Optimal path planning of underwater glider in 3D dubins motion with minimal energy consumption

Cao, Junliang; Cao, Junjun; Zeng, Zheng; Lian, Lian

doi:10.1109/oceansap.2016.7485374

Cited by 12 publications

(8 citation statements)

References 19 publications

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“…When no obstacles are involved, these curves correspond to the shortest trajectory between

q_{i}

and

q_{j}

for a car‐like vehicle that moves under certain motion constraints. They have previously been shown useful of trajectory generation (Cao, Cao, Zeng, & Lian, ; Wehbe, Shammas, Zeaiter, & Asmar, ) and will be used in this study as well.…”

Section: Related Workmentioning

confidence: 99%

Online motion planning for unexplored underwater environments using autonomous underwater vehicles

Hernández

Vidal

Moll

et al. 2018

Journal of Field Robotics

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We present an approach to endow an autonomous underwater vehicle with the capabilities to move through unexplored environments. To do so, we propose a computational framework for planning feasible and safe paths. The framework allows the vehicle to incrementally build a map of the surroundings, while simultaneously (re)planning a feasible path to a specified goal. To accomplish this, the framework considers motion constraints to plan feasible 3D paths, that is, those that meet the vehicle's motion capabilities. It also incorporates a risk function to avoid navigating close to nearby obstacles. Furthermore, the framework makes use of two strategies to ensure meeting online computation limitations. The first one is to reuse the last best known solution to eliminate time-consuming pruning routines. The second one is to opportunistically check the states' risk of collision. To evaluate the proposed approach, we use the Sparus II performing autonomous missions in different realworld scenarios. These experiments consist of simulated and in-water trials for different tasks. The conducted tasks include the exploration of challenging scenarios such as artificial marine structures, natural marine structures, and confined natural environments. All these applications allow us to extensively prove the efficacy of the presented approach, not only for constant-depth missions (2D), but, more important, for situations in which the vehicle must vary its depth (3D).Although these AUV applications share some common requirements with others in the domain of aerial and terrestrial robots (e.g., localization, mapping, vision, etc.), navigating autonomously while J Field Robotics. 2019;36:370-396. wileyonlinelibrary.com/journal/rob 370 |

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“…When no obstacles are involved, these curves correspond to the shortest trajectory between

q_{i}

and

q_{j}

Section: Related Workmentioning

confidence: 99%

Online motion planning for unexplored underwater environments using autonomous underwater vehicles

Hernández

Vidal

Moll

et al. 2018

Journal of Field Robotics

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show abstract

“…It was proven to be the shortest using Maximum Pontryagin's principle [2] and additional information for the synthesis of the path was given later [3]. The Dubins path is used in multiple variations for different platforms such as Aerial Unmanned Vehicles (AUVs) [4], wheeled vehicles [5] or general 2D and 3D path-planning methods [6], [7]. These studies sometimes differ from the classic Dubins vehicle model, by varying the rate of turn or the velocity of the vehicle.…”

Section: Introductionmentioning

confidence: 99%

Restricted Orientation Dubins Path With Application to Sailboats

Vautier

Viel

Wan

et al. 2019

IEEE Robot. Autom. Lett.

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This paper develops a geometrical construction of the shortest Dubins path in a discontinuous orientation-restricted environment. The method proposed here builds the shortest path from one pose to the other while avoiding a no-go zone in terms of orientation, and being constrained to move forward. Finally, an application to autonomous sailboats is then provided to validate the feasibility of the planned shortest path in a position keeping scenario.

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“…For point-to-point path planning, Dubins curves have been widely utilized to achieve

continuity and shortest path length [ 6 , 7 ]. Recent literatures on Dubins vehicles also consider environmental conditions such as ocean currents [ 8 , 9 ], obstacle avoidance [ 10 , 11 ], and vehicle/glider characteristics [ 12 , 13 ]. However, most of the works only consider 2-dimensional (2D) Dubins curve, and the extension to 3D Dubins curve is recently proposed in [ 14 ] for unmanned aerial vehicles by using linear interpolation.…”

Section: Introductionmentioning

confidence: 99%

“…However, most of the works only consider 2-dimensional (2D) Dubins curve, and the extension to 3D Dubins curve is recently proposed in [ 14 ] for unmanned aerial vehicles by using linear interpolation. This method is also adopted in [ 12 , 13 ] for path planning of gliders and AUVs.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the Dubins TSP solutions also have the limitation of using the 2D Dubins curves without considering the 3D underwater space. A few recent works extend the 2D Dubins curves to 3D [ 12 , 13 , 14 ] without considering multiple targets. Some works in 3D multi-target task assignment [ 11 ] consider targets in the 3D space without path smoothing.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Task Assignment and Path Planning for Multiple Autonomous Underwater Vehicles Using 3D Dubins Curves †

Cai

Zhang

Zheng

2017

Sensors

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This paper investigates the task assignment and path planning problem for multiple AUVs in three dimensional (3D) underwater wireless sensor networks where nonholonomic motion constraints of underwater AUVs in 3D space are considered. The multi-target task assignment and path planning problem is modeled by the Multiple Traveling Sales Person (MTSP) problem and the Genetic Algorithm (GA) is used to solve the MTSP problem with Euclidean distance as the cost function and the Tour Hop Balance (THB) or Tour Length Balance (TLB) constraints as the stop criterion. The resulting tour sequences are mapped to 2D Dubins curves in the X−Y plane, and then interpolated linearly to obtain the Z coordinates. We demonstrate that the linear interpolation fails to achieve G1 continuity in the 3D Dubins path for multiple targets. Therefore, the interpolated 3D Dubins curves are checked against the AUV dynamics constraint and the ones satisfying the constraint are accepted to finalize the 3D Dubins curve selection. Simulation results demonstrate that the integration of the 3D Dubins curve with the MTSP model is successful and effective for solving the 3D target assignment and path planning problem.

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Optimal path planning of underwater glider in 3D dubins motion with minimal energy consumption

Cited by 12 publications

References 19 publications

Online motion planning for unexplored underwater environments using autonomous underwater vehicles

Online motion planning for unexplored underwater environments using autonomous underwater vehicles

Restricted Orientation Dubins Path With Application to Sailboats

Task Assignment and Path Planning for Multiple Autonomous Underwater Vehicles Using 3D Dubins Curves †

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