Operation of an autonomous vehicle along a marked path, in an obstacle-laden environment, requires path detection, relative position detection and control, and obstacle detection and avoidance. The design solution of the team from the U.S. Military Academy is a tracked vehicle operating open-loop in response to position information from an omnidirectional mirror, and to obstacle-detection input from the mirror and from a scanning laser. The use of a tracked rather than a wheeled vehicle is the team's open-loop solution to the problem of wheeled-vehicle slippage on wet and sandy surfaces. The vehiclere-sponds to sensor information from (1) a digital camera-mounted parabolic omnidirectional mirror for visual inputs and (2) a scanning laser for detecting obstacles in relief. Raw sensor data is converted synchronously into a global virtual context, which places the vehicle's center at the origin of a 2-D Cartesian coordinate system. A four-phase process is used to convert the camera's inputs into the data structures needed to reason about the vehicle's position relative to the course. Development of the path plan proceeds incrementally, using a space-sweeping algorithm to identify safe paths along waypoints within the course boundaries. An attempt is made to minimize translation errors by favoring paths which exhibit fewer sharp turns. Integration of Intel's OpenCV computer vision library and the Independent JPEG Group's JPEG library allow for very good encapsulation of the low-level functions needed to do most of the image processing. Ada95 is the language of choice for the majority of the team-developed software, except where needed to interface to motors and sensors. Use of an object-oriented high-level language has been invaluable in leveraging the efforts of previous years' development activities, and for maximizing the ability to log or otherwise respond to anomalous behavior.
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