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

扩展基于无人机的测绘传感以支持地面车辆

• 批准号: 515360-2017
• 负责人: Melek, William
• 金额: $ 10.29万
• 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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=618784
• 关键词: Extended; UAV; based; sensing; mapping

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.

今天，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，它已经发展到提供类似轨道的越野机动性，同时保持在改善的道路上快速安全行驶的能力，而不会对基础设施造成不必要的损害。作为LAV的传感器，无人驾驶车辆（UV）将比典型的车载设备单独提供更远的LAV前方的地形信息。在陆基军事行动中，及时和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，该研究计划将专注于为我们的行业合作伙伴GDLS-C开发使能技术，使用扩展的基于无人机的地形测绘传感来支持地面车辆的使命规划; LAV和无人驾驶地面车辆（UGV）。具体而言，该合作项目有三个主要主题，其研究目标如下：1）使用LAV/UGV和UAV团队上的传感器快速增量地图构建，2）基于3D地形地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。研究结果将提供一种新的能力，直接支持加拿大的自适应分散作战概念。