Paper Info
Title
Robot Path Planning in Kernel Space
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.
Year
DOI
Venue
2007
10.1007/978-3-540-71629-7_75
ICANNGA (2)
Keywords
Field
DocType
kernel space,interior point,kernel space meb,free space,collision-free path,satisfactory free collision path,work space,robot path planning,reproducing kernel space,proposed algorithm,high-dimensional continuous space,path planning,satisfiability,degree of freedom
Any-angle path planning,Computer science,Workspace,Artificial intelligence,Kernel (linear algebra),Motion planning,Topology,Mathematical optimization,Kernel embedding of distributions,Collision,Robot,Kernel method,Machine learning
Conference
Volume
ISSN
Citations 
4432
0302-9743
0
PageRank 
References 
Authors
0.34
8
2
Name
Order
Citations
PageRank
José Alí Moreno1658.60
Cristina García2193.56