An auto-adaptive convex map generating path-finding algorithm: Genetic Convex A

• Published in: International journal of machine learning and cybernetics Vol. 4; no. 5; pp. 551 - 563
• Main Authors: Su, Pan, Li, Yan, Li, Yingjie, Shiu, Simon Chi-Keung
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2013; Springer Nature B.V
• Subjects: Artificial Intelligence; Complex Systems; Computational Intelligence; Computer & video games; Control; Engineering; Genetic algorithms; Global positioning systems; GPS; Graphs; Heuristic; Mechatronics; Nodes; Optimization; Original Article; Pattern Recognition; Robot control; Robotics; Systems Biology; Convex map; Path-finding; G-CA; Abstract graph; Genetic algorithm
• ISSN: 1868-8071; 1868-808X
• DOI: 10.1007/s13042-012-0120-x

Path-finding is a fundamental problem in many applications, such as robot control, global positioning system and computer games. Since A * is time-consuming when applied to large maps, some abstraction methods have been proposed. Abstractions can greatly speedup on-line path-finding by combing the abstract and the original maps. However, most of these methods do not consider obstacle distributions, which may result in unnecessary storage and non-optimal paths in certain open areas. In this paper, a new abstract graph-based path-finding method named Genetic Convex A * is proposed. An important convex map concept which guides the partition of the original map is defined. It is proven that the path length between any two nodes within a convex map is equal to their Manhattan distance. Based on the convex map, a fitness function is defined to improve the extraction of key nodes; and genetic algorithm is employed to optimize the abstraction. Finally, the on-line refinement is accelerated by Convex A * , which is a fast alternative to A * on convex maps. Experimental results demonstrated that the proposed abstraction generated by Genetic Convex A * guarantees the optimality of the path whilst searches less nodes during the on-line processing.