CT-CPP: Coverage Path Planning for 3D Terrain Reconstruction Using Dynamic Coverage Trees

Zongyuan Shen; Junnan Song; Khushboo Mittal; Shalabh Gupta

RA-L 202226 citations

CT-CPP: Coverage Path Planning for 3D Terrain Reconstruction Using Dynamic Coverage Trees

Zongyuan Shen, Junnan Song, Khushboo Mittal, Shalabh Gupta

Abstract

This letter addresses the 3D coverage path planning (CPP) problem for terrain reconstruction of unknown obstacle-rich environments. Due to sensing limitations, the proposed method, called CT-CPP, performs layered scanning of the 3D region to collect terrain data, where the traveling sequence is optimized using the concept of a coverage tree (CT) with a TSP-inspired tree traversal strategy. The CT-CPP method is validated on a high-fidelity underwater simulator and the results are compared to an existing terrain following CPP method. The results show that CT-CPP yields significant reduction in trajectory length, energy consumption, and reconstruction error.

BibTeX

@inproceedings{ral2022_ctcppcoveragepat,
  title = {CT-CPP: Coverage Path Planning for 3D Terrain Reconstruction Using Dynamic Coverage Trees},
  author = {Zongyuan Shen and Junnan Song and Khushboo Mittal and Shalabh Gupta},
  booktitle = {RA-L 2022},
  year = {2022}
}