Multiple Task Assignment and Path Planning of a Multiple Unmanned Surface Vehicles System Based on Improved Self-Organizing Mapping and Improved Genetic Algorithm

Abstract

This paper addresses multiple task assignment and path-planning problems for a multiple unmanned surface vehicle (USVs) system. Since it is difficult to solve multi-task allocation and path planning together, we divide them into two sub-problems, multiple task allocation and path planning, and study them separately. First, to resolve the multi-task assignment problem, an improved self-organizing mapping (ISOM) is proposed. The method can allocate all tasks in the mission area, and obtain the set of task nodes that each USV needs to access. Second, aiming at the path planning of the USV accessing the task nodes, an improved genetic algorithm (IGA) with the shortest path is proposed. To avoid USV collision during navigation, an artificial potential field function (APFF) is proposed. A multiple USV system with multi-task allocation and path planning is simulated. Simulation results verify the effectiveness of the proposed algorithms.

A Swarm Intelligence Graph-Based Pathfinding Algorithm Based on Fuzzy Logic (SIGPAF): A Case Study on Unmanned Surface Vehicle Multi-Objective Path Planning

“…Specifically, methodologies have been proposed for finding the shortest path based on popular metaheuristics that are commonly used for addressing the Traveling Salesman Problem (TSP). These approaches include improvements to the Ant Colony Optimization (ACO) algorithm [19] by dynamically selecting the pheromone volatility coefficient to enhance the searching ability of the algorithm; Genetic Algorithm (GA) by using the multidomain inversion to increase the offspring and consecutively increase the convergence speed [20], and in cooperation with other heuristic algorithms for task assignment and obstacle avoidance in the case of swarm of USVs [21]; A* algorithm for safer routes by adding a smoothing process [8], or for obstacle avoidance [7,22] combined in a hybrid scheme with Artificial Potential Field (APF) algorithm [23]; rapidly-exploring random tree (RRT) algorithm with adaptive hybrid dynamic step size and target attractive force [24]; and Particle Swarm Optimization (PSO) [25] with orientation-angle-based grouping for higher convergence speed [26]. Other approaches are based on B-splines for collision avoidance where the optimal path is generated in multiple steps so that an initial shortest path will be smoothed and directed away from obstacles [27].…”