An Efficient Hybrid Route-Path Planning Model For Dynamic Task Allocation and Safe Maneuvering of an Underwater Vehicle in a Realistic Environment
This addresses task management and safety for underwater vehicles in realistic environments, but it appears incremental as it combines existing optimization algorithms.
The paper tackles the problem of dynamic task allocation and safe maneuvering for an Autonomous Underwater Vehicle in variable littoral waters, achieving efficient performance by completing a maximum number of tasks while minimizing energy usage and controlling mission time through a hybrid route-path planning model.
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.