Acceleration-Aware Path Planning with Waypoints

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.