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.

We have built a virtual-reality simulator to design quadrotor robots that increase the independence of elderly persons living alone. The simulator lets us measure under what conditions a robot makes someone feel intrigued, or alarmed, or unconcerned, extending to nonhumanoids the studies of emotion that have been done for robots with facial expressions. This better model of how the virtual robot is perceived lets us agilely improve its appearance and behavior, containing the combinatorial explosion of parameters to study in costlier real-world experiments. The residence and the robot are rendered with Unity, and viewed with an Oculus Rift. Audio corresponding to flight maneuvers is resynthesized from recordings of an actual quadrotor. Flight dynamics using L1 adaptive control run in MATLAB and Simulink. Through menus operated by a Leap Motion hand tracker, the human subject commands the quadrotor to fly to various rooms (Unity waypoints), along precomputed paths that avoid collisions. The flight path is varied through a mathematical mapping to Laban Motion Factors (e.g., directness, sustainedness, lightness, and boundness) to provoke a range of responses. The subject's emotion is estimated from heart rate, skin conductance, and head tilt from the Rift's IMU (which is a quick metric for discomfort level).