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GPU-based Global Path Planning Using Genetic Algorithm with Near Corner Initialization

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Abstract

This paper presents a global path planning framework and method that utilizes genetic algorithm (GA) optimization on a highly parallelized Graphics Processing Unit (GPU) platform to achieve salient computing performance. A method to randomly initialize waypoints in the free space near obstacle corners is proposed, which in conjunction with mutation in the free space shows great advantages over other methods in reaching low fitness value. Furthermore, the migration process is introduced into the GA to mitigate the issue of premature convergence. To determine best GA configurations, a tradeoff analysis is conducted, and it is found that the runtime is minimized and optimization accuracy is preserved when the number of populations and individuals are selected as 640 and 64. The number of generations is selected as 1,000 based on the convergence rate of GA optimization. An objective function enabling differential consideration of the path length, smoothness, safety, and feasibility through individual weights is also presented. Numerical experiments demonstrate that different optimal paths can be obtained from the same terrain by tuning the weights. Compared to its serial CPU counterpart, the average speedup achieved by GPU is 83×.

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Data Availability

Not applicable.

Code Availability

The code used for this study is available from corresponding author by request.

References

  1. Elhoseny, M., Tharwat, A., Hassanien, A.E.: Bezier curve based path planning in a dynamic field using modified genetic algorithm. J. Comput. Sci. 25, 339–350 (2018)

    Article  Google Scholar 

  2. Zafar, M.N., J., M.C.: Methodology for path planning and optimization of mobile robots: a review. Procedia Comput. Sci. 133, 141–152 (2018)

    Article  Google Scholar 

  3. Pandey, A., Parhi, D.R.: Optimum path planning of mobile robot in unknown static and dynamic environments using Fuzzy-Wind Driven Optimization algorithm. Def. Technol. 13(1), 47–58 (2017)

    Article  Google Scholar 

  4. MahmoudZadeh, S., Yazdani, M.A., Sammut, K., Powers, D.: Online path planning for AUV rendezvous in dynamic cluttered undersea environment using evolutionary algorithms. Appl. Soft Comput. 70, 929–945 (2018)

    Article  Google Scholar 

  5. Nazarahari, M., Khanmirza, E., Doostie, S.: Multi-objective multi-robot path planning in continuous environment. Expert Syst. Appl. 115, 106–120 (2019)

    Article  Google Scholar 

  6. Patle, K.B., Pandey, L.G.B., Parhi, A., D., & Jagadeesh, A.: A review: On path planning strategies for navigation of mobile robot. Def. Technol. 15(4), 582–606 (2019)

    Article  Google Scholar 

  7. Wang, H., Duan, J., Wang, M., Zhao, J., Dong, Z.: Research on robot path planning based on fuzzy neural network algorithm. 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference, pp.1800-1803. IEEE, Chongqing (2018)

  8. Lv, Q., Yang, D.: Multi-target path planning for mobile robot based on improved PSO algorithm. 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference, pp. 1042-1047. IEEE, Chongqing (2020)

  9. Bakdi, A., Hentout, A., Boutami, H., Maoudj, A., Hachour, O., Bouzouia, B.: Optimal path planning and execution for mobile robots using genetic algorithm and adaptive fuzzy-logic control. Robot. Auton. Syst. 89, 95–109 (2017)

    Article  Google Scholar 

  10. Mac, T.T., Copot, C., Tran, T.D., Keyser, D.R.: A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization. Appl. Soft Comput. 59, 68–76 (2017)

    Article  Google Scholar 

  11. Ali, H., Gong, D., Wang, M., Dai, X.: Path planning of mobile robot with improved ant colony algorithm and MDP to produce smooth trajectory in grid-based environment. Front. Neurorobot. 14, 44 (2020)

    Article  Google Scholar 

  12. Nie, Z., Zhao, H.: Research on robot path planning based on dijkstra and ant colony optimization. In 2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), pp. 222-226. IEEE (2019)

  13. Farzan, S., DeSouza, G.N.: Path planning in dynamic environments using time-warped grids and a parallel implementation. arXiv preprint arXiv:1903.07441 (2019)

  14. Hidalgo-Paniagua, A., Bandera, J.P., Ruiz-de-Quintanilla, M., Bandera, A.: Quad-RRT: A real-time GPU-based global path planner in large-scale real environments. Expert Syst. Appl. 99, 141–154 (2018)

    Article  Google Scholar 

  15. Juelg, C., Hermann, A., Roennau, A., Dillmann, R.: Fast online collision avoidance for mobile service robots through potential fields on 3D environment data processed on GPUs. In 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 921-928. IEEE (2017)

  16. Xue, Y.: Mobile robot path planning with a non-dominated sorting genetic algorithm. Appl. Sci. 8(11), 2253 (2018)

    Article  Google Scholar 

  17. Lamini, C., Benhlima, S., Elbekri, A.: Genetic algorithm based approach for autonomous mobile robot path planning. Procedia Comput. Sci. 127, 180–189 (2018)

    Article  Google Scholar 

  18. Hong, S.H., Cornelius, J., Wang, Y., Pant, K.: Optimized artificial neural network model and compensator in model predictive control for anomaly mitigation. J. Dyn. Syst. Meas. Control 143(5) (2021)

  19. Hong, S.H., Cornelius, J., Wang, Y., Pant, K.: Fault compensation by online updating of genetic algorithm-selected neural network model for model predictive control. SN Appl. Sci. 1(11), 1488 (2019)

    Article  Google Scholar 

  20. Yang, H., Hong, S.H., ZhG, R., Wang, Y.: Surrogate-based optimization with adaptive sampling for microfluidic concentration gradient generator design. RSC Adv. 10(23), 13799–13814 (2020)

    Article  Google Scholar 

  21. Hong, S.H., Shu, JI., Ou, J. et al.: GPU-enabled microfluidic design automation for concentration gradient generators. Engineering with Computers (2022). https://doi.org/10.1007/s00366-021-01548-8

  22. Idrees, A.K., Al-Yaseen, W.L.: Distributed genetic algorithm for lifetime coverage optimisation in wireless sensor networks. Int. J. Adv. Intell. Paradigms 18(1), 3–24 (2021)

    Article  Google Scholar 

  23. Park, H., Son, D., Koo, B., Jeong, B.: Waiting strategy for the vehicle routing problem with simultaneous pickup and delivery using genetic algorithm. Expert. Syst. Appl. 165, 113959 (2021)

    Article  Google Scholar 

  24. Han, J., Seo, Y.: Mobile robot path planning with surrounding point set and path. Appl. Soft Comput. 57, 35–47 (2017)

    Article  Google Scholar 

  25. Shivgan, R., Dong, Z.: Energy-efficient drone coverage path planning using genetic algorithm. 2020 IEEE 21st International Conference on High Performance Switching and Routing, pp. 1-6. Newark: IEEE (2020)

  26. yazıcı, bC.: towards data science. Retrieved from continuous genetic algorithm from scratch with python: https://towardsdatascience.com/continuous-genetic-algorithm-from-scratch-with-python-ff29deedd099 (2019). Accessed 25 Jan 2022

  27. Parallel Algorithm - Sorting: Retrieved from tutorial points: https://www.tutorialspoint.com/parallel_algorithm/parallel_algorithm_sorting.htm#:~:tex (n.d.)

  28. Roberge, V., Tarbouchi, M.: Fast path planning for unmaned aerial vehicle using embedded GPU system. 2017 14th International Multi-Conference on Systems, Signals & Devices (SSD). IEEE, Marrakech (2017)

  29. Patle, B., Parhi, D., Jagadeesh, A., Kashyap, S.K.: Matrix-binary codes based genetic Algorithm for path planning of mobile robot. Comput. Electr. Eng. 67, 708–728 (2018)

  30. Lamini, C., Benhlima, S., Elbekri, A.: Genetic algorithm based approach for autonomous mobile robot path planning. Procedia Comput. Sci. 127, 180–189 (2018)

    Article  Google Scholar 

  31. Yanhui, L., Zhonghua, H., Xie, Y.: Path planning of mobile robot based on improved genetic algorithm. 2020 3rd International Conference on Electron Device and Mechanical Engineering, pp. 691-695. IEEE, Suzhou (2020)

  32. Balakrishnan, K.: Parallel genetic algorithms, premature convergence and the nCUBE. Coms 625x Term Project, 1-19 (1993)

  33. Pandey, H.M., Chaudhary, A., Mehrotra, D.: A comparative review of approaches to prevent premature convergence in GA. Appl. Soft Comput. 24, 1047–1077 (2014)

    Article  Google Scholar 

  34. Izzo, D., Ruciński, M., Biscani, F.: The generalized island model. In F. Fernández de Vega, J. Ignacio Hidalgo Pérez, & J. Lanchares, Parallel Architectures and Bioinspired Algorithms, pp. 151-169. Springer, Berlin, Heidelberg (2012)

  35. Zhang, Y., Dai, E., Tong-hui, R.: Path planning of mobile robot based on improved genetic algorithm. 2016 2nd International Conference on Mechanical, Electronic and Information Technology Engineering, pp. 398-404. DEStech Transactions on Engineering and Technology Research (2016)

  36. Zhang, Y., Gong, D., Zhang, J.: Robot path planning in uncertain environment using multi-objective particle swarm optimization. Neurocomputing. 103, 172–185 (2013)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, University of South Carolina, Columbia, SC, 29208, USA

    Junlin Ou, Seong Hyeon Hong, Paul Ziehl & Yi Wang

  2. Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC, 29208, USA

    Paul Ziehl

Authors
  1. Junlin Ou
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  2. Seong Hyeon Hong
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  3. Paul Ziehl
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  4. Yi Wang
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Contributions

Conceptualization: Junlin Ou, Seong Hyeon Hong, Yi Wang; Methodology: Junlin Ou; Formal analysis and investigation: Junlin Ou, Seong Hyeon Hong, Yi Wang; Writing - original draft preparation: Junlin Ou; Writing - review and editing: Seong Hyeon Hong, Yi Wang, Paul Ziehl1.

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Correspondence to Yi Wang.

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Ou, J., Hong, S.H., Ziehl, P. et al. GPU-based Global Path Planning Using Genetic Algorithm with Near Corner Initialization. J Intell Robot Syst 104, 34 (2022). https://doi.org/10.1007/s10846-022-01576-6

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  • DOI: https://doi.org/10.1007/s10846-022-01576-6

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