Research Of The Navigation System For Agricultural Equipment Based On Beidou And Vision

With the continuous acceleration of modernization,the rapid development of unmanned technology and the concept of precision agriculture,the application of autonomous navigation system in agriculture has become a popular issue concerning the national economic development and scientific and technological progress.The navigation technology of agricultural equipment is one of the core technologies for the current domestic and foreign agricultural intelligence,unmanned and modernization.In the navigation and positioning accuracy of agricultural equipment is often demanding,the current navigation of agricultural equipment is mainly based on satellite navigation,a single satellite navigation is far from being able to meet the operational positioning accuracy requirements.In order to realize the operation navigation and precise positioning of agricultural equipment,this paper mainly researches the navigation system of agricultural equipment based on Beidou and vision,and designs a combined navigation system with Beidou satellite navigation system and vision navigation system as the main,supplemented by inertial navigation,to obtain high accuracy under the premise of ensuring stability,compatibility and economy.The main research contents are as follows.Aiming at the problems of low accuracy and incomplete information of a single navigation system in the field of unmanned agricultural equipment,the working principle of Bei Dou satellite navigation system,the types and sources of errors generated are studied,and combined with vision navigation-related technologies,a combined navigation method based on Bei Dou and vision is proposed,combining RTK technology with BDS to eliminate some satellite positioning errors and achieve high accuracy positioning.To address the problems of low resolution accuracy and slow processing speed when traditional image processing methods are used for visual navigation applications,deep learning methods and semantic segmentation techniques are studied,and a navigation line extraction technique based on semantic segmentation is proposed,using PSPNet network to segment the images acquired during the operation of agricultural equipment to make recognition of crops and roads,and based on the recognition results of semantic segmentation,a region of interest method is proposed to The pixel value of the target region is changed to derive the road boundary coordinates,and the median value is taken for fitting,and then the navigation baseline is extracted.Aiming at the demand of autonomous navigation operation of unmanned agricultural machinery and the defects of slow convergence speed,low convergence accuracy and easy to fall into local optimum of traditional whale optimization algorithm in solving path planning problems,a global path planning method for agricultural equipment with improved whale optimization algorithm(IWOA)is proposed,which incorporates three strategies of Cauchy Mutation,adaptive weight factor and backward learning of dimension-by-dimension small-hole imaging to improve the algorithm’s accuracy and speed to solve the path planning problem of unmanned farm machinery operation.According to the overall design scheme and requirements of the navigation system for agricultural equipment,the hardware selection of each functional module of Bei Dou satellite navigation system,visual navigation system and inertial navigation system is carried out,and the information obtained by different navigation systems is synthesized and estimated using data fusion methods.The key technical specifications of the designed navigation system based on Beidou and vision for agricultural equipment were built into the hardware platform,and the system was experimentally tested.The results showed that the navigation system has high positioning accuracy and strong self-adaptive interference capability,which can meet the operational accuracy requirements of agricultural equipment and provide technical support for the automatic navigation control of agricultural equipment.Figure 40 Table 5 Reference 89...