CT-CPP: Coverage Path Planning for 3D Terrain Reconstruction Using Dynamic Coverage Trees
This work addresses path planning for terrain reconstruction in challenging environments like underwater settings, offering improvements in efficiency and accuracy, though it appears incremental as it builds on existing CPP methods.
The paper tackles the 3D coverage path planning problem for terrain reconstruction in unknown, obstacle-rich environments by proposing CT-CPP, which uses dynamic coverage trees and a TSP-inspired strategy to optimize scanning sequences; results show significant reductions in trajectory length, energy consumption, and reconstruction error compared to an existing method.
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.