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Efficient Lazy Theta* Path Planning over a Sparse Grid to Explore Large 3D Volumes with a Multirotor UAV

1
Center for Advanced Aerospace Technologies, Calle Wilbur y Orville Wright, 19, 41300 La Rinconada, Sevilla, Spain
2
Autonomous Systems Lab., ETH Zurich, 8092 Zürich, Switzerland
3
Robotics, Vision and Control Group, University of Seville, Avda. de los Descubrimientos s/n, 41092 Sevilla, Spain
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(1), 174; https://doi.org/10.3390/s19010174
Received: 7 December 2018 / Revised: 31 December 2018 / Accepted: 31 December 2018 / Published: 5 January 2019
(This article belongs to the Special Issue Mobile Robot Navigation)
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Abstract

Exploring large, unknown, and unstructured environments is challenging for Unmanned Aerial Vehicles (UAVs), but they are valuable tools to inspect large structures safely and efficiently. The Lazy Theta* path-planning algorithm is revisited and adapted to generate paths fast enough to be used in real time and outdoors in large 3D scenarios. In real unknown scenarios, a given minimum safety distance to the nearest obstacle or unknown space should be observed, increasing the associated obstacle detection queries, and creating a bottleneck in the path-planning algorithm. We have reduced the dimension of the problem by considering geometrical properties to speed up these computations. On the other hand, we have also applied a non-regular grid representation of the world to increase the performance of the path-planning algorithm. In particular, a sparse resolution grid in the form of an octree is used, organizing the measurements spatially, merging voxels when they are of the same state. Additionally, the number of neighbors is trimmed to match the sparse tree to reduce the number of obstacle detection queries. The development methodology adopted was Test-Driven Development (TDD) and the outcome was evaluated in real outdoors flights with a multirotor UAV. In the results, the performance shows over 90 percent decrease in overall path generation computation time. Furthermore, our approach scales well with the safety distance increases. View Full-Text
Keywords: path planning; UAV; autonomous exploration; sparse grids; Lazy Theta* path planning; UAV; autonomous exploration; sparse grids; Lazy Theta*
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Faria, M.; Marín, R.; Popović, M.; Maza, I.; Viguria, A. Efficient Lazy Theta* Path Planning over a Sparse Grid to Explore Large 3D Volumes with a Multirotor UAV. Sensors 2019, 19, 174.

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