Tractor Assistant Driving Control Method Based on EEG Combined With RNN-TL Deep Learning Algorithm

Wei Lu,Yiming Deng,Jinxia Yuan,Yuning Wei,Aiguo Song

doi:10.1109/access.2020.3021051

Abstract

Nowadays, fieldwork is often accompanied by tight schedules, which tends to strain the shoulder muscles due to high-intensity work. Moreover, it is difficult and stressful for the disabled to drive agricultural machinery. Besides, current artificial intelligence technology could not fully realize tractor autonomous driving because of a high uncertain filed environment and short interruptions of satellite navigation signal shaded by trees. To reduce manual operations, a tractor assistant driving control method was proposed based on the human-machine interface utilizing the electroencephalographic (EEG) signal. First, the EEG signals of the tractor drivers were collected by a low-cost brain-computer interface (BCI), followed by denoising using a wavelet packet. Then the spectral features of EEG signals were calculated and extracted as the input of Recurrent Neural Network (RNN). Finally, the EEG-aided RNN driving model was trained for tractor driving robot control such as straight going, brake, left turn, and right turn operations, which control accuracy was 94.5% and time cost was 0.61 ms. Also, 8 electrodes were selected by the PCA algorithm for the design of a portable EEG controller. And the control accuracy reached 93.1% with the time cost of 0.48 ms. To solve the incomplete driving data set in the actual world because some driving manners may cause dangerous or even death, RNN-TL algorithm was employed by creating the complete driving data in the virtual environment followed by transferring the driving control experience to the actual world with small actual driving data set in the field, which control accuracy was 93.5% and time consumption was 0.48 ms. The experimental results showed the feasibility of the proposed tractor driving control method based on EEG signal combined with RNN-TL deep learning algorithm which can work with the displacement error less than 6.7 mm when the tractor speed is less than 50 km/h.

Highlights

Agricultural machinery is popularly used in most field operations such as tillage, harvesting, weeding, and land preparation for improving agricultural efficiency
We proposed a tractor assistant driving technology based on the EEG signal and tested on the second generation of tractor driving robot platform developed by our lab [22]–[24]
Firstly, the EEG signals of the subjects were collected in the virtual driving environment without any danger to build a complete data set by using the Emotiv-EPOC+ system followed by denoising using 8th order of 50 Hz Butterworth low-pass filter

Summary

INTRODUCTION

Agricultural machinery is popularly used in most field operations such as tillage, harvesting, weeding, and land preparation for improving agricultural efficiency. W. Lu et al.: Tractor Assistant Driving Control Method Based on EEG Combined With RNN-TL Deep Learning Algorithm the disabled drivers whose proportion is about one fifth in America. Since electroencephalogram (EEG) signal is the direct embodiment of human brain consciousness, the auxiliary driving method based on the EEG signal can reduce the driver’s workload, improve work precision, avoid disastrous consequences caused by driver fatigue operation, and provide a feasible solution for the disabilities to drive agricultural machinery as well. Gomez-Gil in tractor steering control with a deviation of less than 7 cm from the standard track which shows the feasibility of tractor driving based on EEG [21]. We proposed a tractor assistant driving technology based on the EEG signal and tested on the second generation of tractor driving robot platform developed by our lab [22]–[24]. The experiments were carried out and the results were discussed and concluded

MATERIALS AND METHODS

DISCUSSION AND CONCLUSION

Findings

CONCLUSION