Research on Multi-UAV Cooperative Search-Attack Mission Planning Simulation Based on Ant Colony Optimization Algorithm

• Main Authors: Zhang, Yongjin, Qu, Chongxiao, Jin, Lei, Fan, Changjun, Zhang, Cheng, Zhu, Liaoyuan
• Format: Conference Proceeding
• Language: English
• Subjects: Ant colony optimization; ant colony optimization algorithm; Autonomous aerial vehicles; Collaboration; LÖVE 2D; Planning; Reliability; search-attack mission planning; Simulation; Statistical analysis; UAV swarm
• ISSN: 2771-7372
• DOI: 10.1109/ICUS58632.2023.10318498

The use of unmanned aerial vehicles (UAVs) for target search and engagement represents an emerging paradigm shift in modern warfare. Due to limitations in the performance and reliability of individual drones, it remains challenging for a single drone to accomplish search-attack missions in complex scenarios independently. To address this, inspired by the foraging behavior of ant colonies, this paper introduces the concept of utilizing drone swarm formations to simulate search-attack mission planning (SASP). First, the underlying principles of ant colony optimization algorithms are introduced, based on which the simulation workflow for SASP is designed. Then, the collaborative mechanism of the drone swarm is implemented using Lua, with a particular focus on the iterative updating process of pheromone-like information, and demonstrated on the LÖVE 2D platform. Finally, the simulation results of two representative scenarios are presented, and a comprehensive quantitative analysis is conducted to assess the impact of different parameters on simulation outcomes and efficiency.