A Light-Weight, Low-Cost, and Sustainable Planning System for UAVs Using a Local Map Origin Update Approach

Abstract

This paper proposes a sustainable planning system for small-sized unmanned aerial vehicles (UAVs). Our mapping module of the system uses a voxel array as data structure with an introduced feature which is local map origin update. This approach has clear advantages that the planning system can sustainably plan trajectories regardless of operating radius and flight distance, and it shows fastest invariant time complexity \mathcal{O}(1)\mathcal{O}(1) unlike other representation methods. Also, we propose an efficient configuration space (C-space) construction algorithm using incremental voxel inflation, and extend state-of-the-art Euclidean signed distance field (ESDF) algorithm, FIESTA by applying the local map origin update feature. The proposed planning system requires single depth camera only as a sensor, and can operate in realtime on embedded computing platforms. We have verified the planning system through real-world flight tests in dense environments using a lightweight quadrotor plat-form under 300 mm size equipped with low-cost components only.