Swarm Robotic Machine Learning Systems: Using Cloud Computing and Computer Aided Vision
Primary Faculty Mentor’s Name
Biswanath Samanta
Proposal Track
Student
Session Format
Paper Presentation
Abstract
The main objective of the proposed research is to develop a machine learning system for robot swarms using cloud computing and computer aided vision. Ideally, the process of acquiring, processing, analyzing, understanding and conversion of an image into numerical or symbolic data models that can be processed by a computer can be best described as computer vision. To humans, the understanding of the world around us depends on the cognitive ability to process the image and environmental data that is seen daily. Motion, balance, learning ability and correct performance of general daily activities are based on the ability of humans to properly process the data of various images that are seen by the human eye. Various research projects in computer vision aim to develop parallel mathematical processes for the recovery and understanding of three dimensional shapes and images. Today, with the aid of computerized image processing, we can track faces, motion of an object across complex backgrounds, colors, and other environmental data. However, even with all these advancements, making a computer understand and learn from the environment like a 2 year old child seems to be almost impossible. Despite this perception, the possibility of training artificially intelligent systems by a swarm robotic supervised learning approach that can save learned tasks on a cloud server for further processing, sharing and accessibility could create a new dynamics to human machine interaction. For effective machine learning, the methods employed in this research theorize an artificial intelligent system to have three states of operation “Normal Mode”, “Learning Mode” and “Action Mode”. The robotic system is designed to learn and adapt to the environment by object identification methods from image data combined with other geolocation and motion sensors. This research offers a possibility for the adaptation of machine learning robotic algorithms in assistive technologies for people with disabilities, personal robotics, medicine, military, as well as in manufacturing and construction industries.
Keywords
Robotics, Artificial intelligence, Swarm robots, Cloud robotics, Robots, Computer vision, Machine learning, Supervised learning
Award Consideration
1
Location
Room 2908
Presentation Year
2014
Start Date
11-15-2014 1:45 PM
End Date
11-15-2014 2:45 PM
Publication Type and Release Option
Presentation (Open Access)
Recommended Citation
Okechukwu, Ugochukwu Francis, "Swarm Robotic Machine Learning Systems: Using Cloud Computing and Computer Aided Vision" (2014). Georgia Undergraduate Research Conference (2014-2015). 89.
https://digitalcommons.georgiasouthern.edu/gurc/2014/2014/89
Swarm Robotic Machine Learning Systems: Using Cloud Computing and Computer Aided Vision
Room 2908
The main objective of the proposed research is to develop a machine learning system for robot swarms using cloud computing and computer aided vision. Ideally, the process of acquiring, processing, analyzing, understanding and conversion of an image into numerical or symbolic data models that can be processed by a computer can be best described as computer vision. To humans, the understanding of the world around us depends on the cognitive ability to process the image and environmental data that is seen daily. Motion, balance, learning ability and correct performance of general daily activities are based on the ability of humans to properly process the data of various images that are seen by the human eye. Various research projects in computer vision aim to develop parallel mathematical processes for the recovery and understanding of three dimensional shapes and images. Today, with the aid of computerized image processing, we can track faces, motion of an object across complex backgrounds, colors, and other environmental data. However, even with all these advancements, making a computer understand and learn from the environment like a 2 year old child seems to be almost impossible. Despite this perception, the possibility of training artificially intelligent systems by a swarm robotic supervised learning approach that can save learned tasks on a cloud server for further processing, sharing and accessibility could create a new dynamics to human machine interaction. For effective machine learning, the methods employed in this research theorize an artificial intelligent system to have three states of operation “Normal Mode”, “Learning Mode” and “Action Mode”. The robotic system is designed to learn and adapt to the environment by object identification methods from image data combined with other geolocation and motion sensors. This research offers a possibility for the adaptation of machine learning robotic algorithms in assistive technologies for people with disabilities, personal robotics, medicine, military, as well as in manufacturing and construction industries.
