  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.
