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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.

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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

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  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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