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Hybrid Motion Planning Task Allocation Model for AUV’s Safe Maneuvering in a Realistic Ocean Environment

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Abstract

This paper presents a hybrid route-path planning model for an Autonomous Underwater Vehicle’s task assignment and management while the AUV is operating through the variable littoral waters. Several prioritized tasks distributed in a large scale terrain is defined first; then, considering the limitations over the mission time, vehicle’s battery, uncertainty and variability of the underlying operating field, appropriate mission timing and energy management is undertaken. The proposed objective is fulfilled by incorporating a route-planner that is in charge of prioritizing the list of available tasks according to available battery and a path-planer that acts in a smaller scale to provide vehicle’s safe deployment against environmental sudden changes. The synchronous process of the task assign-route and path planning is simulated using a specific composition of Differential Evolution and Firefly Optimization (DEFO) Algorithms. The simulation results indicate that the proposed hybrid model offers efficient performance in terms of completion of maximum number of assigned tasks while perfectly expending the minimum energy, provided by using the favorable current flow, and controlling the associated mission time. The Monte-Carlo test is also performed for further analysis. The corresponding results show the significant robustness of the model against uncertainties of the operating field and variations of mission conditions.

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Abbreviations

ℵ i :

Task index

ρ i :

Priority of task i

ξ i :

Risk percentage associated with task i

δ i :

Absolute time required for completion of task i

P :

Vertices of the network that corresponds to waypoints

E :

Edges of the network

m :

Number of waypoints in the network

k :

Number of edges in the network

\(p^{i}_{x,y,z}\) :

Position of arbitrary waypoint i in 3-D space

e i j :

An arbitrary edge that connects \(p^{i}_{x,y,z}\) to \(p^{j}_{x,y,z}\)

w i j :

The weight assigned to eij

d i j :

Distance between position of \(p^{i}_{x,y,z}\) and \(p^{j}_{x,y,z}\)

t i j :

Time required for traversing edge eij

Θ:

Obstacle

Θp :

Obstacle’s position

Θr :

Obstacle’s radius

ΘUr :

Obstacle’s uncertainty rate

V C :

The current velocity vector

u c :

X component of the current vector

v c :

Y component of the current vector

S :

Two dimensional x-y space

S o :

The center of the vortex in the current map

ℓ :

The radius of the vortex in the current map

I :

The strength of the vortex in the current map

Γ3−D :

Symbol of the three dimensional terrain

η :

The AUV state on NED frame {n}

[X, Y, Z]:

Vehicles North, x, East, y, Depth, z, position along the path ℘

ϕ :

The Euler angle of roll

θ :

The Euler angle of pitch

ψ :

The Euler angle of yaw

υ :

Vehicle’s water referenced velocity in the body frame {b}

u :

The surge component of the velocity υ

v :

The sway component of the velocity υ

w :

The heave component of the velocity υ

℘:

The potential trajectory generated by the local path planner

𝜗 :

Control point along the path ℘

n :

Number of control points along an arbitrary path ℘

L ℘ :

Length of the candidate path ℘

T ℘ :

The local path flight time

T e x p :

The expected time for passing an edge

℘CPU :

computational time for generating a local path

R :

An arbitrary route including sequences of tasks and waypoints

T R :

The route traveled time

T τ :

The total available time for the mission

T c o m p u t e :

Computation time for checking re-routing criterion and its process

C ℘ :

The cost of local path generated by path planner

C ℵ :

The cost of tasks completion

C R :

The total cost of route including C℘ and Cℵ

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Authors and Affiliations

  1. College of Science and Engineering, Flinders University, Adelaide, SA, Australia

    Somaiyeh MahmoudZadeh & David M. W. Powers

  2. Center for Maritime Engineering, Control and Imaging, Flinders University, Adelaide, SA, Australia

    Karl Sammut & Amir Mehdi Yazdani

  3. Seattle Children’s Research Institute, University of Washington, Washington, USA

    Adham Atyabi

Authors
  1. Somaiyeh MahmoudZadeh
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  2. David M. W. Powers
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  3. Karl Sammut
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  4. Amir Mehdi Yazdani
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  5. Adham Atyabi
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Correspondence to Somaiyeh MahmoudZadeh.

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MahmoudZadeh, S., Powers, D.M.W., Sammut, K. et al. Hybrid Motion Planning Task Allocation Model for AUV’s Safe Maneuvering in a Realistic Ocean Environment. J Intell Robot Syst 94, 265–282 (2019). https://doi.org/10.1007/s10846-018-0793-9

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