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GenPath - A Genetic Multi-Round Path Planning Algorithm for Aerial Vehicles
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AI
DOI：10.1109/INFOCOMWKSHPS51825.2021.9484505.png)
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Abstract
En 中文
The past few years have witnessed unprecedented proliferation of Unmanned Aerial Vehicles (UAVs). They are employed in a growing number of scenarios, from parcel delivery to search and rescue operations, requiring coordinated missions of a fleet of drones. Recently, there has been growing interest in optimized techniques to assign tasks and related trajectories to drones. While these techniques promise high coverage of inspected area, their applicability in real scenarios is precluded by unconsidered constraints. Among these, the limited amount of power of UAVs, and the consequent need of performing multiple trips to provide complete monitoring coverage, with battery replacement/charging and data offloading in between. To address this problem we develop Gen-Path, a genetic algorithm for efficient scheduling of multi-round UAV missions, under several objective functions. By means of simulations we show that Gen-Path fits various scenarios, improving existing solutions in terms of covered points, and energetic cost.
Keywords:
UAV
Path Planning
Genetic Algorithms
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