scholarly journals Tool delivery robot using convolutional neural network

2020 ◽  
Vol 10 (6) ◽  
pp. 6300
Author(s):  
Javier Pinzon-Arenas ◽  
Robinson Jimenez-Moreno
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Trajectory Planning ◽  
Geometric Analysis ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Simulated Environment ◽  
Planning Algorithm ◽  
Position And Orientation

In the following article, it is presented a human-robot interaction system where algorithms were developed to control the movement of a manipulator in order to allow it to search and deliver, in the hand of the user, a desired tool with a certain orientation. A Convolutional Neural Network (CNN) was used to detect and recognize the user's hand, geometric analysis for the adjustment of the delivery status of the tool from any position of the robot and any orientation of the gripper, and a trajectory planning algorithm for the movement of the manipulator. It was possible to use the activations of a CNN developed in previous works for the detection of the position and orientation of the hand in the workspace and thus track it in real time, both in a simulated environment and in a real environment.

scholarly journals Active stability observer using artificial neural network for intuitive physical human–robot interaction

2017 ◽  
Vol 14 (4) ◽  
pp. 172988141772732 ◽  
Author(s):  
Mohamed Amir Sassi ◽  
Martin J-D Otis ◽  
Alexandre Campeau-Lecours
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Statistical Analysis ◽  
Real Time ◽  
Multilayer Perceptron ◽  
Human Robot Interaction ◽  
Sensor Signal ◽  
Robot Interaction ◽  
Artificial Neural ◽  
Real Time Simulations

Physical human–robot interaction may present an obstacle to transparency and operations’ intuitiveness. This barrier could occur due to the vibrations caused by a stiff environment interacting with the robotic mechanisms. In this regard, this article aims to deal with the aforementioned issues while using an observer and an adaptive gain controller. The adaptation of the gain loop should be performed in all circumstances in order to maintain operators’ safety and operations’ intuitiveness. Hence, two approaches for detecting and then reducing vibrations will be introduced in this study as follows: (1) a statistical analysis of a sensor signal (force and velocity) and (2) a multilayer perceptron artificial neural network capable of compensating the first approach for ensuring vibrations identification in real time. Simulations and experimental results are then conducted and compared in order to evaluate the validity of the suggested approaches in minimizing vibrations.

Real-time trajectory planning based on joint-decoupled optimization in human-robot interaction

2020 ◽  
Vol 144 ◽  
pp. 103664 ◽  
Author(s):  
Shiyu Zhang ◽  
Andrea Maria Zanchettin ◽  
Renzo Villa ◽  
Shuling Dai
Keyword(s):  
Real Time ◽  
Trajectory Planning ◽  
Human Robot Interaction ◽  
Robot Interaction

scholarly journals Real Time Multipurpose Smart Waste Classification Model for Efficient Recycling in Smart Cities Using Multilayer Convolutional Neural Network and Perceptron

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4916
Author(s):  
Ali Usman Gondal ◽  
Muhammad Imran Sadiq ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Multilayer Perceptron ◽  
Smart Cities ◽  
Classification Model ◽  
Rapid Urbanization ◽  
Metal Waste ◽  
Waste Classification ◽  
The City

Urbanization is a big concern for both developed and developing countries in recent years. People shift themselves and their families to urban areas for the sake of better education and a modern lifestyle. Due to rapid urbanization, cities are facing huge challenges, one of which is waste management, as the volume of waste is directly proportional to the people living in the city. The municipalities and the city administrations use the traditional wastage classification techniques which are manual, very slow, inefficient and costly. Therefore, automatic waste classification and management is essential for the cities that are being urbanized for the better recycling of waste. Better recycling of waste gives the opportunity to reduce the amount of waste sent to landfills by reducing the need to collect new raw material. In this paper, the idea of a real-time smart waste classification model is presented that uses a hybrid approach to classify waste into various classes. Two machine learning models, a multilayer perceptron and multilayer convolutional neural network (ML-CNN), are implemented. The multilayer perceptron is used to provide binary classification, i.e., metal or non-metal waste, and the CNN identifies the class of non-metal waste. A camera is placed in front of the waste conveyor belt, which takes a picture of the waste and classifies it. Upon successful classification, an automatic hand hammer is used to push the waste into the assigned labeled bucket. Experiments were carried out in a real-time environment with image segmentation. The training, testing, and validation accuracy of the purposed model was 0.99% under different training batches with different input features.

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