Abstract
This paper presents a new coverage flight path planning algorithm that finds collision-free, minimum length and flyable paths for unmanned aerial vehicle (UAV) navigation in three-dimensional (3D) urban environments with fixed obstacles for coverage missions. The proposed algorithm significantly reduces computational time, number of turns, and path overlapping while finding a path that passes over all reachable points of an area or volume of interest by using sensor footprints’ sweeps fitting and a sparse waypoint graph in the pathfinding process. We devise a novel footprints’ sweep fitting method considering UAV sensor footprint as coverage unit in the free spaces to achieve maximal coverage with fewer and longer footprints’ sweeps. After footprints’ sweeps fitting, the proposed algorithm determines the visiting sequence of footprints’ sweeps by formulating it as travelling salesman problem (TSP), and ant colony optimization (ACO) algorithm is employed to solve the TSP. Furthermore, we generate a sparse waypoint graph by connecting footprints’ sweeps’ endpoints to obtain a complete coverage flight path. The simulation results obtained from various scenarios fortify the effectiveness of the proposed algorithm and verify the aforementioned claims.
Highlights
Unmanned aerial vehicles (UAVs) are achieving ground-breaking success in many application areas such as temporary infrastructure, monitoring and tracking, data collection and surveying, and delivery of goods [1]
Apart from the physical challenges, in many applications, a UAV needs the ability to compute a path between two pre-determined locations while avoiding various obstacles or to find a path which covers every reachable point of a certain area or volume of interest which is called coverage path planning (CPP)
We present the background and related work regarding the area of interest (AOI) decomposition techniques used for UAV CPP, coverage types, different types of the AOI used for coverage missions, geometric flight patterns, path optimization algorithms and application specific CPP methods
Summary
Unmanned aerial vehicles (UAVs) are achieving ground-breaking success in many application areas such as temporary infrastructure, monitoring and tracking, data collection and surveying, and delivery of goods [1]. Apart from the physical challenges, in many applications, a UAV needs the ability to compute a path between two pre-determined locations while avoiding various obstacles or to find a path which covers every reachable point of a certain area or volume of interest which is called coverage path planning (CPP). The basic approach adopted by most of the offline CPP algorithm is the area decomposition into non-overlapping subregions, determining the visiting sequence of the subregions, and covering decomposed regions individually in a back and forth manner to obtain a complete coverage path. Various approaches have been proposed to compute a low cost coverage path such as mirror mapping method [22], viewpoints sampling [23], in-field obstacles classification [24], optimal polygon decomposition [25], and context-aware UAV mobility [26].
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