A Multi-objective Genetic Algorithm for Path Planning with Micro Aerial Vehicle Test Bed

  • H. David Mathias
  • Vincent R. Ragusa
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 751)


The problem of robotic path planning is relevant to many applications that have led to extensive study. This has only increased as autonomous robotic vehicles have become more affordable and varied. Optimal solutions to this problem can be computationally expensive, leading to the need for efficiently achievable approximate solutions. In this work, we present a genetic algorithm to solve the path planning problem. The algorithm operates offline but runs onboard the micro aerial vehicle (MAV). This is accomplished by mounting a single-board computer on the vehicle and integrating it with the flight control board. In addition, we evaluate the effectiveness of two genetic operators: crossover and mass extinction. Results demonstrate that a standard, single-point crossover operator is largely ineffective. Mass extinction, an operator that has been used rarely in previous work, is explored within the framework of a genetic algorithm utilizing only mutation and selection. Based on initial results, mass extinction may have some utility for path planning, however, due to large number of parameters and potential implementations, additional experimentation is needed.



The authors thank Florida Southern College for financial support of this work. They also thank Annie Wu for valuable insights and feedback, Susan Serrano and Isabel Loyd for assistance with the statistical analysis, and members of the ArduPilot and Dronekit-Python development teams for valuable suggestions for developing flight control client code.


  1. Ahmed, F., Deb, K.: Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms. Tech. Rep. 20111013, Kanpur Genetic Algorithms Laboratory (KanGAL), Indian Institute of Technology (2011)Google Scholar
  2. Al-Sultan, K., Aliyu, M.: A new potential field-based algorithm for path planning. J. Intell. Robot. Syst. 17(3), 265–282 (2010)Google Scholar
  3. Buniyamin, N., Ngah, W.W., Shariff, N., Mohammad, Z.: A simple local path planning algorithm for autonomous mobile robots. J. Syst. Appl. Eng. Dev. 5(2), 151–159 (2011)Google Scholar
  4. Burchardt , H., Salomon, R.: Implementation of path planning using genetic algorithms on mobile robots. In: Proceedings of Congress on Evolutionary Computation, pp. 1831–1836, IEEE (2006)Google Scholar
  5. Choset H., Pignon, P.: Coverage path planning: the boustrophedon decomposition. In: International Conference on Field and Service Robotics (1997)Google Scholar
  6. Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based non-dominated sorting approach, part i: solving problems with box constraints. IEEE Trans. Evol. Comput. 18(4), 577–601 (2014)Google Scholar
  7. Deb, K., Pratap, A., Agarwal, S., Meyarivan ,T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)Google Scholar
  8. Ferguson, D., Likachev, M., Stentz, A.: A guide to heuristic-based path planning. In: Proceedings of International Conference on Automated Planning and Scheduling (2005)Google Scholar
  9. Fonseca, C., Fleming, P.: Multiobjective genetic algorithms. In: IEEE Colloquium on Genetic Algorithms for Control Systems Engineering, vol Digest No. 1993/130 (1993)Google Scholar
  10. Glasius, R., Komoda, A., Gielen, S.: Neural network dynamics for path planning and obstacle avoidance. Neural Networks 8(1), 125–133 (1995)Google Scholar
  11. Goldberg, D.: Genetic algorithms for search, optimization, and machine learning. Addison-Wesley (1989)Google Scholar
  12. Hasircioglu, I., Topcuoglu, H., Ermis, M.: 3-d path planning for the navigation of unmanned aerial vehicles by using evolutionary algorithms. In: Genetic Algorithms and Evolutionary Computation Conference, pp. 1499–1506. ACM (2008)Google Scholar
  13. Hermanu, A., Manikas, T., Ashenayi, K., Wainwright, R.: Autonomous robot navigation using a genetic algorithm with an efficient genotype structure. In: Intelligent Engineering Systems Through Artificial Neural Networks: Smart Engineering Systems Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, omplex Systems and Artificial Life. ASME Press (2004)Google Scholar
  14. Jain, H., Deb, K.: An evolutionary many-objective optimization algorithm using reference-point-based non-dominated sorting approach, part ii: handling constraints and extending to an adaptive approach. IEEE Trans. Evol. Comput. 18(4), 602–622 (2014)Google Scholar
  15. Jaworski, B., Kuczkowski, L., Smierzchalski, R., Kolendo, P.: Extinction event concepts for the evolutionary algorithms. Przeglad Elektrotechniczny (Electrical Review) 88(10b) (2012)Google Scholar
  16. Jun, H., Qingbao, Z.: Multi-objective mobile robot path planning based on improved genetic algorithm. In: IEEE International Conference on Intelligent Computation Technology and Automation, pp. 752–756 (2010)Google Scholar
  17. Knowles, J., Corne, D.: The pareto archived evolution strategy: a new baseline algorithm for pareto multiobjective optimization. In: Proceedings of Congress on Evolutionary Computation (1999)Google Scholar
  18. Konak, A., Coit, D., Smith, A.: multi-objective optimization using genetic algorithms: a tutorial. Reliab. Eng. Syst. Saf. 91 (2006)Google Scholar
  19. Lehman, J., Miikkulainen, R.: Extinction events can accelerate evolution. PLoS ONE. 10(8) (2015)Google Scholar
  20. Li, K., Deb, K., Zhang, Q., Kwong, S.: Efficient non-domination level update approach for steady-state evolutionary multiobjective optimization. Tech. Rep. 2014014, Computational Optimization and Innovation (COIN) Laboratory, Michigan State University (2014)Google Scholar
  21. Lin, HS., Xiao, J., Michalewicz, Z.: Evolutionary navigator for a mobile robot. In: International Conference on Evolutionary Computation, pp. 2199–2204. IEEE (1994)Google Scholar
  22. Mathias, D., Ragusa, V.: An empirical study of crossover and mass extinction in a genetic algorithm for pathfinding in a continuous environment. In: Proceedings of Congress on Evolutionary Computation, IEEE (2016)Google Scholar
  23. Mengshoel, O., Goldberg, D.: The crowding approach to niching in genetic algorithms. Evol. Comput. 16(3), 315–354 (2008)Google Scholar
  24. Page, W., McDonnell, J., Anderson, B.: An evolutionary programming approach to multidimensional path planning. In: First Annual Conference on Evolutionary Programming, pp. 63–70 (1992)Google Scholar
  25. Sedighi, K., Ashenayi, K., Manikas, T., Wainwright, R., Tai, H.M.: Autonomous local path planning for a mobile robot using a genetic algorithm. In: Proceedings of Congress on Evolutionary Computation, pp. 1338–1345, IEEE (2004)Google Scholar
  26. Siddiqi, U., Shriraishi, Y., Sait, S.: Memory-efficient genetic algorithm for path optimization in embedded systems. IPSJ Trans. Math. Model. Appl. 6(1) (2013)Google Scholar
  27. Srinivas, N., Deb, K.: Multiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2(3), 221–248 (1994)Google Scholar
  28. Tonupunuri, P.: Evolutionary based path-finding for mobile agents in sensor networks. Master’s thesis, Southern Illinois University (2008)Google Scholar
  29. Xiao, J., Michalewicz, Z., Zhang, L., Trojanowski, K.: Adaptive evolutionary planner/navigator for mobile robots. IEEE Trans. Evol. Comput. 1(1) (1997)Google Scholar
  30. Yun, G., Lu, H.: Dynamic multiobjective evolutionary algorithm: adaptive cell-based rank and density estimation. IEEE Trans. Evol. Comput. 7(3), 253–274 (2003)Google Scholar
  31. Zhang, Q., Li, H.: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6) (2007)Google Scholar
  32. Zheng, C., Ding, M., Zhou, C., Li, L.: Coevolving and cooperating path planner for multiple unmanned air vehicles. Eng. Appl. Artif. Intell. 17, 887–896 (2004)Google Scholar
  33. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength of the pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)Google Scholar
  34. Zitzler, E., Laumanns, M., Thiele, L.: Spea2: improving the strength of pareto evolutionary algorithms. Tech. Rep., ETH Zurich (2001)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Florida Southern CollegeLakelandUSA

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