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AN IMPLEMENTATION OF UAS PATH PLANNING FOR DYNAMICAL WILDFIRE MONITORING WITH UNEVEN IMPORTANCE
ABSTRACT
When compared to traditional wildfire monitoring technologies, unmanned aerial systems (UASs) offer numerous advantages in wildfire monitoring.When planning the course of a UAS for wildfire monitoring, it is critical to consider the uneven propagation nature of the wildfire since different portions of the fire perimeter require varying levels of monitoring attention (importance) dependent on the propagation speed.Furthermore, several existing works use a centralized strategy for UAS path planning.However, the adoption of centralized techniques is frequently limited in terms of applicability and adaptability.
This research focuses on building decentralized UAS path-planning algorithms to autonomously monitor a rising wildfire while accounting for uneven priority. The algorithms enable UASs to target the most active areas of a wildfire while still covering the full perimeter.
When monitoring a relatively small and geographically static fire, a single UAS may be sufficient. When monitoring a larger wildfire that is changing in location and time, the efficient and optimized usage of many UASs is necessary. We also work on decentralized and importance-based multi-UAS path planning for wildfire monitoring in response to this need. The design, implementation, analysis, and simulation results for both single-UAS and multi-UAS path planning algorithms have been thoroughly explored. The results of the experiments demonstrate the efficacy and robustness of the suggested algorithms for dynamic wildfire monitoring.
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