Research On Algorithms For Target Recognition And Path Planning Of Apple Picking Robot Based On Deep Camera

In China's agricultural production,fruit and vegetable harvesting has always been an important part of the fruit and vegetable industry.Because of the backwardness of agricultural production technology,harvesting work needs a lot of manpower and material resources,which increases the cost of agricultural production and restricts the development of agricultural modernization.Therefore,we should speed up the research of picking robots,improve the efficiency of picking robots,promote the transformation and upgrading of agriculture,and achieve highly automated and accurate agricultural development goals.Under the support of the project of National Natural Science Foundation of China(31571571),"Research on the efficient apple picking method in multi-illumination environment based on fast visual servo control",this paper mainly studies the target detection and path planning algorithm of apple picking robot.1.Design of visual servo system for apple picking robot.According to the requirements and objectives of the experiment,the visual platform of apple picking robot was designed,and the hardware structure and software algorithm were built.2.Research on Apple recognition algorithm.In order to improve the efficiency of apple picking robot,it is necessary to recognize apples quickly and accurately in the image.In this paper,the convolutional neural network model SSD is chosen as the basic network architecture.The basic principle and structure of the network framework are introduced in detail from the default frame design,matching principle and loss function.The basic SSD network framework is improved from the default frame and basic network layer.For the improved SSD network framework,a dedicated Apple data set is designed for training and testing,and the best performance model parameters are selected.Finally,the improved SSD network framework can accurately and quickly identify multiple target fruits in the image,and obtain their two-dimensional position coordinates.3.Deep information acquisition.This paper first compares three different principles of depth cameras: structured light,binocular vision and TOF time-of-flight method.Based on the requirements and technical parameters of this project,TOF camera is selected as the equipment to collect depth information.Secondly,the depth of the apple is determined by combining the two-dimensional coordinates of the apple obtained before,and then the information of the depth map is used to determine whether there are obstacles in the surrounding area of the apple,and the types of obstacles are distinguished by combining the feature information of the three-dimensional reconstruction map and the intensity map.Finally,the three-dimensional coordinates of the target fruit and its surrounding obstacles are obtained,which provides complete information for environmental modeling of path planning.4.Research on the algorithm of picking path planning.Firstly,this paper introduces the search theory and environment modeling method.Secondly,combined with the location information and grid method obtained above,the three-dimensional picking planning is transformed into the path planning on the two-dimensional plane,and the two-dimensional picking map with multiple target fruits and different kinds of obstacles is obtained.Then,the basic principle and main steps of the standard A* algorithm are studied.To solve the problems of slow planning time and too many searching nodes in standard A* algorithm,the heuristic function,data structure of list and mobile cost are improved respectively,so that the improved A* algorithm speeds up the planning path,reduces the length of picking path and is more suitable for the actual picking environment.Finally,using the three-dimensional position information of apples and obstacles,the planned picking path on the two-dimensional picking map is extended to the three-dimensional path points in the actual picking space,providing accurate path information for the next picking experiment.5.Picking experiment results and analysis.This paper mainly verifies the effectiveness and real-time of the improved algorithm from the identification of fruit time,path planning time,picking path length and total picking time in the picking experiment.The experimental results show that the improved SSD model not only improves the recognition speed by 30% compared with the basic model,but also ensures better recognition accuracy and stability.The improved A* algorithm improves the total picking path by 26% and the planning time by 40%,which ensures the shortening of the total picking time and improves the picking efficiency of the picking robot.Finally,the experimental results show that the improved algorithm takes about 11.4 seconds to grab an apple,which is 30.06% higher than before,and meets the requirements of apple picking robot.