Search for: All records

Localization of radio-tagged wildlife is essential in environmental research and conservation. Recent advancements in Uncrewed Aerial Vehicles (UAVs) have expanded the potential for improving this process. However, a key challenge lies in the optimal choice of waypoints for UAVs to localize animals with high precision. This study addresses the intelligent selection of waypoints for UAVs assigned to localize multiple stationary Very High Frequency (VHF)-tagged wildlife simultaneously, with a primary emphasis on minimizing localization uncertainty in the shortest possible time. At each designated waypoint, the UAV obtains bearing measurements to tagged animals, considering the associated uncertainty. The algorithm then intelligently recommends subsequent locations that minimize predicted localization uncertainty while accounting for constraints related to mission time, keeping the UAV within signal range, and maintaining a suitable distance from targets to avoid disturbing the wildlife. The evaluation of the algorithm’s performance includes comprehensive assessments, featuring the analysis of uncertainty reduction throughout the mission, comparison of estimated animal locations with ground truth data, and analysis of mission time using Monte Carlo simulations.