A genetic algorithm for multi-robot routing in automated bridge inspection

ABSTRACT

We attack the problem of generating balanced and efficient routing for automated inspection by using genetic algorithms to solve the equivalent Min-Max k Windy Chinese Postman Problem. Specifically, we use k robots to collectively inspect every member of a steel truss bridge. Experimental results show that the genetic algorithm produces efficient routes that are well-balanced among the robots. Additionally, we demonstrate that with our novel representation, as the number of robots increases, the generated routes exhibit near-linear speedup in the time needed to complete the inspection task - k robots take [MATHS HERE] the time needed by one robot. Finally, our genetic algorithm produces similar results on a set of benchmark arc routing problem instances from the literature.

References

1. S.K. Amponsah and S. Salhi. 2004. The investigation of a class of capacitated arc routing problems: The collection of garbage in developing countries. Waste Management 24, 7 (2004), 711--721.
2. Enrique Benavent, Ángel Corberán, Isaac Plana, and José María Sanchis. 2009. Min-Max K-vehicles windy rural postman problem. Networks 54 (2009), 216--226.
3. L. J. Eshelman. 1991. The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination. Foundations of Genetic Algorithms (1991), 265--283. http://ci.nii.ac.jp/naid/10000024547/en/
4. Gianpaolo Ghiani, Francesca Guerriero, and Gennaro Improta. 2005. Waste collection in Southern Italy: Solution of a real-life arc routing problem. Int Trans Oper Res 12, 2 (2005), 135--144.
5. Lacomme, Prins, and Sevaux. 2006. A genetic algorithm for a bi-objective capacitated arc routing problem. Computers & Operations Research 33, 12 (2006), 3473--3493.
6. L. Muyldermans, D. Cattrysse, D. Van Oudheusen, and Lotan T. 2002. Districting for salt spreading operations. European Journal of Operational Research 139, 3 (2002), 521--532.

Index Terms

• Computing methodologies

  • Artificial intelligence

    • Planning and scheduling

      • Robotic planning

• Theory of computation

  • Design and analysis of algorithms

    • Graph algorithms analysis

    • Mathematical optimization

      • Discrete optimization