Affiliation:
1. College of System Engineer, National University of Defense Technology, Changsha 410003, China
Abstract
Navigating multiple drones autonomously in complex and unpredictable environments, such as forests, poses a significant challenge typically addressed by wireless communication for coordination. However, this approach falls short in situations with limited central control or blocked communications. Addressing this gap, our paper explores the learning of complex behaviors by multiple drones with limited vision. Drones in a swarm rely on onboard sensors, primarily forward-facing stereo cameras, for environmental perception and neighbor detection. They learn complex maneuvers through the imitation of a privileged expert system, which involves finding the optimal set of neural network parameters to enable the most effective mapping from sensory perception to control commands. The training process adopts the Dagger algorithm, employing the framework of centralized training with decentralized execution. Using this technique, drones rapidly learn complex behaviors, such as avoiding obstacles, coordinating movements, and navigating to specified targets, all in the absence of wireless communication. This paper details the construction of a distributed multi-UAV cooperative motion model under limited vision, emphasizing the autonomy of each drone in achieving coordinated flight and obstacle avoidance. Our methodological approach and experimental results validate the effectiveness of the proposed vision-based end-to-end controller, paving the way for more sophisticated applications of multi-UAV systems in intricate, real-world scenarios.
Funder
National Natural Science Foundation of China
Subject
Artificial Intelligence,Computer Science Applications,Aerospace Engineering,Information Systems,Control and Systems Engineering
Reference45 articles.
1. Lopez, B.T., and How, J.P. (June, January 29). Aggressive 3-D collision avoidance for high-speed navigation. Proceedings of the ICRA, Singapore.
2. Nanomap: Fast, uncertainty-aware proximity queries with lazy search over local 3d data;Florence;Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA),2018
3. Integrated perception and control at high speed: Evaluating collision avoidance maneuvers without maps;Florence;Proceedings of the Algorithmic Foundations of Robotics XII: Proceedings of the Twelfth Workshop on the Algorithmic Foundations of Robotics,2020
4. Raptor: Robust and perception-aware trajectory replanning for quadrotor fast flight;Zhou;IEEE Trans. Robot.,2021
5. Rectangular pyramid partitioning using integrated depth sensors (rappids): A fast planner for multicopter navigation;Bucki;IEEE Robot. Autom. Lett.,2020