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灾难环境地图研究融合建设和强化学习基于空地协作多元异质机器人系统的导航技术

Hongtao Tao¹, Wen Zhao², Li Zhao³

¹School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China.

Sensors (Basel, Switzerland)

|August 28, 2025

概括

这项研究引入了一种用于灾难地图和导航的空地机器人系统. 它结合了无人飞行器 (UAV) 和无人地面飞行器 (UGV) 的功能,在复杂的环境中精确,高效地绘制和路径规划.

科学领域:

机器人技术
人工智能
地理空间绘图

背景情况:

灾难救援机器人需要精确的地图和自主导航.
目前的方法难以处理复杂的地形和有效的数据采集.

研究的目的:

开发一个空地协作系统以准确地构建灾难地图.
在充满挑战的环境中增强自主导航能力.

主要方法:

使用无人机进行快速3D覆盖和无人地面车辆进行详细的地面调查.
使用FAST LiDAR-惯性测距2 (FAST-LIO2) 框架进行精确的定位和点云映射.
实施改进的样本共识初始对齐 (SAC-IA) 进行粗略的注册,并对精细的注册进行 Voxelized Generalized Iterative Closest Point (VGICP).
应用深度决定性政策梯度 (DDPG) 算法用于基于强化学习的路径规划.
在 Gazebo 模拟环境中进行实验.

主要成果:

实现空中和地面数据的准确整合,以准确地绘制灾害环境地图.
即使在复杂的环境中,也证明了高效准确的点云地图生成.
与DQN和A*相比,DDPG算法提供了更顺和更优的路径规划.
楼模拟为测试和调试提供了安全,经济有效和高效的环境.

结论:

拟议的空中-地面协作方法显著改善了灾难映射和自主导航.
综合系统为灾难场景中的机器人操作提供了强大的解决方案.
使用的算法和模拟环境有助于开发先进的救援机器人.

关键词:

深度强化学习灾难地图的建设高精度地图融合路径规划

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