Robot Path Integration in Manufacturing Processes: Genetic Algorithm Versus Ant Colony Optimization

• Published in: IEEE transactions on systems, man and cybernetics. Part A, Systems and humans Vol. 38; no. 2; pp. 278 - 287
• Main Authors: Tewolde, G.S., Weihua Sheng
• Format: Journal Article
• Language: English
• Published: IEEE 01.03.2008
• Subjects: Ant colony optimization; Ant colony optimization (ACO); Design automation; Formicidae; genetic algorithm (GA); Genetic algorithms; Glass; Manufacturing automation; Manufacturing processes; path integration; Path planning; Robotics and automation; Robots; spray forming; Spraying; tool path planning; genetic algorithm (GA); Ant colony optimization (ACO); path integration; tool path planning; spray forming
• ISSN: 1083-4427; 1558-2426
• DOI: 10.1109/TSMCA.2007.914769

Tool path planning for automated manufacturing processes is a computationally complex task. This paper addresses the problem of tool path integration in the context of spray-forming processes. Tool paths for geometry-complicated parts are generated by partitioning them into individual freeform surfaces, generating the paths for each partition, and then, finally, interconnecting the paths from the different patches so as to minimize the overall path length. We model the problem as a variant of the rural postman problem (RPP), which we call open-RPP. In this paper, we present two different solutions to the open-RPP. The first solution is based on genetic algorithms and the second one is based on ant colony optimization. This paper presents and compares the results from both methods on sample data and on real-world automotive body parts. We conclude this paper with remarks about the effectiveness of our implementations and the pros and cons of each method.