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Deep-reinforcement learning-based route planning with obstacle avoidance for autonomous vessels

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Abstract

This paper proposes a method to enables the generation of short-length routes with consideration of obstacle avoidance and significantly reduces the computation time compared to existing research for ocean route optimization. The reduced computation time allows recalculation of routes for autonomous vessel underway. By simulating the recalculation of four cases of the vessel underway that may require recalculation, this paper demonstrates that the proposed method can generate new and superior routes for the vessel that needs to change their routes due to certain factors.

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Notes

  1. Since n = 20 can be implemented in the model based on the model on n = 50, n = 20 was not trained in this paper.

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Author information

Authors and Affiliations

  1. Graduate School of Informatics, Osaka Metropolitan University, Osaka, Japan

    Ryosuke Saga

  2. Graduate School of Humanities and Sustainable System Sciences, Osaka Prefecture University, Osaka, Japan

    Rinto Kozono

  3. Graduate School of Engineering, Osaka Prefecture University, Osaka, Japan

    Yutaro Tsurumi

  4. Graduate School of Engineering, Osaka Metropolitan University, Osaka, Japan

    Yasunori Nihei

Authors
  1. Ryosuke Saga
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  2. Rinto Kozono
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  3. Yutaro Tsurumi
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  4. Yasunori Nihei
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Corresponding author

Correspondence to Ryosuke Saga.

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This work was presented in part at the joint symposium of the 28th International Symposium on Artificial Life and Robotics, the 8th International Symposium on BioComplexity, and the 6th International Symposium on Swarm Behavior and Bio-Inspired Robotics (Beppu, Oita and Online, January 25–27, 2023).

Appendix

Appendix

Appendix: additional experiments on random obstacles and points at n = 100

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Saga, R., Kozono, R., Tsurumi, Y. et al. Deep-reinforcement learning-based route planning with obstacle avoidance for autonomous vessels. Artif Life Robotics 29, 136–144 (2024). https://doi.org/10.1007/s10015-023-00909-4

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  • DOI: https://doi.org/10.1007/s10015-023-00909-4

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