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

TL;DR

This paper introduces E2CoPre, a decentralized collision avoidance method for UAV swarms that combines APF and PSO with trajectory prediction to enhance energy efficiency and cooperation.

Contribution

It presents a novel decentralized trajectory planning framework integrating APF, PSO, and calculus of variation for proactive collision avoidance in UAV swarms.

Findings

Effective collision avoidance demonstrated in various scenarios.

Improved energy efficiency compared to baseline methods.

Decentralized approach reduces communication overhead.

Abstract

This paper presents a novel solution to address the challenges in achieving energy efficiency and cooperation for collision avoidance in UAV swarms. The proposed method combines Artificial Potential Field (APF) and Particle Swarm Optimization (PSO) techniques. APF provides environmental awareness and implicit coordination to UAVs, while PSO searches for collision-free and energy-efficient trajectories for each UAV in a decentralized manner under the implicit coordination. This decentralized approach is achieved by minimizing a novel cost function that leverages the advantages of the active contour model from image processing. Additionally, future trajectories are predicted by approximating the minima of the novel cost function using calculus of variation, which enables proactive actions and defines the initial conditions for PSO. We propose a two-branch trajectory planning framework that ensures UAVs only change altitudes when necessary for energy considerations. Extensive experiments are conducted to evaluate the effectiveness and efficiency of our method in various situations.