Optimal Route Planning with Prioritized Task Scheduling for AUV Missions
This addresses route planning for AUV missions, but it is incremental as it applies existing heuristic algorithms to a specific domain problem.
This paper tackled the joint problem of route planning and task assignment for Autonomous Underwater Vehicles (AUVs) by using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to optimize routes based on task priorities and risk minimization, with results showing that GA-based planning produced superior outcomes compared to PSO.
This paper presents a solution to Autonomous Underwater Vehicles (AUVs) large scale route planning and task assignment joint problem. Given a set of constraints (e.g., time) and a set of task priority values, the goal is to find the optimal route for underwater mission that maximizes the sum of the priorities and minimizes the total risk percentage while meeting the given constraints. Making use of the heuristic nature of genetic and swarm intelligence algorithms in solving NP-hard graph problems, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are employed to find the optimum solution, where each individual in the population is a candidate solution (route). To evaluate the robustness of the proposed methods, the performance of the all PS and GA algorithms are examined and compared for a number of Monte Carlo runs. Simulation results suggest that the routes generated by both algorithms are feasible and reliable enough, and applicable for underwater motion planning. However, the GA-based route planner produces superior results comparing to the results obtained from the PSO based route planner.