REAC: Remote Autonomous Quadrotor Control System

Abstract

Research in quadrotor control has yielded promising results, with most algorithms being deployed onboard. However, the substantial mass of the onboard computer limits the quadrotor’s performance potential. In this work, we propose a remote autonomous control (REAC) system that shifts computational loads to a remote computer, eliminating the onboard computing payload and enabling multi-quadrotor control. REAC consists of an explicit trajectory planner that generates reference trajectories at low frequency and a robust controller that follows them by generating real-time actions. However, remote control inherently introduces persistent and time-varying transmission delays, leading to inaccurate state estimation and a temporal mismatch between control actions and system dynamics. To address this, we incorporate a temporal buffering mechanism into the controller. In parallel, we propose a two-phase training strategy: initial policy shaping through imitation learning, followed by reinforcement learning refinement with randomized disturbance injections. Simulation experiments demonstrate the effectiveness of the REAC in remote quadrotor control and exhibit robustness against environmental uncertainties brought by transmission delays, while preliminary real-flight tests indicate practical feasibility.

Keywords

quadrotor control / remote autonomous control (REAC) / trajectory planning / delay compensation / reinforcement learning