| Summary: | Unmanned autonomous vehicles, airborne or terrestrial, can be used to solve many varying tasks in vastly different environments. This thesis describes a proposed collaboration between two types of such vehicles, namely unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). The vehicles' objective is to traverse unknown terrain in order to access a target area. The exploration of the unknown terrain is in this thesis divided into three parts. These parts are terrain mapping, informative path planning (IPP) for the UAVs and path planning for the UGV. A Gaussian Process (GP) is used to model the terrain. The use of a GP map makes it possible to model spatial dependence and to interpolate data between measurements. Furthermore, sequential update of the map is achieved with a Kalman filter when new measurements are collected. In the second part, IPP is used to decide the best locations for the terrain height measurements. The IPP algorithm will prioritize measurements in locations with uncertain terrain height estimates in order to lower the overall map uncertainty. Finally, when the map is complete, the UGV plans an optimal path through the mapped terrain using A* graph search, while minimizing the total altitude difference for the path and respecting the map uncertainty. Collaborative behavior of autonomous vehicles requires highly accurate position estimates. In this thesis RTK is investigated and its accuracy and precision evaluated for the positioning of autonomous UAVs and UGVs through a series of experiments. The experiments range from stationary and dynamic accuracy to investigation of the consistency of the positioning estimates. The results are promising, RTK outperforms standard GNSS and can be used for centimeter-level accuracy when positioning a UAV in-flight. The proposed exploration algorithms are evaluated in simulations. The results show that the algorithms successfully solves the task of mapping and traversing unknown terrain. IPP makes the mapping of the unknown terrain efficient, which enables the possibility to use the resulting map to plan safe paths for the UGV. Traversing unknown terrain is hard for a single UGV but with the help from one or more UAVs the process is much more efficient. The use of multiple cooperating autonomous vehicles makes it possible to solve tasks complicated for the individual vehicle in an efficient manner.
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