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Journal of Intelligent & Robotic Systems

, Volume 94, Issue 1, pp 265–282 | Cite as

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

  • Somaiyeh MahmoudZadehEmail author
  • David M. W. Powers
  • Karl Sammut
  • Amir Mehdi Yazdani
  • Adham Atyabi
Article

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.

Keywords

Autonomous underwater vehicle Path planning Autonomous mission Task allocation Mission timing Mission management 

Abbreviations

i

Task index

ρi

Priority of task i

ξi

Risk percentage associated with task i

δi

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

eij

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

wij

The weight assigned to eij

dij

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

tij

Time required for traversing edge eij

Θ

Obstacle

Θp

Obstacle’s position

Θr

Obstacle’s radius

ΘUr

Obstacle’s uncertainty rate

VC

The current velocity vector

uc

X component of the current vector

vc

Y component of the current vector

S

Two dimensional x-y space

So

The center of the vortex in the current map

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

Texp

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

TR

The route traveled time

Tτ

The total available time for the mission

Tcompute

Computation time for checking re-routing criterion and its process

C

The cost of local path generated by path planner

C

The cost of tasks completion

CR

The total cost of route including C and C

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Science and EngineeringFlinders UniversityAdelaideAustralia
  2. 2.Center for Maritime Engineering, Control and ImagingFlinders UniversityAdelaideAustralia
  3. 3.Seattle Children’s Research InstituteUniversity of WashingtonWashingtonUSA

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