Real-time Object Detection in a Collaborative Artificial Intelligence System Simulation

Collaborative Artificial Intelligence Systems (CAISs) are systems wherein robots and humans work in a shared space and the robots are equipped with artificial intelligence or machine learning (ML) components. While humans can reason about simple tasks like seeing and smelling almost instinctively, robots even with ML components struggle with completing these tasks successfully.

In this work, the student explores different aspects of real-time object detection in a collaborative artificial intelligence system simulation, such as the design, development, and implementation of these systems. The student also explores the challenges of developing real-time object detection in a collaborative artificial intelligence system simulation, such as the need for large amounts of data for training, the need for specialized hardware, and the need for specialized software. In furtherance, the thesis aims to also explore the future of real-time object detection in a collaborative artificial intelligence system simulation, such as the potential for these systems to replace human workers in certain industries.