dueling double deep recurrent Q-learning with monocular vision
- Author
-
Ming Zhu, Xiao Guo, Jiajun Ou, and Wenjie Lou
- Subjects
FOS: Computer and information sciences ,0209 industrial biotechnology ,Computer science ,Generalization ,business.industry ,Cognitive Neuroscience ,Q-learning ,Systems and Control (eess.SY) ,02 engineering and technology ,Electrical Engineering and Systems Science - Systems and Control ,Computer Science Applications ,Computer Science - Robotics ,020901 industrial engineering & automation ,Action (philosophy) ,Artificial Intelligence ,Obstacle avoidance ,FOS: Electrical engineering, electronic engineering, information engineering ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Computer vision ,Artificial intelligence ,business ,Robotics (cs.RO) ,Monocular vision - Abstract
The rapid development of unmanned aerial vehicles (UAV) puts forward a higher requirement for autonomous obstacle avoidance. Due to the limited payload and power supply, small UAVs such as quadrotors usually carry simple sensors and computation units, which makes traditional methods more challenging to implement. In this paper, a novel framework is demonstrated to control a quadrotor flying through crowded environments autonomously with monocular vision. The framework adopts a two-stage architecture, consisting of a sensing module and a decision module. The sensing module is based on an unsupervised deep learning method. And the decision module uses dueling double deep recurrent Q-learning to eliminate the adverse effects of limited observation capacity of an on-board monocular camera. The framework enables the quadrotor to realize autonomous obstacle avoidance without any prior environment information or labeled datasets for training. The trained model shows a high success rate in the simulation and a good generalization ability for transformed scenarios., 23 pages, 10 figures
- Published
- 2021