Design of Full Coverage Path Algorithm for exploration truck

Abstract

The survey work for urban underground pipelines is mostly carried out outdoors, and the randomness of its environment brings a lot of difficulties to the full-coverage path planning work of intelligent exploration vehicles. To adapt to the complex outdoor environment and improve the performance and efficiency of the crossing, this article constructs the exploration vehicle model and improves the traditional DFS algorithm. Changing its backtracking strategy based on the original algorithm and introducing a node cost function to linearly constrain the number of turns of the exploration vehicle. Through simulation experiments, the improved DFS algorithm can efficiently complete the full-coverage exploration task for pipeline exploration sites and compared with some classical algorithms, the improved algorithm has a significant reduction in the map traversal repetition rate, and also shows obvious advantages in solving the full-coverage path planning problem in complex environments, which can complete the related work more efficiently.