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A 6D human pose estimation method is studied to assist autonomous UAV control in human environments. As autonomous robots/UAVs become increasingly prevalent in the future workspace, autonomous robots must detect/estimate human movement and predict their trajectory to plan a safe motion path. Our method utilize a deep Convolutional Neural Network to calculate a 3D torso bounding box to determine the location and orientation of human objects. The training uses a loss function that includes both 3D angle and translation errors. The trained model delivers <10-degree angular error and outperforms a reference method based on RSN. 

Digital twin is a vital enabling technology for smart manufacturing in the era of Industry 4.0. Digital twin effectively replicates its physical asset enabling easy visualization, smart decision-making and cognitive capability in the system. In this paper, a framework of dynamic data driven digital twin for complex engineering products was proposed. To illustrate the proposed framework, an example of health management on aircraft engines was studied. This framework models the digital twin by extracting information from the various sensors and Industry Internet of Things (IIoT) monitoring the remaining useful life (RUL) of an engine in both cyber and physical domains. Then, with sensor measurements selected from linear degradation models, a long short-term memory (LSTM) neural network is proposed to dynamically update the digital twin, which can estimate the most up-to-date RUL of the physical aircraft engine. Through comparison with other machine learning algorithms, including similarity based linear regression and feed forward neural network, on RUL modelling, this LSTM based dynamical data driven digital twin provides a promising tool to accurately replicate the health status of aircraft engines. This digital twin based RUL technique can also be extended for health management and remote operation of manufacturing systems. 

It requires a lot of hands-on experience to learn how to operate a computer numerical control (CNC) mill. Virtual Reality (VR) can serve as a way to teach how to properly operate it. The goal of this research is to create a virtual CNC mill that can provide interactive training for students. The Unity software was used for this goal. Unity is a game development engine used to produce video games, utility software, and more. The functionality of the CNC simulation was created with C# scripting. The visual representation of the CNC mill was built through 3D modeling, and then transferred into FBX 3D models which are compatible with Unity. The virtual machine is able to take G-code inputs via either an input field or a text file. For the current version, it can simulate G00, G01, and G02/G03 commands and is able to cut sample workpieces per input. It also can save the coordinates of the cutting path via json file and use a python script to view its movement on a line graph. The virtual machine emulates the input commands very similar to the actual machine. This can serve as a good learning tool for CNC machine operators. The virtual machine created through Unity is proof that a digital simulation is achievable for the CNC machine. Future research will include implementing and incorporating mixed reality. Incorporating these kinds of technology will help a more immersive learning experience for students.