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

Shuangyao Huang, Haibo Zhang, Zhiyi Huang

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalIEEE Transactions on Intelligent Transportation Systems
Volume25
Issue number7
DOIs
Publication statusPublished - 2 Jan 2024

Keywords

  • APF
  • Autonomous aerial vehicles
  • Collision avoidance
  • collision avoidance
  • Cost function
  • Energy consumption
  • Energy efficiency
  • Planning
  • PSO
  • swarm
  • Trajectory
  • UAV

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