We provide an experimental evaluation of a wearable augmented reality (AR) system we have developed for human-robot teams working on tasks requiring collaboration in shared physical workspace. Recent advances in AR technology have facilitated the development of more intuitive user interfaces for many human-robot interaction applications. While it has been anticipated that AR can provided a more intuitive interface to robot assistants helping human workers in various manufacturing scenarios, existing studies in robotics have been largely limited to teleoperation and programming. Industry 5.0 envisions cooperation between human and robot working in teams. Indeed, there exist many industrial task that can benefit from human-robot collaboration. A prime example is high-value composite manufacturing. Working with our industry partner towards this example application, we evaluated our AR interface design for shared physical workspace collaboration in human-robot teams. We conducted a multi-dimensional analysis of our interface using establish metrics. Results from our user study (n=26) show that subjectively, the AR interface feels more novel and a standard joystick interface feels more dependable to users. However, the AR interface was found to reduce physical demand and task completion time, while increasing robot utilization. Furthermore, user’s freedom of choice to collaborate with the robot may also affect the perceived usability of the system.
A method for the setting and evaluation of TCAS (Traffic Alert and Collision Avoidance System) alerting threshold is proposed in this paper. Firstly, by analyzing the error and uncertainty of the flight path, a stochastic differential equation is introduced to model the random aircraft motion. Then a pilot model according to ICAO standards is given to simulate the TCAS alert event. And then five kinds of alarm outputs are summed up by analyzing the principle of TCAS alerting function. Finally, the evaluation system with multiple variables is established on System Operating Characteristic (SOC) curve. The validation of the Encounter model is performed under different parameters. Through the simulation under different threshold values, the optimal alerting threshold values are obtained. The experiments show the rationality of the TCAS threshold and verify the validity of our threshold-setting method.
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