Adapting the wavefront expansion in presence of strong currents

Soulignac, Michaël; Taillibert, Patrick; Rueher, Michel

doi:10.1109/robot.2008.4543391

Cited by 34 publications

(20 citation statements)

References 7 publications

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“…The cost wavefront propagation is a simple yet efficient method for generating optimal paths in autonomous robot applications in unknown environments [41]. This approach starts from the robots's current position and expands anisotropically outward toward the goal position while minimizing a cost function.…”

Section: Cost Wavefront Expansion Plannermentioning

confidence: 99%

A High-Fidelity Energy Efficient Path Planner for Unmanned Airships

2017

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This paper presents a comparative study of the effects of grid resolution, vehicle velocity and wind vector fields on the trajectory planning of unmanned airships. A wavefront expansion trajectory planner that minimizes a multi-objective cost function consisting of flight time, energy consumption and collision avoidance while respecting the differential constraints of the vehicle is presented. Trajectories are generated using a variety of test environments and flight conditions to demonstrate that the inclusion of a high terrain map resolution, a temporal vehicle velocity and a spatial wind vector field yields significant improvements in trajectory feasibility and energy economy when compared to trajectories generated using only two of these three elements.

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Section: Cost Wavefront Expansion Plannermentioning

confidence: 99%

A High-Fidelity Energy Efficient Path Planner for Unmanned Airships

2017

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“…It also addresses the discretization problem of the search space that A* has. Soulignac extended this line by presenting a series of papers [21], [22], [23] that manages strong and time-dependent ocean currents. In both cases, the approach bases on Wavefront Expansion, which is Dijkstra's method [4] in essence.…”

Section: B Related Workmentioning

confidence: 99%

“…Basically we have used this algorithm as reference to compare the new developments. In the next step, we adapted A* method to manage ocean currents as in Garau's work [6], using the constrained motion model of Soulignac [21] (Fig. 4).…”

Section: A Originsmentioning

confidence: 99%

Obstacle avoidance in underwater glider path planning

González¹,

Sosa²,

Perdomo³

et al. 2012

jopha

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Abstract-Underwater gliders have revealed as a valuable scientific platform, with a growing number of successful environmental sampling applications. They are specially suited for long range missions due to their unmatched autonomy level, although their low surge speed makes them strongly affected by ocean currents. Path planning constitutes a real concern for this type of vehicle, as it may reduce the time taken to reach a given waypoint or save power. In such a dynamic environment it is not easy to find an optimal solution or any such requires large computational resources. In this paper, we present a path planning scheme with low computational cost for this kind of underwater vehicle that allows static or dynamic obstacle avoidance, frequently demanded in coastal environments, with land areas, strong currents, shipping routes, etc. The method combines an initialization phase, inspired by a variant of the A* search process and ND algorithm, with an optimization process that embraces the physical vehicle motion pattern. Consequently, our method simulates a glider affected by the ocean currents, while it looks for the path that optimized a given objective. The method is easy to configure and adapt to various optimization problems, including missions in different operational scenarios. This planner shows promising results in realistic simulations, including ocean currents that vary considerably in time, and provides a superior performance over other approaches that are compared in this paper.

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“…Various algorithms have been proposed for navigation guided by ocean model flow data, including use of the A* algorithm for min-time path planning under spatially varying, time-static flow [28], [29]; fast marching (FM)-based algorithms for efficient path planning in a static flow field [30] (this method is generalized by Soulignac et al, to strong [31] and time-varying flows [32]); genetic algorithms [33]- [35]; and case-based path planning [36].…”

Section: Introductionmentioning

confidence: 99%

Trend and Bounds for Error Growth in Controlled Lagrangian Particle Tracking

Szwaykowska

Zhang

2014

IEEE J. Oceanic Eng.

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This paper establishes the method of controlled Lagrangian particle tracking (CLPT) to analyze the offsets between physical positions of marine robots in the ocean and simulated positions of controlled particles in an ocean model. The offset, which we term the CLPT error, demonstrates distinguished characteristics not previously seen in drifters and floats that cannot be actively controlled. The CLPT error growth over time is exponential until it reaches a turning point that only depends on the resolution of the ocean model. After this turning point, the error growth slows down significantly to polynomial functions of time. In the ideal case, a theoretical upper threshold on exponential growth of CLPT error can be derived. These characteristics are proved theoretically, verified via simulation, and justified with ocean experimental data. The method of CLPT may be applied to improve the accuracy of ocean circulation models and the performance of navigation algorithms for marine robots.

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Adapting the wavefront expansion in presence of strong currents

Cited by 34 publications

References 7 publications

A High-Fidelity Energy Efficient Path Planner for Unmanned Airships

A High-Fidelity Energy Efficient Path Planner for Unmanned Airships

Obstacle avoidance in underwater glider path planning

Trend and Bounds for Error Growth in Controlled Lagrangian Particle Tracking

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