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 2.7 cm and the navigation effect of the multi-sensor information fusion method is better than that of the single navigation method in the case of multiple disturbances.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.

Relay navigation strategy study on intelligent drive on urban roads

In order to solve navigation problem of intelligent vehicle driving on urban roads and to achieve the navigation in intersection area, intersection transition area and section area. The relay navigation strategy and algorithm can solve the navigation problem of intelligent vehicle driving in typical urban roads such as intersection area, intersection transition area and section area, realizing seamless handover among different typical areas. Bezier curve function model was introduced to different typical areas, which solved the self-adaption recognition problem in different typical areas and revised positional accuracy with the help of cloud computing positioning service. In order to explain the strategy implement, an instance based on the strategy was adopted. Instance analysis indicates that as for the navigation problem in intersection area, intersection transition area and section area, if the relay navigation strategy is utilized, the self-adaption recognition problem in different typical areas can be handled. Based on the relay navigation strategy, the drive of intelligent vehicle on urban roads can effectively solve the self-adaption recognition problem in different typical areas in urban and further solve driving problems of intelligent vehicle of the same category in urban roads.