Station-viewpoint joint coverage path planning towards mobile visual inspection

Abstract

Coverage path planning (CPP) has been widely studied due to its significant impact on the efficiency of automated surface quality inspection. However, these researches mostly concentrate on fixed-base visual robotic schemes, with limited focus on the widely utilized mobile-base schemes which require considerations of inherent constraints between stations (base positions) and viewpoints. Therefore, this article models a station-viewpoint joint coverage path planning problem and proposes a workflow to solve it. Within this workflow, firstly, a viewpoint selection genetic algorithm based on alternating evolution strategy is presented to optimize both the viewpoint quantity and view quality; secondly, a novel genetic algorithm is devised to accomplish joint assignment and sequence planning for stations and viewpoints. Several experimental studies are conducted to validate the effectiveness and efficiency of the proposed methods, and the proposed genetic algorithms exhibit notable superiorities compared to the benchmark methods in terms of viewpoint quantity, mean view quality, motion cost, and computational efficiency.