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A comparative study of modified best-first and randomized algorithms for image-based path-planning

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3 Author(s)
Noborio, H. ; Graduate Sch. of Eng., Osaka Electro-Commun. Univ., Japan ; Naito, S. ; Kawata, D.

In this paper, we propose three types of sensor-based path-planning algorithms and compare them as the image-based path-planning algorithm for a huge search space. In the general image-based path-planning, a camera mounted on a tip of a manipulator is seeking for an objective image while the manipulator is controlled in a joint space. As long as the number of degrees-of-freedom of a manipulator increases, the search space becomes exponentially huge. Though the sensor-based path-planning algorithm is a noncombination search, it sometimes spends much time to escape from a valley minimized by a local minimum. To overcome this problem, we use a modified version of the randomized algorithm as the best image-based path-planning algorithm for a huge search space. This tendency is checked by several simulation results. In addition, the version can be easily applied for a real problem as the classic visual servoing (the steepest descendent method) with memorizing a set of visited points and another set of their neighbor points, as well as generating a sequence of random motions.

Published in:

Robotics and Automation, 2002. Proceedings. ICRA '02. IEEE International Conference on  (Volume:4 )

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