Intelligent navigation algorithm of plant phenotype detection robot based on dynamic credibility evaluation

Due to the non-standardization and complexity of the farmland environment, Global Navigation Satellite System (GNSS) navigation signal may be affected by the tree shade, and visual navigation is susceptible to winged insect and mud, which makes the navigation information of the plant phenotype detection robot unreliable. To solve this problem, this study proposed a multi-sensor information fusion intelligent navigation algorithm based on dynamic credibility evaluation. First, three navigation methods were studied: GNSS and Inertial Navigation System (INS) deep coupling navigation, depth image-based visual navigation, and maize image sequence navigation. Then a credibility evaluation model based on a deep belief network was established, which used dynamically updated credibility to intelligently fuse navigation results to reduce data fusion errors and enhance navigation reliability. At last, the algorithm was loaded on the plant phenotype detection robot for experimental testing in the field. The result shows that the navigation error is within 2.7 cm and the navigation effect of the multi-sensor information fusion method is better than that of the single navigation method. The multi-sensor information fusion method proposed in this study uses the credibility model of the deep belief network to perform navigation information fusion, which can effectively solve the problem of reliable navigation of the plant phenotype detection robot in the complex environment of farmland, and has important application prospects.