Persistent Charging System for Crazyflie Platform

Nowadays, quadcopters are used widely in different applications, but their flight time is limited during operation. In this paper, a precision landing method based on a Kalman filter is proposed for an autonomous indoor persistent drone system that aims to increase the flight time of quadcopters. First, a local positioning system is used for tracking performance. Second, instead of using this local positioning system during the landing phase, a multi-ranger sensor is proposed to increase the accuracy of horizontal errors. Next, based on the relative position provided by the multi-ranger sensor, a Kalman filter technique is applied to estimate the relative velocity of the system, which is then applied to control the position of the quadcopter during the landing phase. Finally, a charging state machine law is proposed to charge the battery of three quadcopters sequentially. The experimental results demonstrate that the proposed concept based on a multi-ranger sensor can enhance the accuracy of the landing phase in comparison with the conventional method.

Automated summary

This paper proposes a precision landing method based on a Kalman filter to increase the flight time of quadcopters. It uses a local positioning system for tracking performance and a multi-ranger sensor to improve the accuracy of horizontal errors during the landing phase. The relative velocity of the system is estimated using a Kalman filter technique, which is then used to control the quadcopter's position during landing. A charging state machine law is also proposed to sequentially charge the quadcopters' batteries. Experimental results show that the proposed method enhances the accuracy of the landing phase compared to conventional methods.