多无人机合作覆盖 基于差异进化算法搜索各种区域
Hui Zeng1, Lei Tong2, Xuewen Xia3
1Xinjiang Institute of Engineering, College of Information Engineering, Urumqi 830091, China.
Biomimetics (Basel, Switzerland)
|July 26, 2024
概括
本研究介绍了使用差异演化 (DE) 的多无人机 (UAV) 合作覆盖算法. DECSMU算法通过优化飞行路径以实现高覆盖率和低能耗来增强搜索任务.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 优化算法 优化算法
背景情况:
- 无人驾驶飞行器 (UAV) 在搜索任务中具有优势,但在射程和耐力方面面临限制.
- 合作的多无人机系统对于在复杂环境中有效覆盖至关重要.
- 现有的方法在单个无人机的有效路径规划和能源管理方面扎.
研究的目的:
- 为高效的搜索任务提出一个新的多无人机合作覆盖算法 (DECSMU).
- 为了提高覆盖率,并尽量减少能源消耗在复杂的地形.
- 确保避免碰撞,并证明算法稳定性和通用性.
主要方法:
- 基于差异演化 (DE) 的算法 (DECSMU) 将覆盖范围划分为阶段.
- 每个无人机使用DE动态规划飞行路径,并具有覆盖范围和能量奖励功能.
- 分布式模型预测控制促进了协作覆盖和信息交互.
主要成果:
- DECSMU算法实现了高覆盖率和低能耗.
- 在整个搜索任务中都保持了避免碰撞的方法.
- 实验结果表明,该算法在不同地区的稳定性和通用性.
结论:
- 拟议的DECSMU算法有效地解决了单个无人机在覆盖范围搜索中的局限性.
- DECSMU为多无人机合作任务提供了稳定和可通用的解决方案.
- 动态奖励功能和分布式控制有助于高效和安全的操作.
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