In this study, as a proof-of-concept, the cooperation of an Unmanned Ground Vehicle (UGV) and an Unmanned Aerial Vehicle (UAV) in building and updating an obstacle map by providing information of the area of operation from different vantage points is presented. The small tracked skid steer UGV used in this study is equipped with a Laser Range Finder (LRF) that can only detect the front face of "positive" obstacles and it has difficulty detecting "negative" obstacles like pits, holes, or trenches. This work uses a camera mounted on an indoor blimp as the UAV-based aerial sensor in order to improve the ability to detect both "negative" and "positive" obstacles. In addition to detecting "negative" obstacles the UAV-based camera can also enhance the detection of "positive" obstacles that fall in the LRF shadow created by closer objects. The fusion of aerial and ground-based obstacle information is achieved by the Probabilistic Threat Exposure Map (PTEM) mathematical formulation, which represents the area of operation that contains various types of threats, obstacles, and restricted areas, in a single framework. Experiment results demonstrated that the UGV can avoid a negative obstacle during waypoint navigation based on the PTEM constructed from aerial negative obstacle information. These activities are a part of a larger effort to establish a theoretical foundation for autonomous and cooperative multi-UxV guidance solutions in adversarial environments.
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