Abstract
Finding an optimal/quasi-optimal path for Unmanned Aerial Vehicles (UAVs) utilizing full map information yields time performance degradation in large and complex three-dimensional (3D) urban environments populated by various obstacles. A major portion of the computing time is usually wasted on modeling and exploration of spaces that have a very low possibility of providing optimal/sub-optimal paths. However, computing time can be significantly reduced by searching for paths solely in the spaces that have the highest priority of providing an optimal/sub-optimal path. Many Path Planning (PP) techniques have been proposed, but a majority of the existing techniques equally evaluate many spaces of the maps, including unlikely ones, thereby creating time performance issues. Ignoring high-probability spaces and instead exploring too many spaces on maps while searching for a path yields extensive computing-time overhead. This paper presents a new PP method that finds optimal/quasi-optimal and safe (e.g., collision-free) working paths for UAVs in a 3D urban environment encompassing substantial obstacles. By using Constrained Polygonal Space (CPS) and an Extremely Sparse Waypoint Graph (ESWG) while searching for a path, the proposed PP method significantly lowers pathfinding time complexity without degrading the length of the path by much. We suggest an intelligent method exploiting obstacle geometry information to constrain the search space in a 3D polygon form from which a quasi-optimal flyable path can be found quickly. Furthermore, we perform task modeling with an ESWG using as few nodes and edges from the CPS as possible, and we find an abstract path that is subsequently improved. The results achieved from extensive experiments, and comparison with prior methods certify the efficacy of the proposed method and verify the above assertions.
Highlights
Unmanned aerial vehicles (UAVs) are highly useful for executing diverse missions in urban environments and in hazardous areas that are not reachable, such as forests, deserts, and hilly areas
We compared our Path Planning (PP) method’s performance with two existing algorithms: the Approximation with Visibility Line (ApVL) algorithm proposed by Guillermo et al [60] and the Rapidly Exploring Random Trees (RRTs)*-AB algorithm proposed by Noreen et al [70]
This article proposed a new PP method based on Constrained Polygonal Space (CPS) and an Extremely Sparse Waypoint Graph (ESWG) to enable a UAV’s safe navigation in 3D urban environments
Summary
Unmanned aerial vehicles (UAVs) are highly useful for executing diverse missions in urban environments and in hazardous areas that are not reachable, such as forests, deserts, and hilly areas. Owing to military and civilian investments in UAV technology, this field continuously advances with the passage of time. Advancements in the technology, such as improved computation capacity, low-cost sensors, artificial intelligence-based algorithms, and fuzzy logic–based decision-making abilities, enable UAVs to perform many practical applications in complex environments that otherwise would take a long time and require significantly high costs. We briefly discuss the UAV operating environment’s modeling techniques, the pathfinding algorithms, and geometric- and sampling-based PP methods. A comprehensive discussion about the performance impacts of distinct environment modeling techniques collectively tested with their respective search methods was given by Sariff et al [52]. Many UAV operating environment methods have been discussed in the published studies These modeling methods are categorized as RoadMap (RM), Cell Decomposition (CD), and Potential Fields (PF). After modeling the environment with a visibility/waypoint graph, a search algorithm is utilized for the graph’s exploration in order to find a path
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