System identification methods for dynamic models of brain activity

Griffith, Tristan D.; Hubbard, James E.

doi:10.1016/j.bspc.2021.102765

Cited by 7 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Obstacles in the neural activity state diagram can only be partially avoided by the robot [22]. e dynamic neuron activity state diagram based on neural network, combined with the incomplete kinematic constraints of the robot, can reasonably plan the robot path for autonomous obstacle avoidance [23].…”

Section: Neural Network Dynamic Model Of Biologicalmentioning

confidence: 99%

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

Li¹

2022

Scientific Programming

View full text Add to dashboard Cite

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.

show abstract

Section: Neural Network Dynamic Model Of Biologicalmentioning

confidence: 99%