On-Demand Precise Tracking for Energy-Constrained UAVs in Underground Coal Mines

Abstract

The precise tracking of unmanned aerial vehicle (UAV) in coal mines remains a challenging task that faces strict energy constraints. The existing approaches mainly focus on achieving better accuracy but ignore energy consumption. This article proposes an on-demand precise tracking (OPT) framework based on the fusion of the ultrawideband (UWB) and inertial measurement unit (IMU). The fusion unscented Kalman filter (fusion UKF) is devised for energy-efficient tracking with customized performance. First, OPT provides an adaptive adjustment mechanism of UWB signal transmission to trade off between accuracy and energy consumption for UAV local positioning in coal mines. Second, we propose an on-demand trigger algorithm for UAV remote tracking to determine whether the data are transmitted to the ground server. Especially, the on-demand trigger condition is ameliorated due to the existence of packet drops in complicated coal mines. Finally, extensive simulations in the Gazebo Platform and field experiments utilizing P440 UWB nodes in the indoor and coal mine laboratories have been conducted to verify the feasibility. Results show that OPT is efficient and practical and balances the performance and energy consumption under appropriate parameters. Compared with the existing event-triggered extended Kalman filter (ET-EKF) scheme, the accuracy is improved by 10.3%, and the communication rate is contracted by 11.4% in the coal mine environment.