Biogeography-based combinatorial strategy for efficient autonomous underwater vehicle motion planning and task-time management

Abstract

Autonomous Underwater Vehicles (AUVs) are capable of spending long periods of time for carrying out various underwater missions and marine tasks. In this paper, a novel conflict-free motion planning framework is introduced to enhance underwater vehicle's mission performance by completing maximum number of highest priority tasks in a limited time through a large scale waypoint cluttered operating field, and ensuring safe deployment during the mission. The proposed combinatorial route-path planner model takes the advantages of the biogeography-based optimization (BBO) algorithm toward satisfying objectives of both higher-lower level motion planners and guarantees maximization of the mission productivity for a single vehicle operation. The performance of the model is investigated under different scenarios including the particular cost constraints in time-varying operating fields. To show the reliability of the proposed model, performance of each motion planner assessed separately and then statistical analysis is undertaken to evaluate the total performance of the entire model. The simulation results indicate the stability of the contributed model and its feasible application for real experiments.

Nonlinear multiple-input-multiple-output adaptive backstepping control of underwater glider systems

“…Actually, the authors have already developed an underwater glider named after Seagull, 33 which will be applied for an ocean test in the near future. Another extension of this work is to develop an on-line planning scheme [34][35][36][37] that can be incorporated into a glider's guidance system to allow it to regenerate the trajectory during the course of the mission. 38,39…”

“…In equation (37), _ m b is defined as u 3 , the expressions of _ V 1 , _ V 3 , and _ have been described in equation ( 8), equation (10) and equation (3), and assume _ z 12 ¼ Àc 12 z 12 (38) Substituting equation (38) into equation (37), the expression of u 3 is obtained (equation ( 20)).…”

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.…”

“…(30), the Pmax is probability of the habitat with Smax and mmax represents the maximum mutation rate. Further details can be found in [35].…”

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.…”