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Human–robot collaboration has emerged as a prominent research topic in recent years. To enhance collaboration and ensure safety between humans and robots, researchers employ a variety of methods. One such method is physiological computing, which aims to estimate a human’s psycho-physiological state by measuring various physiological signals such as galvanic skin response (GSR), electrocardiograph (ECG), heart rate variability (HRV), and electroencephalogram (EEG). This information is then used to provide feedback to the robot. In this paper, we present the latest state-of-the-art methods in physiological computing for human–robot collaboration. Our goal is to provide a comprehensive guide for new researchers to understand the commonly used physiological signals, data collection methods, and data labeling techniques. Additionally, we have categorized and tabulated relevant research to further aid in understanding this area of study. 

While human safety is always a concern in an environment with human-robot collaboration, this concern magnifies when it is the human-robot work-space that overlaps. This overlap creates potential for collision which would reduce the safety of the system. Fear of such a collision could reduce the productivity of the system. This apprehensiveness is referred to as the perceived safety of the robot by the human. Therefore, we designed a within-subject human-robot collaboration experiment where a human and a robot work together in an assembling task. In order to evaluate the perceived safety during this HRC task, we collected subjective data by means of a questionnaire through two methods: during and after trial. The collected data was analyzed using non-parametric methods and these statistical tests were conducted: Friedman and Wilcoxon. The most clear relationship was found when changing only sensitivity of the robot or all three behaviors of velocity, trajectory, and sensitivity. There is a positive moderate linear relationship between the average safety of the during trial data and the after trial data. 

Interfacing between robots and humans has come a long way in the past few years, and new methods for smart, robust interaction are needed. Typically, a technician has to program a routine for a robot in order for the robot to be useful. This puts up a significant barrier to entry into the field of automating tasks using robots—not only is a technician and a computer required, but the robot is not adaptive to the immediate needs of the user. The robot is only capable of executing a pre-determined task and for any change to be made the entire system needs to be paused. This project seeks to bridge the gap between user and robot interface, creating an easy-to-use system that allows for adaptive robot control. Using a combination of computer vision and a monocular camera system and integrated LiDAR sensor on an iPhone, gesture recognition and pose estimation was conducted within an independent system to control the Baxter humanoid robot. The gathered data was sent wirelessly to the robot to be interpreted and then replay actions performed by the user. 

An emerging technology for indoor localization is ultra wide-band, also known as UWB. UWB has been making waves as a system that can be both secure and function as an “indoor GPS”. The proliferation of UWB is underway and soon it will be as ubiquitous as Bluetooth orWi-Fi.With this in mind, the benchmarking of the DWM3000EVB module in an Ultra Wideband Real Time Locating System is the goal of this research. The UWB RTLS created is a three anchor - one tag system that can calculate position just under 100 Hz and has an average accuracy of 5 centimeters.