This paper investigates multi-objective optimization of coordinated patrolling flight of multiple unmanned aerial vehicles in the vicinity of terrain, while respecting their performance parameters. A new efficient modified A-star (A*) algorithm with a novel defined criterion known as individual revisit time cell value is introduced and extended to the whole area of the three-dimensional mountainous environment. As a contribution to solving tradeoffs in the optimization problem, revisit time is conjugated with other contrary costs effective in flight planning through Pareto analysis. By introducing the revisit time and applying a specific setup to mitigate computational complexity, the proposed algorithm efficiently revisits the desired zones, which are more important to be revisited during the patrolling mission. The results of the introduced modified A* algorithm are compared in various scenarios with two different algorithms: a complete and optimal algorithm known as Dijkstra, and an evolutionary algorithm known as the genetic algorithm. Simulation results demonstrate that the proposed algorithm generates faster and more efficient trajectories in complex multi-agent scenarios due to the introduced cell selection method and dynamic-based simplifications applied in this research.

本文研究了多台无人机在地形附近协同巡逻飞行的多目标优化，同时尊重它们的性能参数。引入了一种新的高效修改 A-star (A*) 算法，该算法具有称为个体重访时间单元值的新定义标准，并将其扩展到三维山区环境的整个区域。作为对解决优化问题中的权衡的贡献，重访时间与通过帕累托分析在飞行计划中有效的其他相反成本相结合。通过引入重访时间并应用特定设置来减轻计算复杂性，所提出的算法有效地重访了在巡逻任务期间更重要的所需区域。引入的改进 A* 算法的结果在各种场景中与两种不同的算法进行了比较：称为 Dijkstra 的完整优化算法和称为遗传算法的进化算法。仿真结果表明，由于本研究中引入的细胞选择方法和基于动态的简化，所提出的算法在复杂的多智能体场景中生成更快、更有效的轨迹。