Cooperative human-robot search in a partially-known environment using multiple UAVs

This thesis details out a system developed with objective of conducting cooperative search operation in a partially-known environment, with a human operator, and two Unmanned Aerial Vehicles (UAVs) with nadir, and front on-board cameras. The system uses two phases of flight operations, where the first phase is aimed at gathering latest overhead images of the environment using a UAV’s nadir camera. These images are used to generate and update representations of the environment including 3D reconstruction, mosaic image, occupancy image, and a network graph. During the second phase of flight operations, a human operator marks multiple areas of interest for closer inspection on the mosaic generated in previous step, displayed via a UI. These areas are used by the path planner as visitation goals. The two-step path planner, which uses network graph, utilizes the weighted-A* planning, and Travelling Salesman Problem’s solution to compute an optimal visitation plan. This visitation plan is then converted into Mission waypoints for a second UAV, and are communicated through a navigation module over a MavLink connection. A UAV flying at low altitude, executes the mission plan, and streams a live video from its front-facing camera to a ground station over a wireless network. The human operator views the video on the ground station, and uses it to locate the target object, culminating the mission.