Applying Frontier Cells Based Exploration and Lazy Theta* Path Planning over Single Grid-Based World Representation for Autonomous Inspection of Large 3D Structures with an UAS

Description

Aerial robots are a promising platform to perform autonomous inspection of infrastructures. For this application, the world is a large and unknown space, requiring light data structures to store its representation while performing autonomous exploration and path planning for obstacle avoidance. In this paper, we combine frontier cells based exploration with the Lazy Theta* path planning algorithm over the same light sparse grid—the octree implementation of octomap. Test-driven development has been adopted for the software implementation and the subsequent automated testing process. These tests provided insight into the amount of iterations needed to generate a path with different voxel configurations. The results for synthetic and real datasets are analyzed having as baseline a regular grid with the same resolution as the maximum resolution of the octree. The number of iterations needed to find frontier cells for exploration was smaller in all cases by, at least, one order of magnitude. For the Lazy Theta* algorithm there was a reduction in the number of iterations needed to find the solution in 75% of the cases. These reductions can be explained both by the existent grouping of regions with the same status and by the ability to confine inspection to the known voxels of the octree.

Abstract

Unión Europea, Horizonte 2020, Marie Sklodowska-Curie 64215

Abstract

Unión Europea, MULTIDRONE H2020-ICT-731667

Abstract

Unión Europea, AEROARMS H2020-ICT-644271

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