Advances in Surveying Topographically Complex Ecosystems with UAVs: Manta Ray Foraging Algorithms
This study introduces an innovative UAV cruise data collection path planning approach using the manta ray foraging optimization (MRFO) algorithm to enhance efficiency and energy utilization in forest ecosystem monitoring. Traditionally reliant on costly manual patrols, this method leverages UAVs and...
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| Vydané v: | Drones (Basel) Ročník 8; číslo 11; s. 631 |
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01.11.2024
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| Abstract | This study introduces an innovative UAV cruise data collection path planning approach using the manta ray foraging optimization (MRFO) algorithm to enhance efficiency and energy utilization in forest ecosystem monitoring. Traditionally reliant on costly manual patrols, this method leverages UAVs and ground-based sensors for data collection. The approach begins with a self-organized clustering algorithm for sensors, minimizing communication between UAVs and sensors. It then refines the UAV’s energy consumption equation by integrating propulsion energy needs, actual terrain data, and wind effects. Compared to other heuristic algorithms, the MRFO algorithm demonstrates superior performance in path planning, particularly for complex engineering optimization problems, displaying heightened adaptability and efficiency. Comparative experimental results on real terrain data and MATLAB r2018b simulation show that the error between the corrected energy calculation equation and the actual value is controlled within 5%, and the accuracy is improved by 10% over the original equation. Meanwhile, the ability of the MRFO algorithm to quickly construct approximate high-quality solutions with shortest path lengths in a limited number of iterations validates its potential in practical applications. The α-hop clustering algorithm used in this paper has a huge advantage in space and time complexity compared with existing clustering algorithms, and the accuracy of data extraction is relatively improved by 7.57% and 6.95%. Real forest digital elevation model (DEM) terrain data was introduced in this study, and the method improves the energy utilization of UAV data collection and also provides a comprehensive and detailed solution to the existing challenges faced in the field of forest data collection. Future research could consider combining the MRFO algorithm with other evolutionary classes of algorithms to take advantage of the algorithm’s fast convergence and high-precision properties to further enhance the application prospects in different scenarios. |
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| AbstractList | This study introduces an innovative UAV cruise data collection path planning approach using the manta ray foraging optimization (MRFO) algorithm to enhance efficiency and energy utilization in forest ecosystem monitoring. Traditionally reliant on costly manual patrols, this method leverages UAVs and ground-based sensors for data collection. The approach begins with a self-organized clustering algorithm for sensors, minimizing communication between UAVs and sensors. It then refines the UAV’s energy consumption equation by integrating propulsion energy needs, actual terrain data, and wind effects. Compared to other heuristic algorithms, the MRFO algorithm demonstrates superior performance in path planning, particularly for complex engineering optimization problems, displaying heightened adaptability and efficiency. Comparative experimental results on real terrain data and MATLAB r2018b simulation show that the error between the corrected energy calculation equation and the actual value is controlled within 5%, and the accuracy is improved by 10% over the original equation. Meanwhile, the ability of the MRFO algorithm to quickly construct approximate high-quality solutions with shortest path lengths in a limited number of iterations validates its potential in practical applications. The α-hop clustering algorithm used in this paper has a huge advantage in space and time complexity compared with existing clustering algorithms, and the accuracy of data extraction is relatively improved by 7.57% and 6.95%. Real forest digital elevation model (DEM) terrain data was introduced in this study, and the method improves the energy utilization of UAV data collection and also provides a comprehensive and detailed solution to the existing challenges faced in the field of forest data collection. Future research could consider combining the MRFO algorithm with other evolutionary classes of algorithms to take advantage of the algorithm’s fast convergence and high-precision properties to further enhance the application prospects in different scenarios. |
| Audience | Academic |
| Author | Zhang, Hanchao Chen, Zhibo Cui, Xiaohui Yuan, Jiateng Yang, Shijie |
| Author_xml | – sequence: 1 givenname: Shijie surname: Yang fullname: Yang, Shijie – sequence: 2 givenname: Jiateng surname: Yuan fullname: Yuan, Jiateng – sequence: 3 givenname: Zhibo surname: Chen fullname: Chen, Zhibo – sequence: 4 givenname: Hanchao surname: Zhang fullname: Zhang, Hanchao – sequence: 5 givenname: Xiaohui orcidid: 0009-0000-2759-1400 surname: Cui fullname: Cui, Xiaohui |
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| SubjectTerms | Accuracy Aerial surveys Algorithms Biodiversity Cameras Carbon sequestration Clustering Complexity Data collection Data transmission digital elevation model Digital Elevation Models Drone aircraft Earth Energy consumption Energy utilization Environmental monitoring Error correction Evolutionary algorithms Feasibility Forest ecology Forests Heuristic methods heuristic path planning Mathematical models Mathematical optimization Optimization sensor clustering Sensors Shortest-path problems Simulation Technology application Terrain unmanned aerial vehicle Wind effects |
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