Deep Reinforcement Learning-Driven Collaborative Rounding-Up for Multiple Unmanned Aerial Vehicles in Obstacle Environments
Year: 2024
Type: article
Abstract: With the rapid advancement of UAV technology, the utilization of multi-UAV cooperative operations has become increasingly prevalent in various domains, including military and civilian applications. However, achieving efficient coordinated rounding-up of multiple UAVs remains a challenging problem. This paper addresses the issue of collaborative drone hunting by proposing a decision-making control model based on deep reinforcement learning. Additionally, a shared experience data pool is established to facilitate communication between drones. Each drone possesses independent decision-making and control capabilities while also considering the presence of other drones in the environment to collaboratively accomplish obstacle avoidance and rounding-up tasks. Furthermore, we redefine and design the reward function of reinforcement learning to achieve precise control of drone swarms in diverse environments. Simulation experiments demonstrate the feasibility of the proposed method, showcasing its successful completion of obstacle avoidance, tracking, and rounding-up tasks in an obstacle environment.
Source: Drones
Authors: Zipeng Zhao, Yu Wan, Yong Chen
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,955
OpenAlex ID: W4402317096