Abstract
This paper presents a multi-objective coverage flight path planning algorithm that finds minimum length, collision-free, and flyable paths for unmanned aerial vehicles (UAV) in three-dimensional (3D) urban environments inhabiting multiple obstacles for covering spatially distributed regions. In many practical applications, UAVs are often required to fully cover multiple spatially distributed regions located in the 3D urban environments while avoiding obstacles. This problem is relatively complex since it requires the optimization of both inter (e.g., traveling from one region/city to another) and intra-regional (e.g., within a region/city) paths. To solve this complex problem, we find the traversal order of each area of interest (AOI) in the form of a coarse tour (i.e., graph) with the help of an ant colony optimization (ACO) algorithm by formulating it as a traveling salesman problem (TSP) from the center of each AOI, which is subsequently optimized. The intra-regional path finding problem is solved with the integration of fitting sensors’ footprints sweeps (SFS) and sparse waypoint graphs (SWG) in the AOI. To find a path that covers all accessible points of an AOI, we fit fewer, longest, and smooth SFSs in such a way that most parts of an AOI can be covered with fewer sweeps. Furthermore, the low-cost traversal order of each SFS is computed, and SWG is constructed by connecting the SFSs while respecting the global and local constraints. It finds a global solution (i.e., inter + intra-regional path) without sacrificing the guarantees on computing time, number of turning maneuvers, perfect coverage, path overlapping, and path length. The results obtained from various representative scenarios show that proposed algorithm is able to compute low-cost coverage paths for UAV navigation in urban environments.
Highlights
Unmanned aerial vehicles (UAVs) are playing a vital role in the realization of smart cities, smart building, and smart infrastructures with innovative applications
In many practical applications, a UAV needs an ability to find a collision-free path between two pre-decided locations which is referred as path planning (PP) or to find a viable path which covers every reachable point of a certain area of interest (AOI) which is called coverage path planning (CPP)
We focus on the CPP problem for a UAV to cover multiple obstacle surrounded strewn AOIs located in 3D urban environments which has not been solved by prior studies
Summary
Unmanned aerial vehicles (UAVs) are playing a vital role in the realization of smart cities, smart building, and smart infrastructures with innovative applications. Collision avoidance with with obstacles, especially with other drones and, eventually, helicopters in complex 3D urban environments and payload constraints are noticeable challenge Beside these challenges, in many practical applications, a UAV needs an ability to find a collision-free path between two pre-decided locations which is referred as path planning (PP) or to find a viable path which covers every reachable point of a certain area of interest (AOI) which is called coverage path planning (CPP). The existing CPP algorithms for UAVs do not provide thorough insight into the coverage of multiple AOIs in complex 3D urban environments with obstacles They do not use sensor footprints (SF) as a coverage unit while decomposing the AOI, thereby causing significant path length degradation and overlapping.
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