Abstract
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.
Highlights
Autonomous UAVs are becoming more and more adept at handling complex tasks and are used in various civil and commercial applications [1]
This article demonstrates that considering acceleration and deceleration matters for realistic path planning—especially when drones are applied, where velocity is by far not linear over the flight path
Acceleration and deceleration for waypoint sampling has a significant share of total flight time
Summary
Autonomous UAVs are becoming more and more adept at handling complex tasks and are used in various civil and commercial applications [1]. An additional constraint in search and rescue scenarios can be considered of locating the target as fast as possible. This type of problem is well studied in literature and is termed as a minimum time search problem (MTS) [2,3,4,5,6,7,8,9,10,11,12,13,14].
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call