In this article we demonstrate that acceleration and deceleration of direction-turning drones at waypoints have a significant influence to path planning which is 9x11 pergola 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 arcade smokey the bear belt 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.