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E2CoPre: Energy Efficient and Cooperative Collision Avoidance for UAV Swarms with Trajectory Prediction

by   Shuangyao Huang, et al.

This paper addresses the collision avoidance problem of UAV swarms in three-dimensional (3D) space. The key challenges are energy efficiency and cooperation of swarm members. We propose to combine Artificial Potential Field (APF) with Particle Swarm Planning (PSO). APF provides environmental awareness and implicit coordination to UAVs. PSO searches for the optimal trajectories for each UAV in terms of safety and energy efficiency by minimizing a fitness function. The fitness function exploits the advantages of the Active Contour Model in image processing for trajectory planning. Lastly, vehicle-to-vehicle collisions are detected in advance based on trajectory prediction and are resolved by cooperatively adjusting the altitude of UAVs. Simulation results demonstrate that our method can save up to 80% of energy compared to state-of-the-art schemes.


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