Coordination and Control of Ground-Aerial Robotic Systems

本研究计划的目标是开发控制和定位技术的异构多机器人系统，包括无人地面车辆（UAV）和微型飞行器（MAV）的协同操作。最近，许多行业已经开始探索近距离监测环境和关键基础设施的替代可能性。MAV可以导航通过人类无法到达的区域，并能够提供环境的3D视角。有效载荷的限制使微型飞行器不能在更大的区域内自主部署更长的时间，而且还对携带先进的传感器套装和用于精确定位的强大计算机造成了物理限制。有两个主要问题需要解决。首先，无人机通常部署在受限制的区域，并且在大多数情况下无法访问GPS。MAV机载传感器的定位能力较差，不足以在这些环境中进行精确定位。第二个问题是传感器的测量范围很短，这使得它们难以部署在更广泛的区域。 大多数轨迹控制系统依赖于从外部跟踪系统获得的反馈;因此，这些控制器对于感测体积之外的操作无效。另一方面，UGV具有更高的耐久性，可用于更长的操作、更快的处理、长距离传感和高度准确的定位信息。因此，勘探任务正逐步转向多代理实现，以利用其互补能力。在这种协作方法中，可以使用地面机器人进行的相对测量来引导MAV，并且可以将计算要求高的任务委托给强大的UGV。因此，本研究的重点是发展辅助导航和控制系统的微型飞行器使用UGV。 在短期内，该计划试图解决以下关键领域的几个研究主题：（a）使用机器人间传感的MAV轨迹跟踪控制，（B）MAV相对定位使用容积卡尔曼滤波方法，（c）混合控制使用模糊离散事件系统和模型预测控制和（d）多机器人分布式控制和探索。 本提案预计将提供四个博士水平和两个硕士水平的HQP培训。加拿大的许多航空航天和海洋工业现在正试图将自动驾驶汽车用于许多海底和北极勘探任务。 在这个建议中解决的定位和控制问题是高度适用于这样的应用。因此，该项目将在加拿大许多工业部门开展HQP培训和高影响力的研究。

The objective of this research program is to develop control and localization technology for cooperative operation of a heterogeneous multi-robotic system containing both unmanned ground vehicles (UAV) and micro-aerial vehicles (MAV). Recently many industries have begun to explore alternative possibilities of monitoring the environment and critical infrastructures at close distances. The MAVs can navigate through areas that are inaccessible for humans and have the ability to provide 3D perspective of the environment. The payload constraints prohibit MAVs to be deployed autonomously for longer durations covering a wider area and also introduce physical limitations to carry advance sensor suits and powerful computers for accurate positioning. There are two major issues to be resolved. First is that drones are typically deployed in constrained areas and in most cases where GPS is not accessible. The poor localization available with on-board sensors of an MAV is not sufficient for accurate positioning in these settings. The second issue is that the sensors have short range of measurements which makes them difficult to be deployed covering a wider area. Most trajectory control systems rely on feedback obtained from external tracking systems; as a result these controllers are invalid for operations outside the sensing volume. On the other hand, UGVs are available with higher endurance for longer operations, faster processing, long range sensing, and highly accurate localization information. Therefore, exploration missions are progressively moving towards multi-agent implementations to harness their complementary capabilities. In this collaborative approach, the MAVs can be guided using relative measurements taken by the ground robots and the computationally demanding tasks can be delegated to the powerful UGVs. Therefore, this research focuses on developing aided navigation and control systems for MAVs using UGVs. In the short term this program attempts to address several research themes in the following key areas; (a) trajectory tracking control of MAVs using inter-robot sensing, (b) MAV relative localization using cubature Kalman filtering approaches, (c) hybrid control using fuzzy discrete event system and model predictive control and (d) multi-robotic distributed control and exploration. This proposal expects to provide four doctoral level and two Masters level HQP training . Many aerospace and ocean industries in Canada are now attempting to use autonomous vehicles for many sub-sea and arctic exploration missions. The localization and control problems addressed in this proposal are highly applicable for such applications. Therefore, the project will produce HQP training and high impact research relevant across many industrial sectors in Canada.