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Solving the Multi-Functional Heterogeneous UAV Cooperative Mission Planning Problem Using Multi-Swarm Fruit Fly

Rubin Luo1, Hongxing Zheng2, Jifeng Guo2

  • 1Institute of Aerospace Systems Engineering, Beijing 100076, China.

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|September 9, 2020
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Summary
This summary is machine-generated.

A new multi-swarm fruit fly optimization algorithm (MFOA) enhances cooperative mission planning for multiple unmanned aerial vehicles (UAVs). This approach optimizes task assignment and trajectory planning for improved efficiency and stability in complex missions.

Keywords:
cooperative mission planningmulti-functional heterogeneous UAVsmulti-swarm fruit fly optimization algorithm

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Area of Science:

  • Robotics and Automation
  • Artificial Intelligence
  • Operations Research

Background:

  • Unmanned Aerial Vehicle (UAV) missions are growing in complexity.
  • Cooperative multi-UAV systems with diverse sensors enhance mission efficiency.
  • Effective task assignment, scheduling, and trajectory planning are crucial for heterogeneous UAV cooperation.

Purpose of the Study:

  • To develop an optimized algorithm for multi-functional heterogeneous UAV cooperative mission planning.
  • To simultaneously minimize mission makespan and total mission time.
  • To improve the efficiency and stability of cooperative UAV missions.

Main Methods:

  • Proposed a multi-swarm fruit fly optimization algorithm (MFOA) with dual strategy switching.
  • Introduced a multi-swarm mechanism to enhance global search capabilities.
  • Implemented dual strategy switching in the smell-based search phase (local and large-scale strategies) and a greedy selection strategy in the vision-based search stage.

Main Results:

  • The MFOA algorithm demonstrated superior effectiveness and stability in simulations.
  • The proposed algorithm successfully addressed the multi-functional heterogeneous UAV cooperative mission planning problem.
  • Compared to other algorithms, MFOA showed improved performance in minimizing makespan and total mission time.

Conclusions:

  • The MFOA algorithm is a highly effective and stable solution for complex cooperative UAV mission planning.
  • The multi-swarm approach and dual strategy switching significantly enhance optimization capabilities.
  • This research contributes to more efficient and reliable autonomous multi-UAV operations.