Autonomous vehicle development is a rapidly growing field that has vast possibilities for both military and commercial applications. Removing people from dangerous tasks will save lives. Continued research is necessary in order to build these new technologies and mature those already established. One area of potential in the unmanned vehicle community is that of fully autonomous cooperation. This area of research will allow multiple unmanned platforms to perform new functions on a larger scale by combining their capabilities in a coordinated manner. This thesis addresses the emerging need of research related to fully autonomous cooperation between a heterogeneous team of vehicles, by taking a system level approach and integrating the necessary technologies. Software was developed and then tested that combines an unmanned ground vehicle and an unmanned aerial vehicle in order to perform a task that utilizes the strengths of each platform. The ground vehicle is programmed with a route for which it sends look-ahead waypoints to the aircraft. As it traverses the route, the aircraft searches for possible targets. If a target is detected, the approximate coordinates are sent over the network and the ground vehicle then further localizes and inspects the target. Once the inspection is completed, the ground vehicle continues on its previous route. This thesis demonstrates that pairing ground and aerial vehicles in a fully autonomous target localization problem can indeed provide a team functioning more efficiently than either alone. / Master of Science

Robots have become increasingly adept at performing a wide variety of tasks in the world. However, many of these tasks can benefit tremendously from having more than a single robot simultaneously working on the problem. Multiple robots can aid in a search and rescue mission each scouting a subsection of the entire area in order to cover it quicker than a single robot can. Alternatively, robots with different abilities can collaborate in order to achieve goals that individually would be more difficult, if not impossible, to achieve. In these cases, multi-robot collaboration can provide benefits in terms of shortening search times, providing a larger mix of sensing, computing, and manipulation capabilities, or providing redundancy to the system for communications or mission accomplishment. One principle drawback of multi-robot systems is how to efficiently and effectively generate plans that use each of the team members to their fullest extent, particularly with a heterogeneous mix of capabilities. Towards this goal, I have developed a series of planning algorithms that incorporate this collaboration into the planning process. Starting with systems that use collaboration in an exploration task I show teams of homogeneous ground robots planning to efficiently explore an initially unknown space. These robots share map information and in a centralized fashion determine the best goal location for each taking into account the information gained by other robots as they move. This work is followed up with a similar exploration scheme but this time expanded to a heterogeneous air-ground robot team operating in a full 3-dimensional environment. The extra dimension adds the requirement for the robots to reason about what portions of the environment they can sense during the planning process. With an air-ground team, there are portions of the environment that can only be sensed by one of the two robots and that information informs the algorithm during the planning process. Finally, I extend the air-ground robot team to moving beyond merely collaboratively constructing the map to actually using the other robots to provide pose information for the sensor and computationally limited team members. By explicitly reasoning about when and where the robots must collaborate during the planning process, this approach can generate trajectories that are not feasible to execute if planning occurred on an individual robot basis. An additional contribution of this thesis is the development of the State Lattice Planning with Controller-based Motion Primitives (SLC) framework. While SLC was developed to support the collaborative localization of multiple robots, it can also be used by a single robot to provide a more robust means of planning. For example, using the SLC algorithm to plan using a combination of vision-based and metric-based motion primitives allows a robot to traverse a GPS-denied region.