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 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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Title
UGV and UAV cooperation for constructing probabilistic threat exposure map (PTEM)
Publication Details
15th AIAA Aviation Technology, Integration, and Operations Conference, 22-26 June 2015, Dallas, TX
Resource Type
Conference proceeding
Publisher
American Institute of Aeronautics and Astronautics; United States
Copyright
Permission granted to the University of West Florida Libraries to digitize and/or display this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires the permission of the copyright holder.
Identifiers
99380090791706600
Academic Unit
Intelligent Systems and Robotics; Hal Marcus College of Science and Engineering