论文标题
CT-CPP：使用动态覆盖树的3D地形重建的覆盖范围路径计划
CT-CPP: Coverage Path Planning for 3D Terrain Reconstruction Using Dynamic Coverage Trees
论文作者
论文摘要
这封信解决了未知障碍环境的地形重建的3D覆盖路径计划（CPP）问题。由于传感局限性，所提出的称为CT-CPP的方法对3D区域进行了分层扫描，以收集地形数据，其中使用覆盖树（CT）的概念通过TSP启发的树遍布策略优化了行进序列。在高保真水下模拟器上验证了CT-CPP方法，并将结果与CPP方法后的现有地形进行了比较。结果表明，CT-CPP的轨迹长度，能耗和重建误差显着降低。
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.
