Path Planning Method for UAVs Based on Constrained Polygonal Space and an Extremely Sparse Waypoint Graph

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.

An overview of path planning technologies for unmanned aerial vehicles

“…These methods require some predefined information of the workspace according to the 3-D environment, and they also rapidly generate optimal global solutions or near-optimal solutions. Because of their algorithmic simplicity, these methods are suitable for solving both single-query and real-time PP problems [8][9][10][11][12][13][14][15][16][17][18].…”

“…The GA is one of the intelligent optimization algorithms commonly used in UAV PP problems [8]. Discrete PPA, such as potential fields and grid-based methods, require significant memory and CPU performance.…”

“…In parallel with the widespread use of UAV in many different fields, the problem of PP has become a very important research subject. A PP problem is about finding a flyable path between the starting and target location while safely passing obstacles in the underlying 3-D environment map, simultaneously meeting one or more optimization objectives such as time, distance and energy consumption [8]. During its flight to fulfill a mission, the UAV needs to analyze its position, environment, obstacles and other essential information calculate and find the safest path.…”

A Survey of Path Planning Algorithms for Mobile Robots

“…The list of covered algorithms is, therefore, not exhaustive, but provides coverage of several common algorithms used in the context of path planning for autonomous vehicles and robotic systems. Other path planning algorithms are discussed in [15].…”

OADC: An Obstacle-Avoidance Data Collection Scheme Using Multiple Unmanned Aerial Vehicles

“…The authors developed a method where a graph with elliptic tangents is used that ensures obstacle avoidance. The study presented in [24] proposed collision-idle optimal or quasioptimal paths for UAVs in a 3D urban area, considering the obstacles and focusing on time complexity reduction. In [25], the authors focused on various task allocation and trajectory planning procedures for multiple UAVs while avoiding obstacles and arriving at the targets.…”