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23 January 2012 Intelligence algorithms for autonomous navigation in a ground vehicle
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Abstract
This paper will discuss the approach to autonomous navigation used by "Q," an unmanned ground vehicle designed by the Trinity College Robot Study Team to participate in the Intelligent Ground Vehicle Competition (IGVC). For the 2011 competition, Q's intelligence was upgraded in several different areas, resulting in a more robust decision-making process and a more reliable system. In 2010-2011, the software of Q was modified to operate in a modular parallel manner, with all subtasks (including motor control, data acquisition from sensors, image processing, and intelligence) running simultaneously in separate software processes using the National Instruments (NI) LabVIEW programming language. This eliminated processor bottlenecks and increased flexibility in the software architecture. Though overall throughput was increased, the long runtime of the image processing process (150 ms) reduced the precision of Q's realtime decisions. Q had slow reaction times to obstacles detected only by its cameras, such as white lines, and was limited to slow speeds on the course. To address this issue, the image processing software was simplified and also pipelined to increase the image processing throughput and minimize the robot's reaction times. The vision software was also modified to detect differences in the texture of the ground, so that specific surfaces (such as ramps and sand pits) could be identified. While previous iterations of Q failed to detect white lines that were not on a grassy surface, this new software allowed Q to dynamically alter its image processing state so that appropriate thresholds could be applied to detect white lines in changing conditions. In order to maintain an acceptable target heading, a path history algorithm was used to deal with local obstacle fields and GPS waypoints were added to provide a global target heading. These modifications resulted in Q placing 5th in the autonomous challenge and 4th in the navigation challenge at IGVC.
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Steve Petkovsek, Rahul Shakya, Young Ho Shin, Prasanna Gautam, Adam Norton, and David J. Ahlgren "Intelligence algorithms for autonomous navigation in a ground vehicle", Proc. SPIE 8301, Intelligent Robots and Computer Vision XXIX: Algorithms and Techniques, 830115 (23 January 2012); https://doi.org/10.1117/12.909514
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