Path Planning Strategies for UAVS in 3D Environments

Abstract

The graph-search algorithms developed between 60s and 80s were widely used in many fields, from robotics to video games. The A* algorithm shall be mentioned between some of the most important solutions explicitly oriented to motion-robotics, improving the logic of graph search with heuristic principles inside the loop. Nevertheless, one of the most important drawbacks of the A* algorithm resides in the heading constraints connected with the grid characteristics. Different solutions were developed in the last years to cope with this problem, based on postprocessing algorithms or on improvements of the graph-search algorithm itself. A very important one is Theta* that refines the graph search allowing to obtain paths with "any" heading. In the last two years, the Flight Mechanics Research Group of Politecnico di Torino studied and implemented different path planning algorithms. A

Autonomous monitoring, analysis, and countering of air pollution using environmental drones

“…Path planning refers to planning an optimal flight track for a UAV from a starting point to a target point to guarantee successful completion of mission while avoiding static and dynamic obstacles. Path planning (Filippis et al, 2012;Yanmaza et al, 2017) is essential for UAVs to avoid obstacle collision. The E-drone was programmed to fly upwards in a straight trajectory to the predetermined E altitude , conduct environmental pollution detection and abatement, and then to fly downwards in a straight trajectory back to the earth surface.…”

Survey of Robot 3D Path Planning Algorithms

“…The algorithm has the ability to be able to obtain system constraints; thus it can find shorter and more realistic path. De Filippis et al [103] implemented both Theta * and A * in 3D environment, and an experimental comparison is given to prove that Theta * reduces the searching compared to A * . Although Theta * acts well compared to A * , but when applied to 3D environment, it consumes much time to check unexpected neighbors.…”

A New Path Planning Algorithm Using a GNSS Localization Error Map for UAVs in an Urban Area

“…Considering the physical characteristics of aircraft, the A* algorithm is improved with extra constraints such as heading [17] and turning [18], resulting in a more appropriate route for aircraft.…”