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Acceleration-Aware Path Planning with Waypoints
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AI
DOI：10.3390/drones5040143.png)
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In this article we demonstrate that acceleration and deceleration of direction-turning drones at waypoints have a significant influence to path planning which is important to be considered for time-critical applications, such as drone-supported search and rescue. We present a new path planning approach that takes acceleration and deceleration into account. It follows a local gradient ascend strategy which locally minimizes turns while maximizing search probability accumulation. Our approach outperforms classic coverage-based path planning algorithms, such as spiral- and grid-search, as well as potential field methods that consider search probability distributions. We apply this method in the context of autonomous search and rescue drones and in combination with a novel synthetic aperture imaging technique, called Airborne Optical Sectioning (AOS), which removes occlusion of vegetation and forest in real-time.
Keyword:
search and rescue
autonomous drones
path planning
acceleration
deceleration
期刊
D
IF:
4.8
论文数:
3.9K
被引数:
8.3K
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