SMART-OC: A Real-time Time-risk Optimal Replanning Algorithm for Dynamic Obstacles and Spatio-temporally Varying Currents
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Abstract (arXiv)
Typical marine environments are highly complex with spatio-temporally varying currents and dynamic obstacles, presenting significant challenges to Unmanned Surface Vehicles (USVs) for safe and efficient navigation. Thus, the USVs need to continuously adapt their paths with real-time information to avoid collisions and follow the path of least resistance to the goal via exploiting ocean currents. In this regard, we introduce a novel algorithm, called Self-Morphing Adaptive Replanning Tree for dynamic Obstacles and Currents (SMART-OC), that facilitates real-time time-risk optimal replanning in dynamic environments. SMART-OC integrates the obstacle risks along a path with the time cost to reach the goal to find the time-risk optimal path. The effectiveness of SMART-OC is validated by simulation experiments, which demonstrate that the USV performs fast replannings to avoid dynamic obstacles and exploit ocean currents to successfully reach the goal.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 35 references that are in the map. First posted on arXiv 13 Aug 2025.
Builds on
7 of 35 references.
References · 7
- 2019CARE: Cooperative Autonomy for Resilience and Efficiency of Robot Teams for Complete Coverage of Unknown Environments under Robot Failurescited by 4
- 2020ε^*+: An Online Coverage Path Planning Algorithm for Energy-constrained Autonomous Vehiclescited by 4
- 2020CT-CPP: Coverage Path Planning for 3D Terrain Reconstruction Using Dynamic Coverage Treescited by 4
- 2023SMART: Self-Morphing Adaptive Replanning Treecited by 4
- 2019Rapid Path Planning for Dubins Vehicles under Environmental Currentscited by 3
- 2024Generalized Multi-Speed Dubins Motion Modelcited by 3
- 2020Distance and Steering Heuristics for Streamline-Based Flow Field Planningcited by 2
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Citing papers per year: 2025: 1.
1 paper.