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Abstract
3D map exploration is one of key technologies in robotics. However, finding an optimal exploration path is a challenge since the environment is unknown. This research proposed the submodular exploration (SE) algorithm to enable an unmanned aerial vehicle (UAV) to explore 3D environments. The algorithm learns the submodular function in the Fourier domain and reconstructs the submodular function in the spatial domain via the compressed sensing techniques. Since the objective function of spatial exploration is reformulated as a maximizing submodular function with path constraints, greedy algorithms can achieve \frac{1}{2} (1-e-1) of the optimum. Experiments conducted with this algorithm demonstrate that the UAV can explore more voxels in the environments than the benchmark approach.
Original language | English |
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Title of host publication | 2020 International Conference on Unmanned Aircraft Systems, ICUAS 2020 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1199-1205 |
Number of pages | 7 |
ISBN (Electronic) | 9781728142777 |
DOIs | |
State | Published - Sep 2020 |
Event | 2020 International Conference on Unmanned Aircraft Systems, ICUAS 2020 - Athens, Greece Duration: 1 Sep 2020 → 4 Sep 2020 |
Publication series
Name | 2020 International Conference on Unmanned Aircraft Systems, ICUAS 2020 |
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Conference
Conference | 2020 International Conference on Unmanned Aircraft Systems, ICUAS 2020 |
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Country/Territory | Greece |
City | Athens |
Period | 1/09/20 → 4/09/20 |
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Dive into the research topics of '3D Map Exploration via Learning Submodular Functions in the Fourier Domain'. Together they form a unique fingerprint.Projects
- 1 Finished
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Deep Inverse Reinforcement Learning for Informative Path Planning(2/3)
Tseng, K.-S. (PI)
1/08/20 → 31/07/21
Project: Research