Digital Twin-Based Obstacle Avoidance for Unmanned Aerial Vehicles Using Feedforward-Feedback Control

Abstract

In this paper, a digital twin (DT) system is designed for an unmanned aerial vehicle (UAV) in obstacle avoidance to enable autonomous navigation and control. The DT system for the UAV is composed of four components: Virtual space, physical space, application services and data processing. To reduce computational burden and sensor requirement on the UAV, a trajectory planning framework is designed for obstacle avoidance in the DT system. Multiple feasible trajectories are generated offline by an EGO-Planner algorithm in the virtual space. The optimal feasible trajectory is selected by trajectory length and completion time of the multiple feasible trajectories. To improve control performance, a feedforward-feedback control strategy is proposed for the UAV using the DT system. In the virtual space, feedforward control input of the physical UAV is obtained offline by tracking the optimal feasible trajectory using a model predictive controller. The model predictive controller is also used as feedback control input for the physical UAV in the physical space. Experimental results show effectiveness of the autonomous navigation and control method for a quadrotor using feedforward-feedback control based on the DT system.