Neural Networks for Autonomous Path-Following with an Omnidirectional Image Sensor

2002 ◽  
Vol 11 (1) ◽  
pp. 45-52 ◽  
Author(s):  
A. Rizzi ◽  
R. Cassinis ◽  
N. Serana
Keyword(s):  
Neural Networks ◽  
Path Following ◽  
Image Sensor ◽  
Omnidirectional Image

scholarly journals Intrinsic motivation and episodic memories for robot exploration of high-dimensional sensory spaces

2020 ◽  
pp. 105971232092291
Author(s):  
Guido Schillaci ◽  
Antonio Pico Villalpando ◽  
Verena V Hafner ◽  
Peter Hanappe ◽  
David Colliaux ◽  
...  
Keyword(s):  
Neural Networks ◽  
Episodic Memory ◽  
Intrinsic Motivation ◽  
Computational Models ◽  
Deep Neural Networks ◽  
Image Sensor ◽  
Forward Kinematics ◽  
High Dimensional ◽  
Episodic Memories ◽  
Low Dimensional

This work presents an architecture that generates curiosity-driven goal-directed exploration behaviours for an image sensor of a microfarming robot. A combination of deep neural networks for offline unsupervised learning of low-dimensional features from images and of online learning of shallow neural networks representing the inverse and forward kinematics of the system have been used. The artificial curiosity system assigns interest values to a set of pre-defined goals and drives the exploration towards those that are expected to maximise the learning progress. We propose the integration of an episodic memory in intrinsic motivation systems to face catastrophic forgetting issues, typically experienced when performing online updates of artificial neural networks. Our results show that adopting an episodic memory system not only prevents the computational models from quickly forgetting knowledge that has been previously acquired but also provides new avenues for modulating the balance between plasticity and stability of the models.

scholarly journals Identification of Road-Surface Type Using Deep Neural Networks for Friction Coefficient Estimation

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 612 ◽  
Author(s):  
Eldar Šabanovič ◽  
Vidas Žuraulis ◽  
Olegas Prentkovskis ◽  
Viktor Skrickij
Keyword(s):  
Neural Networks ◽  
Friction Coefficient ◽  
Deep Neural Networks ◽  
Image Sensor ◽  
Video Data ◽  
Road Surface ◽  
Driver Assistance Systems ◽  
Surface Type ◽  
Braking Performance ◽  
The Road

Nowadays, vehicles have advanced driver-assistance systems which help to improve vehicle safety and save the lives of drivers, passengers and pedestrians. Identification of the road-surface type and condition in real time using a video image sensor, can increase the effectiveness of such systems significantly, especially when adapting it for braking and stability-related solutions. This paper contributes to the development of the new efficient engineering solution aimed at improving vehicle dynamics control via the anti-lock braking system (ABS) by estimating friction coefficient using video data. The experimental research on three different road surface types in dry and wet conditions has been carried out and braking performance was established with a car mathematical model (MM). Testing of a deep neural networks (DNN)-based road-surface and conditions classification algorithm revealed that this is the most promising approach for this task. The research has shown that the proposed solution increases the performance of ABS with a rule-based control strategy.

scholarly journals GROWTH IDENTIFICATION OF TOMATO PLANTS USING TENSOR FLOW

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Bharath Kumar G, ◽  
Hemanth V ◽  
Balaji K.P. ◽  
Makinani Gowri Shankar M ◽  
Tupili Sangeetha ◽  
...  
Keyword(s):  
Neural Networks ◽  
Tomato Plant ◽  
Image Sensor ◽  
Project Work ◽  
Growth Stages ◽  
Growth Monitoring ◽  
Tomato Plants ◽  
Vertical Farming ◽  
Effective Growth ◽  
Plant Farming

Vertical Farming is making the necessary daily vegetables available in a minimal space with effective growth. Tomato plant farming is one of it. In tomato plant farming growth monitoring is a key issue which require man-work and experience. In this project work, an attempt has been made to Analyse and Design for a growth identification of tomato plants. This project work involves planning, analysis, designs, and many more to identify growth stages. Vertical Farming is making the necessary daily vegetables available in a minimal space with effective growth. Tomato plant farming is one of it. In tomato plant farming growth monitoring is a key issue which require man-work and experience. To make it effective we are intruding Neural Networks for growth identification for Tomato plant which not require man-work. Without the involvement of man work, we track the plant growth with Neural Networks. And also, we are doing plant stage detection it shows the entire information of that plant such as temperature, quantity of water needed, etc. With the help of an IOT device all the data are stored in a database. We use raspberry pie IOT device and image sensor to store all the data to the database created.

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