Current studies on cable harness layouts have mainly focused on cable harness route planning. However, the topological structure of a cable harness is also extremely complex, and the branch structure of the cable harness can affect the route of the cable harness layout. The topological structure design of the cable harness is a key to such a layout. In this paper, a novel multi-branch cable harness layout design method is presented, which unites the probabilistic roadmap method (PRM) and the genetic algorithm. First, the engineering constraints of the cable harness layout are presented. An obstacle-based PRM used to construct non-interference and near to the surface roadmap is then described. In addition, a new genetic algorithm is proposed, and the algorithm structure of which is redesigned. In addition, the operation probability formula related to fitness is proposed to promote the efficiency of the branch structure design of the cable harness. A prototype system of a cable harness layout design was developed based on the method described in this study, and the method is applied to two scenarios to verify that a quality cable harness layout can be efficiently obtained using the proposed method. In summary, the cable harness layout design method described in this study can be used to quickly design a reasonable topological structure of a cable harness and to search for the corresponding routes of such a harness.
Current studies on cable harness layouts have mainly focused on cable harness route planning. However, the topological structure of a cable harness is also extremely complex, and the branch structure of the cable harness can affect the route of the cable harness layout. The topological structure design of the cable harness is a key to such a layout. In this paper, a novel multi-branch cable harness layout design method is presented, which unites the probabilistic roadmap method (PRM) and the genetic algorithm. First, the engineering constraints of the cable harness layout are presented. An obstacle-based PRM used to construct non-interference and near to the surface roadmap is then described. In addition, a new genetic algorithm is proposed, and the algorithm structure of which is redesigned. In addition, the operation probability formula related to fitness is proposed to promote the efficiency of the branch structure design of the cable harness. A prototype system of a cable harness layout design was developed based on the method described in this study, and the method is applied to two scenarios to verify that a quality cable harness layout can be efficiently obtained using the proposed method. In summary, the cable harness layout design method described in this study can be used to quickly design a reasonable topological structure of a cable harness and to search for the corresponding routes of such a harness.
[1] H Park, H Lee, M R Cutkosky. Computational support for concurrent engineering of cable harnesses. Computes in Engineering, 1992, 1: 261-268.
[2] P B Holt, J M Ritchie, P N Day, et al. Immersive virtual reality in cable and pipe routing: design metaphors and cognitive ergonomics. Journal of Computing and Information Science in Engineering, 2004, 4(3): 161-170.
[3] G Robinson, J M Ritchie, P N Day, et al. System design and user evaluation of Co-Star: An immersive stereoscopic system for cable harness design. Computer-Aided Design, 2007, 39(4): 245-257.
[4] D Zhu, J C Latombe. Pipe routing = path planning (with many constraints). Proceedings of the 1991 IEEE International Conference on Robotics and Automation, Sacramento, California, 1991: 1940-1947.
[5] A B Conru. A genetic approach to the cable harness routing problem. IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, 1994.
[6] M Schäfer, T Lengauer. Automated layout generation and wiring area estimation for 3D electronic modules. Journal of Mechanical Design, 2001, 123(3): 330-336.
[7] X Ma, K Iida, M Xie, et al. A genetic algorithm for the optimization of cable routing. Systems and Computers in Japan, 2006, 37(7): 61-71.
[8] I Kabul, R Gayle, M C Lin. Cable route planning in complex environments using constrained sampling. Proceedings of the 2007 ACM Symposium on Solid and Physical Modeling, 2007: 395-402.
[9] C W Lin, L Rao, P Giusto, et al. Efficient wire routing and wire sizing for weight minimization of automotive systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2015, 34(11): 1730-1741.
[10] T Hermansson, R Bohlin, J S Carlson, et al. Automatic routing of flexible 1D components with functional and manufacturing constraints. Computer-Aided Design, 2016, 79: 27-35.
[11] B J Xu. Research on multi-branch cable automatic layout design technology for complex products. Beijing: Beijing Institute of Technology, 2015. (in Chinese)
[12] B J Xu, J H Liu, J S L, et al. Branch cable automatic routing based on co-evolutionary algorithm. Journal of Graphics, 2016, 37(1): 25-33. (in Chinese)
[13] B J Xu, J H Liu, J S L, et al. Multi-branch cable automatic routing by SMT and LDOB-PRM algorithm. Computer Intergrated Manufacturing Systems, 2016, 22(9): 2099-2107. (in Chinese)
[14] F L Wang, W H Liao, Y Guo, et al. Three dimensional space-wiring technology for cable harness based on multi-scale chaotic mutation particle swarm optimization algorithm. Journal of Mechanical Engineering, 2017, 53(9): 144-156. (in Chinese)
[15] B S Wu, Y Guo, F L Wang, et al. Research on path planning of cable harness based on improved ant colony optimization. Computer Engineering and Applications, 2018, 54(10): 236-241. (in Chinese)
[16] R Geraerts, M H Overmars. Sampling and node adding in probabilistic roadmap planners. Robotics and Autonomous Systems, 2006, 54(2): 165-173.
[17] L E Kavraki, P Svestka, J C Latombe, et al. Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation, 1996, 12(4): 566-580.
[18] N M Amato, O B Bayazit, L K Dale. OBPRM: An obstacle-based PRM for 3D workspaces. Found of Robotics, 1998.
[19] Y T Lin. The gaussian PRM sampling for dynamic configuration spaces. 9th International Conference on Control, Automation, Robotics and Vision, ICARCV Singapore, Proceedings. IEEE, 2006.
[20] D Hsu, T Jiang, J Reif, et al. The bridge test for sampling narrow passages with probabilistic roadmap planners. IEEE Intelligent Conference on Robotics and Automation, 2003: 4420-4426.
[21] H Yeh, S Thomas, D Eppstein, et al. UOBPRM: A uniformly distributed obstacle-based PRM. 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura, 2012: 2655-2662.
[22] R Geraerts, M H Overmars. A comparative study of probabilistic roadmap planners. Springer Tracts in Advanced Robotics, 2003, 7: 43-58.
[23] J H Holland. Adaption in Natural and Artificial Systems. Cambridge, MA: MIT Press, 1975.
[24] R B Hollstien. Artificial genetic adaptation in computer control systems. University of Michigan, Ann Arbor, 1971.
[25] C Z Janikow, Z Michalewicz. An experimental comparison of binary and floating point representations in genetic algorithms. Proceeding 4th Intelligent Conference Genetic Algorithms, July, 1991: 31-36.
[26] A H Wright. Genetic algorithms for real parameter optimization. Foundations of Genetic Algorithms, 1991, 1: 205-218.
[27] Y Deng, Y Liu, D Zhou. An improved genetic algorithm with initial population strategy for symmetric TSP. Mathematical Problems in Engineering, 2015.
[28] E Osaba, R Carballedo, D Fernando, et al. Analysis of the suitability of using blind crossover operators in genetic algorithms for solving routing problems. IEEE International Symposium on Applied Computational Intelligence & Informatics, 2013.
[29] M Srinivas, L M Patnaik. Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics, 1994, 24(4): 656-667.
[30] J Zhang, S H Chung, W L Lo. Clustering-based adaptive crossover and mutation probabilities for genetic algorithm. IEEE Transactions on Evolutionary Computation, 2007, 11(3): 326-335.
[31] E Osaba, E Onieva, R Carballedo, et al. An adaptive multi-crossover population algorithm for solving routing problems. Nature Inspired Cooperative Strategies for Optimization, 2013: 512.
[32] Baker, James Edward. Adaptive selection methods for genetic algorithms. Proceedings of an International Conference on Genetic Algorithms and Their Applications, 1985.
[33] D E Goldberg. A comparative analysis of selection schemes used in genetic algorithms. Foundations of Genetic Algorithms, 1991.
[34] D Datta, K Deb, C M Fonseca, et al. Multi-objective evolutionary algorithm for land-use management problem. International Journal of Computational Intelligence Research, 2007, 3(4): 1-24.
[35] S Baluja, R Caruana. Removing the genetics from the standard genetic algorithm. Machine Learning Proceedings, 1995: 38-46.
