Advances in Surveying Topographically Complex Ecosystems with UAVs: Manta Ray Foraging Algorithms

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.

Keywords

• unmanned aerial vehicle
• sensor clustering
• heuristic path planning
• digital elevation model