Journal of Ocean Engineering and Technology (한국해양공학회지)

Korean Society of Ocean Engineers (한국해양공학회)
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Mission Planning for Underwater Survey with Autonomous Marine Vehicles

  • Received : 2021.12.13
  • Accepted : 2022.01.20
  • Published : 2022.02.28
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Abstract

With the advancement of intelligent vehicles and unmanned systems, there is a growing interest in underwater surveys using autonomous marine vehicles (AMVs). This study presents an automated planning strategy for a long-term survey mission using a fleet of AMVs consisting of autonomous surface vehicles and autonomous underwater vehicles. Due to the complex nature of the mission, the actions of the vehicle must be of high-level abstraction, which means that the actions indicate not only motion of the vehicle but also symbols and semantics, such as those corresponding to deploy, charge, and survey. For automated planning, the planning domain definition language (PDDL) was employed to construct a mission planner for realizing a powerful and flexible planning system. Despite being able to handle abstract actions, such high-level planners have difficulty in efficiently optimizing numerical objectives such as obtaining the shortest route given multiple destinations. To alleviate this issue, a widely known technique in operations research was additionally employed, which limited the solution space so that the high-level planner could devise efficient plans. For a comprehensive evaluation of the proposed method, various PDDL-based planners with different parameter settings were implemented, and their performances were compared through simulation. The simulation result shows that the proposed method outperformed the baseline solutions by yielding plans that completed the missions more quickly, thereby demonstrating the efficacy of the proposed methodology.

Keywords

Acknowledgement

This research was supported by National R&D Program through the National Research Foundation of Korea (NRF) funded by Ministry of Science and ICT (2020M3C1C1A02086303).

References

  1. Benton, J., Coles, A., & Coles, A. (2012, May). Temporal Planning with Preferences and Time-Dependent Continuous Costs. Proceedings of the Twenty-Second International Conference on Automated Planning and Scheduling.
  2. Carreno, Y., Pairet, E., Petillot, Y., & Petrick, R.P. (2020, June). A Decentralised Strategy for Heterogeneous AUV Missions via Goal Distribution and Temporal Planning. Proceedings of the International Conference on Automated Planning and Scheduling, 30(1), 431-439. Retrieved from https://ojs.aaai.org/index.php/ICAPS/article/view/6738
  3. Coles, A., Coles, A., Fox, M., & Long, D. (2021). Forward-Chaining Partial-Order Planning. Proceedings of the International Conference on Automated Planning and Scheduling, 20(1), 42-49. Retrieved from https://ojs.aaai.org/index.php/ICAPS/article/view/13403
  4. Coles, A., Coles, A., Fox, M., & Long, D. (2012). COLIN: Planning with Continuous Linear Numeric Change. Journal of Artificial Intelligence Research, 44, 1-96. https://doi.org/10.1613/jair.3608
  5. Elkins, L., Sellers, D., & Monach, W.R. (2010). The Autonomous Maritime Navigation (AMN) Project: Field tests, Autonomous and Cooperative Behaviors, Data Fusion, Sensors, and Vehicles. Journal of Field Robotics, 27(6), 790-818. https://doi.org/10.1002/rob.20367
  6. Geffner, H.A. (2003). PDDL 2.1: Representation vs. Computation. Journal of Artificial Intelligence Research, 20, 139-144. https://doi.org/10.1613/jair.1995
  7. Kim, B., Wang, Z., Kaelbling, L.P., & Lozano-Perez, T. (2019). Learning to Guide Task and Motion Planning Using Score-Space Representation. The International Journal of Robotics Research, 38(7), 793-812. https://doi.org/10.1177/0278364919848837
  8. Kim, W.J., & Lee, K. (2018). A Study of the Development Test and Evaluation and Verification Procedure of a Multi-Mission USV, M-Searcher. Journal of Ocean Engineering and Technology, 32(5), 402-409. https://doi.org/10.26748/KSOE.2018.6.32.5.402
  9. Martinez, N.L., Martinez-Ortega, J.F., Castillejo, P., & Beltran Martinez, V. (2020). Survey of Mission Planning and Management Architectures for Underwater Cooperative Robotics Operations. Applied Sciences, 10(3), 1086. https://doi.org/10.3390/app10031086
  10. McGann, C., Py, F., Rajan, K., Thomas, H., Henthorn, R., & McEwen, R. (2007, September). T-rex: A Model-Based Architecture for Auv Control. In 3rd Workshop on Planning and Plan Execution for Real-World Systems.
  11. Miloradovic, B., Curuklu, B., & Ekstrom, M. (2017, July). A Genetic Mission Planner for Solving Temporal Multi-Agent Problems with Concurrent Tasks. Advances in Swarm Intelligence, 481-493. https://doi.org/10.1007/978-3-319-61833-3_51
  12. Moon, J. (2021). Centralized, Distributed, Hybrid Task Planning Framework for Multi-Robot System in Diverse Communication Status. Journal of Positioning, Navigation, and Timing, 10(3), 215-220. https://doi.org/10.11003/JPNT.2021.10.3.215
  13. Munoz, P., R-Moreno, M.D., & Barrero, D.F. (2016). Unified Framework for Path-Planning and Task-Planning for Autonomous Robots. Robotics and Autonomous Systems, 82, 1-14. https://doi.org/10.1016/j.robot.2016.04.010
  14. Palomeras, N., Carrera, A., Hurtos, N., Karras, G.C., Bechlioulis, C.P., Cashmore, M., ... Kormushev, P. (2016). Toward Persistent Autonomous Intervention in a Subsea Panel. Autonomous Robots, 40(7), 1279-1306. https://doi.org/10.1007/s10514-015-9511-7
  15. Py, F., Pinto, J., Silva, M.A., Johansen, T.A., Sousa, J., & Rajan, K. (2016, October). Europtus: A Mixed-Initiative Controller for Multi-Vehicle Oceanographic Field Experiments. In International Symposium on Experimental Robotics, 323-340. https://doi.org/10.1007/978-3-319-50115-4_29
  16. Silver, T., Chitnis, R., Curtis, A., Tenenbaum, J., Lozano-Perez, T., & Kaelbling, L.P. (2020). Planning with Learned Object Importance in Large Problem Instances Using Graph Neural Networks. arXiv preprint arXiv:2009.05613.
  17. Tsiogkas, N., & Lane, D.M. (2018). An Evolutionary Algorithm for Online, Resource-Constrained, Multivehicle Sensing Mission Planning. In IEEE Robotics and Automation Letters, 3(2), 1199-1206. https://doi.org/10.1109/LRA.2018.2794578.