PACED-5G: 5G網とエッジを活用したドローンの予測的自律制御
PACED-5G: Predictive Autonomous Control using Edge for Drones over 5G
5G通信の遅延を考慮した状態推定器とモデル予測制御を組み合わせ、エッジに高負荷処理をオフロードしながら障害物回避と軌道追従を行うドローン制御アーキテクチャを提案し、実機で検証した。
著者: Viswa Narayanan Sankaranarayanan, Gerasimos Damigos, Achilleas Santi Seisa, Sumeet Gajanan Satpute, Tore Lindgren, George Nikolakopoulos
分類: cs.RO
原文アブストラクト
With the advent of technologies such as Edge computing, the horizons of remote computational applications have broadened multidimensionally. Autonomous Unmanned Aerial Vehicle (UAV) mission is a vital application to utilize remote computation to catalyze its performance. However, offloading computational complexity to a remote system increases the latency in the system. Though technologies such as 5G networking minimize communication latency, the effects of latency on the control of UAVs are inevitable and may destabilize the system. Hence, it is essential to consider the delays in the system and compensate for them in the control design. Therefore, we propose a novel Edge-based predictive control architecture enabled by 5G networking, PACED-5G (Predictive Autonomous Control using Edge for Drones over 5G). In the proposed control architecture, we have designed a state estimator for estimating the current states based on the available knowledge of the time-varying delays, devised a Model Predictive controller (MPC) for the UAV to track the reference trajectory while avoiding obstacles, and provided an interface to offload the high-level tasks over Edge systems. The proposed architecture is validated in two experimental test cases using a quadrotor UAV.
