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Computer Science > Robotics

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

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.
Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
Journal reference: in IEEE Robotics and Automation Letters, vol. 7, no. 1, pp. 135-142, Jan. 2022
DOI: 10.1109/LRA.2021.3119870
Cite as: arXiv:2010.09231 [cs.RO]
  (or arXiv:2010.09231v3 [cs.RO] for this version)

Submission history

From: Shalabh Gupta [view email]
[v1] Mon, 19 Oct 2020 05:34:13 GMT (9366kb,D)
[v2] Sat, 8 May 2021 16:54:12 GMT (8921kb,D)
[v3] Thu, 2 Dec 2021 05:45:39 GMT (8905kb,D)

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