A Novel Efficient Task-Assign Route Planning Method for AUV Guidance in a Dynamic Cluttered Environment
📝 Abstract
Promoting the levels of autonomy facilitates the vehicle in performing long-range operations with minimum supervision. The capability of Autonomous Underwater Vehicles (AUVs) to fulfill the mission objectives is directly influenced by route planning and task assignment system performance. The system fives the error of “Bad character(s) in field Abstract” for no reason. Please refer to manuscript for the full abstract
💡 Analysis
Promoting the levels of autonomy facilitates the vehicle in performing long-range operations with minimum supervision. The capability of Autonomous Underwater Vehicles (AUVs) to fulfill the mission objectives is directly influenced by route planning and task assignment system performance. The system fives the error of “Bad character(s) in field Abstract” for no reason. Please refer to manuscript for the full abstract
📄 Content
A Novel Efficient Task-Assign Route Planning Method for AUV Guidance in a Dynamic Cluttered Environment S. MahmoudZadeh, D. M.W. Powers, A.M. Yazdani School of Computer Science, Engineering and Mathematics Flinders University, Adelaide, SA 5042, Australia Abstract— Promoting the levels of autonomy facilitates the vehicle in performing long-range operations with minimum supervision. The capability of Autonomous Underwater Vehicles (AUVs) to fulfill the mission objectives is directly influenced by route planning and task assignment system performance. This paper proposes an efficient task-assign route planning model in a semi-dynamic operation network, where the location of some waypoints are changed by time in a bounded area. Two popular meta-heuristic algorithms named biogeography-based optimization (BBO) and particle swarm optimization (PSO) are adopted to provide real-time optimal solutions for task sequence selection and mission time management. To examine the performance of the method in a context of mission productivity, mission time management and vehicle safety, a series of Monte Carlo simulation trials are undertaken. The results of simulations declare that the proposed method is reliable and robust particularly in dealing with uncertainties and changes of the operation network topology; as a result, it can significantly enhance the level of vehicle’s autonomy by relying on its reactive nature and capability of providing fast feasible solutions. Keywords— autonomous underwater vehicle; dynamic network routing; task assignment; evolutionary-based route planning; mission time management I. INTRODUCTION Autonomous Underwater Vehicles (AUVs) are advantageous tools for undersea exploration, interrogation, detection and surveillance and particularly are employed to accomplish tasks that are impossible for human operator to complete. Most of the current AUV applications are supervised from the support vessel which provides higher- level decisions in critical situation and generally takes enormous cost during the mission [1]. Growing attention has been devoted in recent years on increasing the ranges of missions, vehicles endurance, extending vehicles applicability, promoting vehicles autonomy to handle longer missions without supervision, and reducing operation costs [2]. The primary step toward increasing endurance and range of vehicle operation is promoting vehicles autonomy in terms of time management and task allocation while moving toward the destination. More advanced approaches thus aim to increase the efficiency of the vehicle in both robust decision-making and situation awareness. Efficient motion planning and mission scheduling are also key requirements towards advanced autonomy, and facilitate the vehicle’s handling of long-range operations.
Route planning problem usually refers to finding shortest paths in a graph-like network such as modelling the transportation network [3, 4]. The main issue addressed by previous research on route planning system is how to direct vehicle(s) to destination(s) in a network while providing efficient maneuvers and reducing travel time. Some instances of route planning systems applications are in the areas of traffic control [5], real time routing and trip planning [6], and so on. Briefly reviewing the most highlights in route planning works in the state of the art, in [7] a route planning strategy is employed for transportation purpose in a form of multi-agent decisions in which the agent is in charge of order distribution to customers, traversing edges, competing vendors, increasing production and etc.; a three-layer structure to facilitate multiple unmanned surface vehicles to accomplish task management and formation path planning in a maritime environment is proposed in [8]; in [9], graph-based methods using modified Dijkstra Algorithm for the AUV ‘‘SLOCUM Glider’’ motion planning in a dynamic environment is offered; for AUV guidance in large scale underwater environment, a behavior based controller coupled with waypoint tracking scheme is employed [10]; and finally, a special model of multi-agent reinforcement learning (MARL) algorithms is proposed in [11] for a road network route planning system taking advantages of Q-value based dynamic programming (QVDP) to solve vehicle delay’s problems .
With respect to the combinatorial nature of AUV’s route-task allocation problem, which generalizes both TSP and Knapsack problems, there should be a compromise among the mission available time, maximizing number of highest priority tasks with minimum risk percentage, and guaranteeing reaching to the predefined destination, which is combination of a discrete and a continuous optimization problem at the same time and categorized as an Non- deterministic Polynomial-time (NP) hard problem. Obtaining the optimal solutions for NP-hard problems is a computationally challenging
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