GPU accelerated coverage path planning optimized for accuracy in robotic inspection applications

Abstract

In this paper, we introduce a coverage path planning algorithm for inspecting large structures optimized to generate highly accurate 3D models. Robotic inspection of structures such as aircrafts, bridges and buildings, is considered a critical task since missing any detail could affect the performance and integrity of the structures. Additionally, it is a time and resource intensive task that should be performed as efficiently and accurately as possible. The method we propose is a model based coverage path planning approach that generates an optimized path that passes through a set of admissible waypoints to cover a complex structure. The coverage path planning algorithm is developed with a heuristic reward function that exploits our knowledge of the structure mesh model, and the UAV's onboard sensors' models to generate optimal paths that maximizes coverage and accuracy, and minimizes distance travelled. Moreover, we accelerated critical components of the algorithm utilizing the Graphics Processing Unit (GPU) parallel architecture. A set of experiments were conducted in a simulated environment to test the validity of the proposed algorithm.