使用自组织迁移算法的无人机规划轨迹
Quoc Bao Diep1, Thanh-Cong Truong2, Ivan Zelinka3,4
1Faculty of Mechanical-Electrical and Computer Engineering, School of Technology, Van Lang University, Ho Chi Minh City, Vietnam.
PloS one
|July 7, 2025
概括
本研究引入了一种用于无人机 (UAV) 轨迹规划的新方法,提高了复杂环境中的安全性和效率. 该方法使用一种新的成本函数和群集智能,使自主无人机能够实时避开障碍物和获取目标.
科学领域:
- 机器人技术和自主系统
- 人工智能的人工智能
- 航空航天工程 航空航天工程
背景情况:
- 无人驾驶飞行器 (UAV) 在动态和复杂的环境中面临着安全轨迹规划的重大挑战.
- 关键问题包括实时避难障碍,适应环境变化和防止多无人机碰撞.
研究的目的:
- 为无人机开发一种新的轨道规划方法,该方法将避难障碍和目标获取整合在一起.
- 提高在具有挑战性的场景中自主无人机操作的安全性,效率和适应性.
主要方法:
- 一个新的成本函数被设计为平衡最小化距离目标和最大化距离障碍物的距离.
- 自组织迁移算法 (SOMA) 是一种群集智能技术,用于优化成本函数.
- 该方法可以实现实时路径生成,动态适应和可扩展性,用于没有集中控制的多UAV系统.
主要成果:
- 模拟演示了无人机在复杂的环境中成功获取目标并避免与多个代理物的碰撞.
- 提出的方法在动态场景中被证明是有效的,展示了适应不可预测变化的能力.
- 可扩展性得到证实,该系统能够有效地处理一群10架无人机.
结论:
- 开发的轨迹规划方法为自主无人机操作提供了可靠和强大的解决方案.
- 这项工作通过为在充满挑战的动态环境中运行的无人机提供可扩展和适应的方法,推动了该领域的发展.
- 将一个新的成本函数与群集智能的集成有效地解决了无人机导航和安全的关键方面.
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