改进的灰狼模型的应用在无人驾驶飞行器群的协作轨迹优化中
Jiguang Chen1,2,3, Yu Chen4,5,6, Rong Nie5,6
1School of Electronics and Information, Zhengzhou University of Aeronautics, Zhengzhou, 450046, China. jiguang_chen2022@163.com.
这项研究增强了灰狼优化算法,使用激素因子和深度强化学习来规划无人机 (UAV) 群体轨迹,实现了优越的低成本优化和稳定性.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 人工智能的人工智能
- 运营研究 运营研究
背景情况:
- 现有的无人飞行器 (UAV) 轨道规划方法在成本效益和智能方面的局限性.
- 灰狼优化算法 (GWO) 虽然用于无人机群优化,但在动态威胁下表现出弱合作和不稳定的性能.
- 需要先进的算法来提高无人机群行动的效率和智能.
研究的目的:
- 开发一个改进的灰狼优化算法,结合激素因子,以加强在无人机群轨道规划中的合作.
- 整合深度强化学习,以解决动态威胁环境中的群集智能算法的不稳定性能.
- 根据增强的算法构建和评估一个无人驾驶飞行器群体轨迹规划模型.
主要方法:
- 一个改进的灰狼优化算法是通过在传统的GWO中引入激素因子来开发的.
- 用深度强化学习来优化模型,解决动态威胁下的性能不稳定性.
- 使用改进的算法构建了一个无人驾驶飞行器群体轨迹规划模型.
主要成果:
- 与传统的GWO相比,改进的灰狼优化算法实现了较低的最佳健康值 (低于0.43).
- 改进后的模型在复杂的场景中显著减少了轨迹长度 (58.476公里) 和规划时间 (5.33秒).
- 与其他算法相比,拟议的模型表现出更高的稳定性和更低的指标值,这意味着性能优越.
结论:
- 开发的无人机群轨道规划模型有效地实现了低成本的轨道优化.
- 费罗蒙因子和深度强化学习的整合显著提高了无人机群运行的性能和稳定性.
- 这项研究为无人驾驶飞行器任务执行提供了更合理,技术更健全的方法.
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