Influence of the Size Factor of a Mobile Robot Moving Toward a Human on Subjective Acceptable Distance

Hiroi, Yutaka; Ito, Akinori

doi:10.5772/26512

Cited by 15 publications

(8 citation statements)

References 20 publications

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“…For a palm-size "flying IoT node", HDI is a first-class use case that can enhance the user experience (e.g., increased safety) thanks to the small size of the node [21]. In this context, our work addresses the pose estimation of a user from low-resolution images, enabling effective HDI.…”

Section: Related Workmentioning

confidence: 99%

Fully Onboard AI-Powered Human-Drone Pose Estimation on Ultralow-Power Autonomous Flying Nano-UAVs

Palossi

Zimmerman

Burrello

et al. 2022

IEEE Internet Things J.

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Many emerging applications of nano-sized unmanned aerial vehicles (UAVs), with a few cm 2 form-factor, revolve around safely interacting with humans in complex scenarios, for example, monitoring their activities or looking after people needing care. Such sophisticated autonomous functionality must be achieved while dealing with severe constraints in payload, battery, and power budget (∼100 mW). In this work, we attack a complex task going from perception to control: to estimate and maintain the nano-UAV's relative 3D pose with respect to a person while they freely move in the environment -a task that, to the best of our knowledge, has never previously been targeted with fully onboard computation on a nano-sized UAV. Our approach is centered around a novel vision-based deep neural network (DNN), called PULP-Frontnet, designed for deployment on top of a parallel ultra-low-power (PULP) processor aboard a nano-UAV. We present a vertically integrated approach starting from the DNN model design, training, and dataset augmentation down to 8-bit quantization and deployment in-field. PULP-Frontnet can operate in real-time (up to 135 frame/s), consuming less than 87 mW for processing at peak throughput and down to 0.43 mJ/frame in the most energy-efficient operating point. Field experiments demonstrate a closed-loop top-notch autonomous navigation capability, with a tiny 27-grams Crazyflie 2.1 nano-UAV. Compared against an ideal sensing setup, onboard pose inference yields excellent drone behavior in terms of median absolute errors, such as positional (onboard: 41 cm, ideal: 26 cm) and angular (onboard: 3.7 • , ideal: 4.1 • ). We publicly release videos and the source code of our work.

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Section: Related Workmentioning

confidence: 99%

Fully Onboard AI-Powered Human-Drone Pose Estimation on Ultralow-Power Autonomous Flying Nano-UAVs

Palossi

Zimmerman

Burrello

et al. 2022

IEEE Internet Things J.

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“…The AIA measure assesses concepts related to AI-related anxiety such as computer anxiety and robot anxiety, and has been utilized in several investigations (e.g. Beckers & Schmidt, 2001;Bolliger & Halupa, 2012;Chu & Spires, 1991;Haring, Mougenot, Ono, & Watanabe, 2014;Heinssen et al, 1987;Hiroi & Ito, 2011;Johnson & Verdicchio, 2017;Nomura et al, 2006;Nomura, Kanda, Suzuki, & Kato, 2008;Ray, Mondada, & Siegwart, 2008;Rosen & Weil, 1995;Wang, 2007;Wu et al, 2014). Computer anxiety is considered to be an irrational fear, apprehension, or phobia that arises following personal interaction with a computer or thinking about using a computer (Herdman, 1983;Howard, 1986;Marcoulides, 1989).…”

Section: Conceptualization Of Aiamentioning

confidence: 99%

Development and validation of an artificial intelligence anxiety scale: an initial application in predicting motivated learning behavior

Wang

2019

Interactive Learning Environments

142

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While increasing productivity and economic growth, the application of artificial intelligence (AI) may ultimately require millions of people around the world to change careers or improve their skills. These disruptive effects contribute to the general public anxiety toward AI development. Despite the rising levels of AI anxiety (AIA) in recent decades, no AI anxiety scale (AIAS) has been developed. Given the limited utility of existing self-report instruments in measuring AIA, the aim of this paper is to develop a standardized tool to measure this phenomenon. Specifically, this paper introduces and defines the construct of AIA, develops a generic AIAS, and discusses the theoretical and practical applications of the instrument. The procedures used to conceptualize the survey, create the measurement items, collect data, and validate the multi-item scale are described. By analyzing data obtained from a sample of 301 respondents, the reliability, criterionrelated validity, content validity, discriminant validity, convergent validity, and nomological validity of the constructs and relationships are fully examined. Overall, this empirically validated instrument advances scholarly knowledge regarding AIA and its associated behaviors.

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“…For this data collection, we measured the distance at which a participant began to feel uncomfortable when a virtual agent reached its hand toward their face. We based our method of measuring the distance on the Stop Distance Paradigm [12], [13], [14], [15], [24], [25], which is the most commonly used method for measuring interpersonal distance in social interactions. In this method, the virtual agent gradually moves its hand closer to the participant's face and the distance at which the participant starts to feel uncomfortable is recorded.…”

Section: Overviewmentioning

confidence: 99%

Modeling of Pre-Touch Reaction Distance for Faces in a Virtual Environment

Mejía

Saito

Kimoto

et al. 2021

Journal of Information Processing

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One of the most common cues in human relations is the reaction when someone approaches for a touch interaction. While the "before touch" distance has been investigated in daily life scenarios, it has not been studied for virtual environments. The measurements of a pre-touch distance in virtual reality can be applied to study social interactions especially for haptic interactions in virtual spaces where virtual agents interact autonomously with human participants. In the first stage of this study, we collected data to define a pre-touch distance when a virtual agent tries to touch the participant's face. On the basis of these results, we then classified participants into two groups based on preferred pre-touch distance: a "Near" group and a "Far" group. Next, we experimentally investigated the relationship between the participant's perception of an avatar's reaction to touch interaction and their preferred pre-touch distance. The results indicated that the participants felt friendliness to the agent who reacts with shorter pre-touch distance. We also found that the participant's pre-touch distance defined their preferences regarding the agent's behavior: those with a shorter pre-touch distance preferred agents with a closer interaction distance, and those with a longer pre-touch distance preferred agents with a longer interaction distance.

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Influence of the Size Factor of a Mobile Robot Moving Toward a Human on Subjective Acceptable Distance

Cited by 15 publications

References 20 publications

Fully Onboard AI-Powered Human-Drone Pose Estimation on Ultralow-Power Autonomous Flying Nano-UAVs

Fully Onboard AI-Powered Human-Drone Pose Estimation on Ultralow-Power Autonomous Flying Nano-UAVs

Development and validation of an artificial intelligence anxiety scale: an initial application in predicting motivated learning behavior

Modeling of Pre-Touch Reaction Distance for Faces in a Virtual Environment

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