邻居选择对基于有限感知视觉的自组织无人机群的影响
Hui Xiong1, Xiuzhi Shi1, Yaozu Ding1
1Tiangong University, No.399 BinShuiXi Road, XiQing District, Tianjin, People's Republic of China.
Bioinspiration & biomimetics
|October 31, 2024
概括
这项研究介绍了一种新的有限感知视觉 (FPV) 模型和无人机群体的急角测试 (AAT). 这种方法增强了邻居的选择,提高了群体的性能,减少了通信负载.
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
- 计算机视觉 计算机视觉
背景情况:
- 基于视觉的无人机 (UAV) 群集在适应性和可扩展性方面比传统系统具有优势.
- 现有的方法往往忽略了关键的感知因素,如视觉阻塞和传感器限制,影响群体表现.
研究的目的:
- 为自我组织无人机群控制提出一个感知上现实的有限感知视觉 (FPV) 邻居选择模型.
- 开发一种基于急角测试 (AAT) 的方法,以克服近邻相互作用的局限性.
主要方法:
- 灵感来自鸟类视觉系统的有限感知视觉 (FPV) 模型包括可调节的视野参数和阻塞处理.
- 急角测试 (AAT) 与FPV模型集成,用于高级邻居选择.
- 为了进行比较分析,进行了广泛的蒙特卡洛模拟.
主要成果:
- 在大型无人机群中,FPV+AAT机制显著降低了通信开销.
- 与传统方法相比,在订单,安全,联接,连接和防噪方面表现出卓越的性能.
- 有效地处理封闭的代理,这是现实世界的场景中常见的挑战.
结论:
- 拟议的FPV+AAT模型为基于视觉的无人机群中的邻居选择提供了更强大,更有效的方法.
- 这种方法通过考虑感知现实主义来增强群体协调和弹性.
- 为可扩展和可适应的无人机群应用提供了一个有前途的解决方案.
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