Time-optimal path planning: Real-time sea exercises

Subramani, Deepak N.; Lermusiaux, Pierre F. J.; Haley, Patrick J.; Mirabito, Chris; Jana, Sudip; Kulkarni, Chinmay S.; Girard, Andrew; Wickman, Diana; Edwards, Joseph R.; Smith, Josh

doi:10.1109/oceanse.2017.8084776

Cited by 25 publications

(12 citation statements)

References 21 publications

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“…In 2017, Subramani et al used the level set equation to plan a time-optimal path for the REMUS 600 AUV in a strong and dynamic ocean current environment and considered operational constraints (such as minimum depth). e test results in Buzzards Bay and Vineyard Sound regions showed that AUV reaches the target point along the time-optimal path 6-15% faster than the shortest path, although the local currents and geometric constraints are complex [36]. Since then, Subramani et al have conducted further research on the application of the LSM method to path planning in the ocean current environment.…”

Section: 22mentioning

confidence: 99%

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Research Progress of Path Planning Methods for Autonomous Underwater Vehicle

Guo

Liu

Fan

et al. 2021

Mathematical Problems in Engineering

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Path planning is a key technology for autonomous underwater vehicle (AUV) navigation. With the emphasis and research on AUV, AUV path planning technology is continuously developing. Path planning techniques generally include environment modelling methods and path planning algorithms. Based on a brief description of the environment modelling methods, this paper focuses on the path planning algorithms commonly used by AUV. According to the basic principles of the algorithm, the AUV path planning algorithms are divided into four categories: artificial potential field methods, geometric model search methods, random sampling methods, and intelligent bionic methods. In this review, we summarize in detail the development and application of various path planning algorithms in recent years. Meanwhile, we analyse the advantages and disadvantages of various algorithms and their improvement methods. Obstacles, ocean currents, and undersea terrain have an impact on AUV path planning. Therefore, how to deal with the complex underwater environment adds some limits to AUV path planning algorithms. In addition to the external environment, path planning algorithms also need to consider AUV’s physical constraints, such as energy constraints and motion constraints. Then, we analyse the motion constraints in AUV path planning. Finally, we discuss the development direction of AUV path planning algorithm. Time-varying ocean currents, special obstacles, multiobjective constraints, and practicability will be the problems that AUV path planning algorithms need to solve.

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Section: 22mentioning

confidence: 99%

“…Sometimes, the depth limitation is also considered in AUV path planning [36,107]. It can avoid the AUV getting stranded when sailing in shallow water or along the coastline.…”

Section: Other Constraintsmentioning

confidence: 99%

Research Progress of Path Planning Methods for Autonomous Underwater Vehicle

Guo

Liu

Fan

et al. 2021

Mathematical Problems in Engineering

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“…Numerical schemes have been developed to complete the above steps [25] and generalized for various optimality criteria, including the time-, energy-, coordination-, and interception-optimal planning of swarms of AUVs in realistic data-assimilative dynamic ocean re-analyses [26], [51]- [56]. This was also recently utilized successfully in real-time exercises at sea with real AUVs [28], [57].…”

Section: B Differential Path Planning (Lse)mentioning

confidence: 99%

Graph-Search and Differential Equations for Time-Optimal Vessel Route Planning in Dynamic Ocean Waves

Mannarini

Subramani

Lermusiaux

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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Time-optimal paths are evaluated by VISIR ("dis-coVerIng Safe and effIcient Routes"), a graph-search ship routing model, with respect to the solution of the fundamental differential equations governing optimal paths in a dynamic wind-wave environment. The evaluation exercise makes use of identical setups: topological constraints, dynamic wave environmental conditions, and vessel-ocean parametrizations, while advection by external currents is not considered. The emphasis is on predicting the time-optimal ship headings and Speeds Through Water constrained by dynamic ocean wave fields. VISIR upgrades regarding angular resolution, time-interpolation, and static navigational safety constraints are introduced. The deviations of the graph-search results relative to the solution of the exact differential equations in both the path duration and length are assessed. They are found to be of the order of the discretization errors, with VISIR's solution converging to that of the differential equation for sufficient resolution.

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“…In addition to being a good candidate for developing and testing numerical schemes, this flow scenario is commonly encountered in the ocean. For example, the crossing of a shelfbreak front (Subramani et al, 2017a) or of channels (Subramani et al, 2017b) can be idealized by this canonical flow. Hence, studying the properties of time optimal paths in such flows is a valuable first step for planning in realistic ocean flows later on.…”

Section: Applicationsmentioning

confidence: 99%

“…It also directly avoids physical obstacles (e.g., islands) and forbidden regions due to operational constraints (e.g., minimum water depth) thereby ensuring vehicle safety. Its use with realistic deterministic ocean re-analyses was demonstrated in several ocean regions (Lolla et al, 2014a;Subramani et al, 2017a) and in real-time sea exercises with real AUVs (Subramani et al, 2017b). However, the corresponding PDEs and methodology need to be extended to dynamic stochastic environments.…”

Section: Introductionmentioning

confidence: 99%