$E^2Coop$: Energy Efficient and Cooperative Obstacle Detection and Avoidance for UAV Swarms
This addresses energy consumption in trajectory planning for UAV swarms, which is critical for extended operations, though it appears incremental as it builds on existing techniques.
The paper tackles energy-efficient obstacle avoidance for UAV swarms by proposing E^2Coop, a scheme that combines Artificial Potential Field and Particle Swarm Planning with active contour models, resulting in up to 51% energy savings compared to state-of-the-art methods.
Energy efficiency is of critical importance to trajectory planning for UAV swarms in obstacle avoidance. In this paper, we present $E^2Coop$, a new scheme designed to avoid collisions for UAV swarms by tightly coupling Artificial Potential Field (APF) with Particle Swarm Planning (PSO) based trajectory planning. In $E^2Coop$, swarm members perform trajectory planning cooperatively to avoid collisions in an energy-efficient manner. $E^2Coop$ exploits the advantages of the active contour model in image processing for trajectory planning. Each swarm member plans its trajectories on the contours of the environment field to save energy and avoid collisions to obstacles. Swarm members that fall within the safeguard distance of each other plan their trajectories on different contours to avoid collisions with each other. Simulation results demonstrate that $E^2Coop$ can save energy up to 51\% compared with two state-of-the-art schemes.