Time-minimal path planning in dynamic current fields

Soulignac, Michaël; Taillibert, Patrick; Rueher, Michel

doi:10.1109/robot.2009.5152426

Cited by 39 publications

(23 citation statements)

References 8 publications

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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%

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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Section: B Related Workmentioning

confidence: 99%

Obstacle avoidance in underwater glider path planning

González¹,

Sosa²,

Perdomo³

et al. 2012

jopha

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show abstract

“…solved this problem by developing a sliding wavefront expansion system. This method was based on the original wavefront expansion but corrected for the influence of strong air or water currents, and was able to guarantee a feasible path to some specified precision [11].…”

Section: Path Planning Algorithmsmentioning

confidence: 99%

Current-sensitive path planning for an underactuated free-floating ocean Sensorweb

Dahl

Thompson

McLaren

et al. 2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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This work investigates multiagent path planning in strong, dynamic currents using thousands of highly underactuated vehicles. We address the specific task of path planning for a global network of ocean-observing floats. These submersibles are typified by the Argo global network consisting of over 3000 sensor platforms. They can control their buoyancy to float at depth for data collection or rise to the surface for satellite communications. Currently, floats drift at a constant depth regardless of the local currents. However, accurate current forecasts have become available which present the possibility of intentionally controlling floats' motion by dynamically commanding them to linger at different depths. This project explores the use of these current predictions to direct float networks to some desired final formation or position. It presents multiple algorithms for such path optimization and demonstrates their advantage over the standard approach of constant-depth drifting. Fig 1. shows the configuration of Argo floats as of January, 2011. Some areas, such as the Sea of Japan, are congested by floats, and others, particularly near the poles, are unmonitored [2].

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“…More recently, biologically-inspired tau-based path planning strategies such as the decentralized receding horizon optimization in [20] were developed to include velocities and time dependencies in the trajectory generation. Many path planners including evolutionary algorithms are based on uniform WVFs [21][22][23][24][25][26] or constant vehicle velocities [6,8,15,[27][28][29][30]. The few planners that account for variable wind conditions simplify the environment to two dimensions by assuming a fixed cruise altitude [6,27,28] or assume that the WVF is horizontally planar [5,31,32] with coarse resolution discretization of the flight space (≥1 km).…”

Section: Introductionmentioning

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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Time-minimal path planning in dynamic current fields

Cited by 39 publications

References 8 publications

Obstacle avoidance in underwater glider path planning

Obstacle avoidance in underwater glider path planning

Current-sensitive path planning for an underactuated free-floating ocean Sensorweb

A High-Fidelity Energy Efficient Path Planner for Unmanned Airships

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