Rule-Based Path Planning for Unmanned Aerial Vehicles in Non-Segregated Air Space over Congested Areas

Abstract

In this paper, we present an approach for simplified path planning for unmanned aerial vehicles (UAVs) in obstacle-dense high-risk areas. We reduce the complexity of the 3D planning problem to that of a 2D planning problem by leveraging regulatory restrictions and guidelines as well as mission-specific boundary conditions to simplify the configuration space. This is achieved through strict limitations and a projection of the search space into a quasi-2D plane. We further suggest a modular motion planning architecture of multiple planners, each of which is taylored to a specific flight phase and displays deterministic behaviour in memory and runtime complexity. Through the integration of regulatory considerations, we seek to provide a planning result that complies with future regulations for flights over congested areas beyond the visual line of sight (BVLOS) and allows feasible solutions for dense multi-vehicle operation of urban routes. In addition to the methodological approach, we introduce the application of image processing techniques to the generation of roadmaps from available maps. We apply the method to a mission scenario of realistic extend and show how it scales to obstacle-dense environments. The results indicate that efficient pre-processing of environment data can enable regulation-compliant path-planning for UAVs in urban environments on consumer hardware.