Toward efficient task assignment and motion planning for large-scale underwater missions

Abstract

An autonomous underwater vehicle needs to possess a certain degree of autonomy for any particular underwater mission to fulfil the mission objectives successfully and ensure its safety in all stages of the mission in a large-scale operating field. In this article, a novel combinatorial conflict-free task assignment strategy, consisting of an interactive engagement of a local path planner and an adaptive global route planner, is introduced. The method takes advantage of the heuristic search potency of the particle swarm optimization algorithm to address the discrete nature of routing-task assignment approach and the complexity of nondeterministic polynomial-time-hard path planning problem. The proposed hybrid method is highly efficient as a consequence of its reactive guidance framework that guarantees successful completion of missions particularly in cluttered environments. To examine the performance of the method in a context of mission productivity, mission time management, and vehicle safety, a series of simulation studies are undertaken. The results of simulations declare that the proposed method is reliable and robust, particularly in dealing with uncertainties, and it can significantly enhance the level of a vehicle's autonomy by relying on its reactive nature and capability of providing fast feasible solutions.

A survey of formation control and motion planning of multiple unmanned vehicles

“…A good example of integrating path planning capability with the task-planning requirement can be found at Munoz et al 38 , where a unified framework has been proposed for exploration missions. Also, in Mahmoudzadeh et al 39 , a novel combinatorial conflict-free task assignment and path planning strategy has been proposed for largescale underwater missions and based upon such a strategy, Zhu et al 40 incorporated a biologically inspired neural network (BINN) into the task-allocation algorithm to address the dynamics constraints of the vehicles when generating the path. Generated paths will then be passed down to the Task Execution Layer.…”

Online path planning for AUV rendezvous in dynamic cluttered undersea environment using evolutionary algorithms

“…The AUV is equipped with sonar sensors for measuring the velocity and coordinates of the objects with a level of uncertainty depicted with a normal distribution. Modelling of different obstacles derived from [34][35][36] are formulated as follows: 1) Quasi-static Uncertain Objects: Object's position should be placed between the location of AUV's starting and destination spot in the given map. This group of objects are introduced with a fixed-center generated at commencement and an uncertain radius that varying over time according to Eq.…”

“…The PSO is an optimization method that performs fast computation and efficient performance in solving variety of the complex problems and widely used in past [39,22,34]. The PSO start operating with initial population of particles, where particles involve position and velocity in the search space.…”

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

“…The path planner, in this context, is responsible to provide a safe and energy efficient maneuver for the vehicle. The proceeding research is a completion of previous work [26][27][28] that takes a full consideration of details in task management and generalizes the applicability of the motion planners by realistic modeling of various underwater situations, which have not been fully addressed in previous papers.…”