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基于变压器的软行为体对UAV路径规划至关重要精准农业物联网网络物联网网络

Guanting Ge¹, Mingde Sun¹, Yiyuan Xue¹

¹College of Software, Shanxi Agricultural University, Taiyuan 030031, China.

Sensors (Basel, Switzerland)

|December 31, 2025

概括

一个新的算法,基于多代理变压器的软行动者-关键 (MATRS),增强了在农业数据收集中多个无人机 (UAV) 的协调. MATRS实现了更快的任务完成和自主区域划分,以实现高效,无冲突的操作.

科学领域:

机器人和人工智能机器人和人工智能
农业技术农业技术
物联网 (IoT) 的物联网 (IoT) 的物联网.

背景情况:

无人驾驶飞行器 (UAV) 的多代理路径规划在效率和避免冲突方面面临挑战,特别是在复杂的农业数据收集方面.
现有的多代理强化学习 (MARL) 算法与高维状态空间,连续动作和代理之间的依赖性作斗争.

研究的目的:

提出一种新的算法,基于多代理变压器的软行为体-关键 (MATRS),用于无人机安全有效的协作数据收集和轨迹优化.
解决当前MARL算法在处理复杂的多代理协调任务方面的局限性.

主要方法:

开发了MATRS,利用了以分散执行 (CTDE) 的集中的培训模式.
将一个带有自我注意机制的变压器编码器集成到批评网络中,以建模代理之间的关系,并改进联合行动价值函数评估.

主要成果:

与基线算法 (MADDPG,MATD3,MASAC) 相比,MATRS在模拟中显示了更快的融合和更短的任务完成时间.
可扩展性实验显示,MATRS学会了有效的"任务空间分区"策略,以实现无人机群体的自主,无冲突区域覆盖.

结论:

结合基于注意力的架构与软行为体-关键学习,为物联网数据收集中的多无人机协调提供了强大而可扩展的解决方案.
对于复杂的农业数据收集任务,MATRS提供了改进的性能和自主操作策略.

关键词:

无人机无人机无人机是什么?数据收集数据收集数据收集多代理的深度强化学习学习.路径规划路径规划路径规划变压器变压器变压器变压器

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