CAPER: A Connectivity-Aware Path Planner with Regulatory Compliance for UAVs

摘要

Well-connected, regulatory compliant flight paths are crucial for UAVs to be adopted in mission-critical applications. In this paper, we present the Connectivity-Aware Path plannEr with Regulatory compliance (CAPER): a solution for planning safe, cellular-connected UAV paths in environments with heterogeneous connectivity regions, such that the planned paths comply with regulatory no-fly zones and height constraints. CAPER builds on the sampling-based planner Rapidly-exploring Random Trees (RRT), and makes a number of algorithmic modifications both in the planner and the collision detector. RRT has seen widespread use in planning paths in robotics, due to its ability to quickly search high dimensional spaces for feasible paths. However, several challenges exist in adopting RRTs for the connectivity-aware path planning problem in realistic spaces, which CAPER seeks to alleviate. In this paper we detail CAPER, and present results of its implementation in two realistic urban environments in Stockholm and Los Angeles. Since CAPER is built on the randomized algorithm RRT, we also present a brief analysis of multiple runs within the same environment.