Extended UAV-based sensing for mapping in support of ground vehicles

基于无人机的扩展传感测绘支持地面车辆

基本信息

批准号：
515360-2017
负责人：
Melek, William
金额：
$ 4.61万
依托单位：
University of Waterloo
依托单位国家：
加拿大
项目类别：
Collaborative Research and Development Grants
财政年份：
2019
资助国家：
加拿大
起止时间：
2019-01-01 至 2020-12-31
项目状态：
已结题

来源：
https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=691177
关键词：
Extended UAV based sensing mapping

项目摘要

Today, advanced Light Armored Vehicle (LAV) mobility systems can support rapid advancement over very difficult off-road terrain and is often limited only by the operating crew's ability to select the desired route and assure safe traverse through the preferred route. The LAV is a highly versatile military platform which has evolved to provide track-like off-road mobility while retaining the ability to travel on improved roads rapidly and safely without inflicting undue harm to the infrastructure. Unmanned Vehicles (UVs) acting as sensors in support of the LAV will provide terrain information much further ahead of the LAV than typical vehicle mounted equipment alone can. In land based military operations, the availability of timely and extended situational awareness is critical to the success and safety of various operations. Therefore, this research program will focus on developing enabling technology for our industry partner GDLS-C using extended UAV-based sensing for terrain mapping to support mission planning for ground vehicles; both LAV and unmanned Ground Vehicles (UGV). In specific terms, this collaborative project has three main themes captures by the following research objectives: 1) Rapid incremental map building using sensors on-board LAV/UGV and UAV teams, 2) Path recommendation and autonomous path execution based on 3D terrain maps and 3) High fidelity multi-agent system modeling, control and co-ordination. The research results will provide a new capability that directly supports Canada's Adaptive Dispersed Operations (ADO) Concept.

如今，先进的轻型装甲车（LAV）机动系统可以支持在非常困难的越野地形上快速前进，并且通常仅受到操作人员选择所需路线并确保安全穿越首选路线的能力的限制。 LAV 是一种高度通用的军事平台，它已发展为提供类似履带式的越野机动性，同时保留在改进的道路上快速安全行驶的能力，而不会对基础设施造成不当损害。无人驾驶车辆 (UV) 作为支持 LAV 的传感器，将提供比单独的典型车载设备更领先于 LAV 的地形信息。在陆基军事行动中，及时、广泛的态势感知对于各种行动的成功和安全至关重要。因此，该研究计划将重点为我们的行业合作伙伴 GDLS-C 开发支持技术，使用基于无人机的扩展传感进行地形测绘，以支持地面车辆的任务规划； LAV 和无人地面车辆 (UGV)。具体而言，该合作项目具有以下研究目标的三个主题：1）使用LAV/UGV和无人机团队上的传感器快速增量地图构建，2）基于3D地形图的路径推荐和自主路径执行以及3）高保真多智能体系统建模、控制和协调。研究结果将提供直接支持加拿大自适应分散作战（ADO）概念的新能力。