基于FPGA的粒子群协作目标定位算法用于无人机群
Chuanhao Zhang1, Changsheng Li1, Zhipeng Chen1
1School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
本研究介绍了一种基于现场可编程门阵列 (FPGA) 的粒子群集优化 (PSO) 算法,用于精确地定位多个无人机 (UAV) 的协作定位. 在FPGA加速的PSO提高了定位准确性,实时性能和多无人机系统中的融合速度.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 计算智能是一种计算智能.
- 航空航天工程 航空航天工程
背景情况:
- 精确的协作定位对于多无人机系统至关重要.
- 现有方法在复杂环境中的实时性能和准确性可能面临挑战.
研究的目的:
- 开发和验证基于现场可编程门阵列 (FPGA) 的粒子群集优化 (PSO) 算法,以实现增强的多无人机协作本地化.
- 通过硬件加速和优化PSO策略来提高本地化效率,准确性和融合速度.
主要方法:
- 在FPGA上实现PSO算法,用于并行处理和硬件加速.
- 整合优化策略:粒子突变,可变交叉概率和可调节的权重.
- 多种场景的模拟和实验验证使用四节点协作本地化平台.
主要成果:
- 基于FPGA的PSO算法展示了高定位精度和优秀的实时性能.
- 该系统在比较模拟中实现了快速融合.
- 实验结果证实了使用拟议的算法实现多节点协作本地化的可行性和优势.
结论:
- 拟议的基于FPGA的PSO算法有效地增强了多UAV协作本地化.
- 该算法在准确性,融合速度和稳定性方面为实际应用提供了显著的优势.
- 通过FPGA进行硬件加速是优化多UAV系统中复杂本地化任务的可行方法.
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