Coordination and Control of Ground-Aerial Robotic Systems

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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.