Complete Coverage Path Planning Based on Improved Genetic Algorithm for Unmanned Surface Vehicle

Abstract

Complete Coverage Path Planning (CCPP) is a key technology for Unmanned Surface Vehicles (USVs) that require complete coverage on the water surface, such as water sample collection, garbage collection, water field patrol, etc. When facing complex and irregular boundaries, the traditional CCPP-based boustrophedon method may encounter many problems and challenges, such as multiple repeated regions, multiple turns, and the easy occurrence of local optima. The traditional genetic algorithm also has some shortcomings. The fixed fitness function, mutation operator and crossover operator are not conducive to the evolution of the population and the production of better offspring. In order to solve the above problems, this paper proposes a CCPP method based on an improved genetic algorithm, including a stretched fitness function, an adaptive mutation operator, and a crossover operator. The algorithm combines the key operators in the fireworks algorithm. Then, the turning and obstacle avoidance during the operation of the Unmanned Surface Vehicle are optimized. Simulation and experiments show that the improved genetic algorithm has higher performance than the exact unit decomposition method and the traditional genetic algorithm, and has more advantages in reducing the coverage path length and repeating the coverage area. This proves that the proposed CCPP method has strong adaptability to the environment and has practical application value in improving the efficiency and quality of USV related operations.