Over the last few years, cooperative autonomous systems have become a popular solution for accomplishing tasks that are otherwise performed by human operators. Several strides have been made with homogeneous systems of vehicles in areas of localization, formation behaviors, path planning, task allocation, and vehicle controls. This literature will describe work performed in the development of a heterogeneous system and architecture of platforms consisting of unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) that cooperatively work together to accomplish tasks such as search and rescue in an outdoor environment. The Robot Operating System (ROS) is used between and within each vehicle to handle cooperative high level planning and task allocation, as well as control of each vehicle's individual capabilities specializing in identification, classification, and tracking.;A description of the system of UAVs, UGVs, and control station, as well as developed vehicle capabilities is given. These different capabilities include locating and characterizing obstacles using LIDAR, or using vision to locate objects through standard color recognition techniques or specialized fiducial tags. Detailed characterizations of a specialized communication protocol, with emphasis on error control, data size management, optimization, as well as the method used for communicating to various vehicles in the field will be provided. The context and use of this protocol is built within a special package that works directly with the ROS communication system, converting data into smaller, more manageable components within a multi-master system.;Mission control between vehicles includes heterogeneous path planning, task allocation, and vehicle control through a custom swarm architecture. This architecture is presented with details on procedure for use, while also providing information on the modular capabilities in generating new missions and vehicle control plugins. Also illustrated is a specialized plugin used within mission planning, the goal planner, which is capable of defining various lawnmower and spiral search paths for many vehicles in a multi-point convex polygon given the varying capabilities of the vehicle. Finally, real environment testing results are given that will characterize the benefits of the heterogeneous architecture and simultaneously show the functionality and application of the system.
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