File(s) not publicly available
Improved deep neural network object tracking system for applications in home robotics
chapterposted on 2023-02-23, 04:43 authored by B A Erol, A Majumdar, J Lwowski, P Benavidez, P Rad, M Jamshidi
Robotic navigation in GPS-denied environments requires case specific approaches for controlling a mobile robot to any desired destinations. In general, a nominal path is created in an environment described by a set of distinct objects, in other words such obstacles and landmarks. Intelligent voice assistants or digital assistance devices are increasing their importance in today’s smart home. Especially, by the help of fast-growing Internet of Things (IoT) applications. These devices are amassing an ever-growing list of features such as controlling states of connected smart devices, recording tasks, and responding to queries. Assistive robots are the perfect complement to smart voice assistants for providing physical manipulation. A request made by a person can be assigned to the assistive robot by the voice assistant. In this chapter, a new approach for autonomous navigation is presented using pattern recognition and machine learning techniques such as Convolutional Neural Networks to identify markers or objects from images and videos. Computational intelligence techniques are implemented along with Robot Operating System and object positioning to navigate towards these objects and markers by using RGB-depth camera. Multiple potential matching objects detected by the robot with deep neural network object detectors will be displayed on a screen installed on the assistive robot to improve and evaluate Human-Robot Interaction (HRI).