Abstract
We present a new approach to path planning based on the properties of the minimum enclosing ball (MEB) in a reproducing kernel space. The algorithm is designed to find paths in high-dimensional continuous spaces and can be applied to robots with many degrees of freedom in static as well as dynamic environments. In the proposed method a sample of points from free space is enclosed in a kernel space MEB. In this way the interior of the MEB becomes a representation of free space in kernel space. If both start and goal positions are interior points in the MEB a collision-free path is given by the line, contained in the MEB, connecting them. The points in work space that satisfy the implicit conditions for that line in kernel space define the desired path. The proposed algorithm was experimentally tested on a workspace cluttered with random and non random distributed obstacles. With very little computational effort, in all cases, a satisfactory free collision path could be calculated.
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Moreno, J.A., García, C. (2007). Robot Path Planning in Kernel Space. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71629-7_75
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DOI: https://doi.org/10.1007/978-3-540-71629-7_75
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