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This paper presents a novel mission-oriented path planning algorithm for a team of Unmanned Aerial Vehicles (UAVs). In the proposed algorithm, each UAV takes autonomous decisions to find its flight path towards a designated mission area while avoiding collisions to stationary and mobile obstacles. The main distinction with similar algorithms is that the target destination for each UAV is not apriori fixed and the UAVs locate themselves such that they collectively cover a potentially time-varying mission area. One potential application for this algorithm is deploying a team of autonomous drones to collectively cover an evolving forest wildfire and provide virtual reality for firefighters. We formulated the algorithm based on Reinforcement Learning (RL) with a new method to accommodate continuous state space for adjacent locations. To consider a more realistic scenario, we assess the impact of localization errors on the performance of the proposed algorithm. Simulation results show that the success probability for this algorithm is about 80% when the observation error variance is as high as 100 (SNR:-6dB).
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This content will become publicly available on January 1, 2026
UAV Path Planning for Precision Multi-Target Localization
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.
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- Award ID(s):
- 2104570
- PAR ID:
- 10614397
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Access
- Volume:
- 13
- ISSN:
- 2169-3536
- Page Range / eLocation ID:
- 63715 to 63728
- Subject(s) / Keyword(s):
- Decision making, localization, radio telemetry, path planning, UAV, uncrewed aerial vehicle, autonomous aerial vehicle, wildlife monitoring
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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