This article examines how people respond to the presence of a flying robot under various operating conditions using traditional human physiological measures and a novel head movement measurement. A central issue to the integration of flying robotic systems into human-populated environments is how to improve the level of comfort and safety for people around them. Traditional motion control algorithms in robotics tend to focus on the actual safety of collision avoidance. However, people's perceived safety is not necessarily equivalent to the actual safety of the vehicle. Therefore flight control systems must account for people's perception of safety beyond the actual safety of the aerial vehicles in order to allow for successful interaction between humans and the unmanned aerial vehicles (UAVs). Across three experiments participants passively observed quadrotor trajectories in a simulated virtual reality environment. Quadrotor flight characteristics were manipulated in terms of speed, altitude, and audibility to examine their effect on physiological arousal and head motion kinematics. Physiological arousal was greater when the quadrotor was flying with the audio on than off, and at eye-height than overhead, and decreased over repeated exposure. In addition, head acceleration away from the UAVs indicating defensive behavior was stronger for faster speed and audible UAVs. These data suggest head acceleration can serve as a new index specific for measuring perceived safety. Applications intended for human comfort need to consider constraints from specific measures of perceived safety in addition to traditional measures of general physiological arousal.

In this article, we present a preliminary motion planning framework for a cyber-physical system consisting of a human and a flying robot in vicinity. The motion planning of the flying robot takes into account the human’s safety perception. We aim to determine a parametric model for the human’s safety perception based on test data. We use virtual reality as a safe testing environment to collect safety perception data reflected on galvanic skin response (GSR) from the test subjects experiencing a flying robot in their vicinity. The GSR signal contains both meaningful information driven by the interaction with the robot and also disturbances from unknown factors. To address the issue, we use two parametric models to approximate the GSR data: (1) a function of the robot’s position and velocity and (2) a random distribution. Intuitively, we need to choose the more likely model given the data. When GSR is statistically independent of the flying robot, then the random distribution should be selected instead of the function of the robot’s position and velocity. We implement the intuitive idea under the framework of hidden Markov model (HMM) estimation. As a result, the proposed HMM-based model improves the likelihood compared to the Gaussian noise model, which does not make a distinction between relevant and irrelevant samples due to unknown factors. We also present a numerical optimal path planning method that considers the safety perception model while ensuring spatial separation from the obstacle despite the time discretization. Optimal paths generated using the proposed model result in a reasonably safe distance from the human. In contrast, the trajectories generated by the standard regression model with the Gaussian noise assumption, without consideration of unknown factors, have undesirable shapes.

This study explored whether people misremember having seen a physical object when they actually had viewed a virtual one in augmented reality (and vice versa). Participants viewed uniquely shaped objects in a virtual form or a physical, 3D-printed form. A camera mounted behind a computer monitor showed either the physical object or an augmented reality version of it on the display. After viewing the full set of objects, participants viewed photographs of each object (taken from the physical version) and judged whether they had originally seen it as a physical or virtual object. On average, participants correctly identified the object format for 60% of the photographs. When participants were allowed to manipulate the physical or virtual object (using a Leap Motion Controller), accuracy increased to 73%. In both cases, participants were biased to remember the objects as having been virtual.

This paper presents an approach for modeling user's driving-characteristics in a steering task, and determining the parameters of a virtual fixture to assist the user-control on the basis of his/her task-performances. First, we briefly introduce our assistive human-robot interaction (HRI) interface and a virtual fixture as backgrounds related to this research. The designed HRI interface provides assistance by actively constraining the user-control with a virtual fixture. Second, we discuss a way to model a user's driving-characteristics in a steering task. In modeling the driving-characteristics, we use techniques from inverse optimal control (IOC), where known basis functions (speed, steering, and proximities to inner/outer road boundary) are employed to design a cost function. Third, we describe the experimental setup and procedures to obtain user-demonstrated data from human subjects. Utilizing the obtained data sets, we infer the unknown parameter vector by solving inverse optimal control. Afterward, the user's driving-characteristics are expressed in terms of the balances of the inferred parameters, allowing us to find a relationship between the modeled driving-characteristics and task-completion time. Finally, we present a method to set a virtual fixture for a newly given task by predicting the user's task-performances.