Nowadays, virtual personalized assistants (VPAs) exist everywhere around us. For example, Siri or android VPAs exist on every smartphone. More and more people are getting household Virtual Assistants, such as Amazon Alexa, Google Assistant, and Microsoft's Cortana. If the virtual assistants can connect with objects which have physical functions like an actual robot, they will be able to provide better services and more functions for humans. In this project, a mobile robot, Fetch, is connected with the Echo dot from Amazon. This connection allows us to control the robot by voice command. You can ask the robot to move in a given direction or track and follow a certain object. In order to let the robot learn how to predict the position of the target when the target is lost, a map is built as an influence factor. Since a designed algorithm of target position prediction is difficult to implement, we opted to use a machine learning method instead. Therefore, a machine learning algorithm was tested on the following model.

With the rapid development of computers and technology, virtual assistants (VA) are becoming more and more common and intelligent. However, virtual assistants, such as Apple's Siri, Amazon's Alexa, and Google Assistant, do not currently have any physical functions. As an important part of the internet of things (IoT), the field of robotics has become a new trend in the usage of VA. In this project, a mobile robot, Fetch, is connected with the Amazon Echo Dot through the Amazon web service (AWS) and a local robot operation system (ROS) bridge server. We demonstrated that the robot could be controlled by voice commands through an Amazon Alexa. Given certain commands, Fetch was able to move in a desired direction as well as track and follow a target object. The follow model was also learned by Neural Network training, which allows for the target position to be predicted in future maps.

Master of Science