Cooperative and Geometric Learning for Path P{lanning of UAVs
We propose a new learning algorithm, named Cooperative and Geometric Learning (CGL), to solve maneuverability, collision avoidance and information sharing problems in path planning for Unmanned Aerial Vehicles (UAVs). The contributions of CGL are threefold: 1) CGL exploits a specific reward matrix G...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2013
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/30735 |
| _version_ | 1848753174057320448 |
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| author | Zhang, B. Mao, Z. Liu, Wan-Quan Liu, J. Zheng, Z. |
| author2 | Not known |
| author_facet | Not known Zhang, B. Mao, Z. Liu, Wan-Quan Liu, J. Zheng, Z. |
| author_sort | Zhang, B. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | We propose a new learning algorithm, named Cooperative and Geometric Learning (CGL), to solve maneuverability, collision avoidance and information sharing problems in path planning for Unmanned Aerial Vehicles (UAVs). The contributions of CGL are threefold: 1) CGL exploits a specific reward matrix G, which leads to a simple and efficient algorithm for the path planning of multiple UAVs. 2) The optimal path in terms of path length and risk measure from a given point to the target point can be calculated. 3) In CGL, the reward matrix G is calculated in real-time and adaptively updated based on the geometric distance and risk information shared by other UAVs. Extensive experimental results validate the effectiveness and feasibility of CGL on the navigation of UAVs. |
| first_indexed | 2025-11-14T08:20:19Z |
| format | Conference Paper |
| id | curtin-20.500.11937-30735 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:20:19Z |
| publishDate | 2013 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-307352017-09-13T15:09:36Z Cooperative and Geometric Learning for Path P{lanning of UAVs Zhang, B. Mao, Z. Liu, Wan-Quan Liu, J. Zheng, Z. Not known UAV path planning learning We propose a new learning algorithm, named Cooperative and Geometric Learning (CGL), to solve maneuverability, collision avoidance and information sharing problems in path planning for Unmanned Aerial Vehicles (UAVs). The contributions of CGL are threefold: 1) CGL exploits a specific reward matrix G, which leads to a simple and efficient algorithm for the path planning of multiple UAVs. 2) The optimal path in terms of path length and risk measure from a given point to the target point can be calculated. 3) In CGL, the reward matrix G is calculated in real-time and adaptively updated based on the geometric distance and risk information shared by other UAVs. Extensive experimental results validate the effectiveness and feasibility of CGL on the navigation of UAVs. 2013 Conference Paper http://hdl.handle.net/20.500.11937/30735 10.1109/ICUAS.2013.6564675 IEEE restricted |
| spellingShingle | UAV path planning learning Zhang, B. Mao, Z. Liu, Wan-Quan Liu, J. Zheng, Z. Cooperative and Geometric Learning for Path P{lanning of UAVs |
| title | Cooperative and Geometric Learning for Path P{lanning of UAVs |
| title_full | Cooperative and Geometric Learning for Path P{lanning of UAVs |
| title_fullStr | Cooperative and Geometric Learning for Path P{lanning of UAVs |
| title_full_unstemmed | Cooperative and Geometric Learning for Path P{lanning of UAVs |
| title_short | Cooperative and Geometric Learning for Path P{lanning of UAVs |
| title_sort | cooperative and geometric learning for path p{lanning of uavs |
| topic | UAV path planning learning |
| url | http://hdl.handle.net/20.500.11937/30735 |