Diverse multi-path planning with a path-set costmap

• Published in: 2011 11th International Conference on Control, Automation and Systems pp. 694 - 699
• Main Authors: Joon-Hong Seok, Joon-Yong Lee, Changmok Oh, Ju-Jang Lee, Ho Joo Lee
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-10-2011
• Subjects: Evolutionary computation; Genetics; Mobile robots; multi path planning; Optimization; Path diversity; Path planning; path-set costmap; Planning; Shape
• ISBN: 1457708353; 9781457708350
• ISSN: 2093-7121

This paper addresses a planning method to generate well distributed multiple paths in a control space. For this purpose, we employ and combine rapidly-exploring random tree (RRT), evolutionary algorithm (EA) to compose a diverse multi-path planning (DMPP) algorithm. A population is composed of individuals which represent a path-set. Each individual includes a predefined number of feasible path generated by the RRT, one of the sampling-based planners. The proposed method works by building a population with a set of the predefined number of feasible paths by using the RRT, one of the sampling-based planners. As evolving the population with nature selection and genetic operators, more distributed set of the paths can be acquired. The proposed algorithm leads each path element of path-sets to diverge from each other gradually, so that feasible and different paths are well-generated. In order to evaluate the quality and diversity of a path-set, the costmap approach on path elements are also proposed. Experimental results show that the proposed multi-path planning method works well for generating a set of the diverse paths.