A Visual Recognition and Path Planning Method for Intelligent Fruit-Picking Robots

Abstract

With the rapid development of economy and the increasing improvement of agricultural production level, people’s demand for fruits is also increasing year by year. China is the largest fruit production and consumption country in the world. According to relevant statistics reported for China, by the end of 2019, the total amount of various fruits sold had reached about 270 million tons, with apples accounting for 48% of the global output and pears accounting for 69% of the national total output. However, China’s fruit picking is still dominated by manual picking process, which takes a lot of manpower and time to complete, therefore resulting in low fruit picking efficiency. Some farmers are unable to complete fruit picking in a short time, resulting in a large number of fruits unable to be listed, resulting in huge losses. To solve this problem, this paper focuses on the visual recognition and path planning for intelligent fruit-picking robot. Using robot to complete fruit picking is the best way at present. This paper establishes a picking robot recognition and positioning system based on stereo vision, which is used to identify and locate the fruits planted in the orchard area. The coordinate error of the target point of the intelligent fruit-picking robot coordinate system is less than 10 mm, which has high accuracy. Then, the path of intelligent fruit-picking robot is planned based on visual feedback algorithm and biological stimulation neural network. Our empirical evaluations suggest that the proposed robot walks in the planting park in the shape of “zigzag” and realizes full-coverage path planning after 6 turns. The results show the efficiency of the intelligent method.

Three-dimensional continuous picking path planning based on ant colony optimization algorithm

“…The experimental results show that the method can effectively improve the model’s performance in an unstructured environment and increase the learning efficiency and path planning success rate at the early stage of training. Li et al [ 17 ] focused on the problem of visual recognition and path planning for intelligent fruit-picking robots. They established a stereo vision-based identification and positioning system for picking robots, and the coordinate error of the target point of the intelligent fruit picking robot coordinate system is less than 10 mm, with high accuracy.…”

GDMR-Net: A Novel Graphic Detection Neural Network via Multi-Crossed Attention and Rotation Annotation for Agronomic Applications in Supply Cyber Security

“…Although robots and related information technologies have achieved wide application in agricultural supply management, there is still a very challenging problem in precise crop category detection. This important task involves assigning individual plants to a specific species or name based on their characteristics and morphology, and further determining the targets' size, spatial distribution, and density statistics [5].…”