$E^2Coop$: Energy Efficient and Cooperative Obstacle Detection and Avoidance for UAV Swarms

TL;DR

$E^2Coop$ introduces a cooperative, energy-efficient obstacle avoidance scheme for UAV swarms by integrating APF with PSO, significantly reducing energy consumption during navigation.

Contribution

The paper proposes a novel cooperative trajectory planning method combining APF and PSO for UAV swarms to enhance energy efficiency and collision avoidance.

Findings

Achieves up to 51% energy savings compared to existing schemes.

Effectively avoids collisions among swarm members and obstacles.

Utilizes active contour models for energy-efficient trajectory planning.

Abstract

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.