An Efficient Hybrid Route-Path Planning Model For Dynamic Task Allocation and Safe Maneuvering of an Underwater Vehicle in a Realistic Environment

📝 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.

💡 Analysis

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.

📄 Content

Hybrid Motion Planning Task Allocation Model for AUV’s Safe Maneuvering in a Realistic Ocean Environment Somaiyeh MahmoudZadeh1, David M.W Powers1, Karl Sammut2, Amir Mehdi Yazdani2, Adham Atyabi3 1 College of Science and Engineering, Flinders University, Adelaide, SA, Australia 2 Centre for Maritime Engineering, Control and Imaging, Flinders University, Adelaide, SA, Australia 3 Seattle Children’s Research Institute, University of Washington, United States somaiyeh.mahmoudzadeh@flinders.edu.au david.powers@flinders.edu.au karl.sammut@flinders.edu.au amirmehdi.yazdani@flinders.edu.au adham.atyabi@seattlechildrens.org 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
Nomenclature ℵi Task index Γ3-D Symbol of the three dimensional terrain ρi Priority of task i η The AUV state on NED frame{n} ξi Risk percentage associated with task i [X,Y,Z] Vehicles North, x, East, y, Depth, z, position along the path ℘ δi Absolute time required for completion of task i ϕ The Euler angle of roll P Vertices of the network that corresponds to waypoints θ The Euler angle of pitch E Edges of the network ψ The Euler angle of yaw m Number of waypoints in the network υ Vehicle’s water referenced velocity in the body frame {b} k Number of edges in the network u The surge component of the velocity υ pix,y,z Position of arbitrary waypoint i in 3-D space v The sway component of the velocity υ eij An arbitrary edge that connects pix,y,z to pjx,y,z w The heave component of the velocity υ wij The weight assigned to eij ℘ The potential trajectory generated by the local path planner dij Distance between position of pix,y,z and pjx,y,z ϑ Control point along the path ℘ tij Time required for traversing edge eij n Number of control points along an arbitrary path ℘ Θ Obstacle
L℘ Length of the candidate path ℘ Θp Obstacle’s position T℘ The local path flight time Θr Obstacle’s radius Texp The expected time for passing an edge ΘUr Obstacle’s uncertainty rate ℘CPU computational time for generating a local path VC The current velocity vector ℜ An arbitrary route including sequences of tasks and waypoints uc X component of the current vector Tℜ The route travelled time vc Y component of the current vector T𝜏 The total available time for the mission S Two dimensional x-y space Tcompute Computation time for checking re-routing criterion and its process So The center of the vortex in the current map C℘ The cost of local path generated by path planner ℓ The radius of the vortex in the current map Cℵ The cost of tasks completion
ℑ The strength of the vortex in the current map Cℜ The total cost of route including C℘ and Cℵ

1 Introduction

Autonomous Underwater Vehicles (AUVs) have been discovered as the most cost-effective and expedient technology in carrying out the underwater missions over the past and coming years. They are largely employed for various purposes such as scientific underwater explorations [1], inspection and surveys [2], sampling and monitoring coastal areas [3], offshore installations and mining industries [4], etc. However, most of the available AUVs operate with a pre- programmed mission scenario while all parameters for entire mission should be defined in advance and operator’s interaction is necessary issue. For any of scientific, surveillance, mine or military applications of the AUV, a sequence of task

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