UAV Path Planning and Collaborative Searching for Air Pollution Source Using the Particle Swarm Optimization

The air pollution has become a major ecological issue. The surpassed pollution levels can be controlled by searching the pollution source. An environmental monitoring unmanned aerial vehicles (UAVs) can address this issue. The challenge here is how UAVs collaboratively navigate towards pollution source under realistic pollution distribution. In this paper, we proposed a novel methodology by using the collaborative intelligence learned from Golden shiners schooling fish. We adopted shiners collective intelligence with the particle swarm optimization (PSO). We used a Gaussian plume model for depicting the pollution distribution. Furthermore, our proposed method incorporates path planning and collision-avoidance for UAV group navigation. For path planning, we simulated obstacle rich 3D environment. The proposed methodology generates collision-free paths successfully. For group navigation of UAVs, the simulated environment includes a Gaussian plume model which considers several atmospheric constraints like temperature, wind speed, etc. The UAVs can successfully reach the pollution source with accuracy using the proposed methodology. Moreover, we can construct the unknown distribution by plotting the sensed pollution values by UAVs.