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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.2
- 수록면
- 41 - 49 (9page)
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초록· 키워드
오류제보하기
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.
목차
ABSTRACT
1. Introduction
2. Developed Mission Planner for AMVs
3. Mission Planning Using PDDL
4. Methodology
5. Experiments and Results
6. Conclusions
References
참고문헌 (17)
참고문헌 신청
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.
Carreno, Y., Pairet, È., 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
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
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
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
Carreno, Y., Pairet, È., 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
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
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
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
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