Pollution Source Localization Based on Multi-UAV Cooperative Communication

Abstract

Harmful gas leakage accidents in chemical plants have occurred from time to time. The application of mobile robots to find odor source has become one of the hottest research topics. Compared to traditional robots, unmanned aerial vehicle (UAV) is more flexible and safer. Therefore, using multi-UAV to solve pollution source tracking is a meaningful study. In this paper, an air pollution source tracking algorithm based on artificial potential field and particle swarm optimization is proposed. The particle swarm optimization algorithm combined with artificial potential field method is used to guide the UAVs to track the plume and avoid the collisions among them. At the same time, adaptive inertia weights are used to help improve the convergence and the searchability of particles. We not only evaluated this algorithm in simulation experiments but also designed a multi-UAV pollution source tracking platform for real-world experiments. The experimental results show that the algorithm can accurately find the pollution source in a short time.

Keywords

• Artificial potential field
• multi-UAV
• particle swarm optimization
• odor source localization
• encrypted communication