VR Environment for the Study of Collocated Interaction Between Small UAVs and Humans

Widdowson, Christopher; Yoon, Hyung‐Jin; Cichella, Venanzio; Wang, Ranxiao Frances; Hovakimyan, Naira

doi:10.1007/978-3-319-60384-1_33

Cited by 5 publications

(10 citation statements)

References 7 publications

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“…2) Calculate Q(θ , θ old ): Using the poseterior calculated in (9) and (12) and the likelihood in (5), Q(θ , θ old ) in Algorithm 1 is calculated by expanding the log term: p(z n,i , w n,k |x, y, θ old ) log p(w n |φ k )p(y n |z n,i , w n,k , x n , θ ), (13) where z n,i denotes the event {z n = i}, w n,k denotes the event {w n = k}, A i, j is the (i, j) element of the matrix A, and p(y n |z n,i , w n,k , x n , θ ) is calculated using the model equation (2) as follows p(y n |z n,i , w n,k , x n , θ ) = N (y n − f β (x n )|µ, σ 2 ), if i = 1 N (y n |µ k , σ 2 k ), if i = 2.…”

Section: Forward-backward Algorithm [26]mentioning

confidence: 99%

“…3) Find the maximizer θ * : As seen in (13) and (14), each term has a distinct set of parameters. Hence, we can determine the maximizer for each term independently from the other terms.…”

Section: Forward-backward Algorithm [26]mentioning

confidence: 99%

“…The maximizer for the last term in (13) is calculated using the model equation (2). Let L denote the last term in (13). Using (14), L is written as follows: .…”

Section: Forward-backward Algorithm [26]mentioning

confidence: 99%

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A Path Planning Framework for a Flying Robot in Close Proximity of Humans

Yoon

Widdowson

Marinho

et al. 2019

2019 American Control Conference (ACC)

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We present a path planning framework that takes into account the human's safety perception in the presence of a flying robot. The framework addresses two objectives: (i) estimation of the uncertain parameters of the proposed safety perception model based on test data collected using Virtual Reality (VR) testbed, and (ii) offline optimal control computation using the estimated safety perception model. Due to the unknown factors in the human tests data, it is not suitable to use standard regression techniques that minimize the mean squared error (MSE). We propose to use a Hidden Markov model (HMM) approach where human's attention is considered as a hidden state to infer whether the data samples are relevant to learn the safety perception model. The HMM approach improved log-likelihood over the standard least squares solution. For path planning, we use Bernstein polynomials for discretization, as the resulting path remains within the convex hull of the control points, providing guarantees for deconfliction with obstacles at low computational cost. An example of optimal trajectory generation using the learned human model is presented. The optimal trajectory generated using the proposed model results in reasonable safety distance from the human. In contrast, the paths generated using the standard regression model have undesirable shapes due to overfitting. The example demonstrates that the HMM approach has robustness to the unknown factors compared to the standard MSE model. 1 The coefficient of determination R 2 is a performance metric used in regression task. R 2 = 1 − ∑i(yi−ŷi) 2 ∑i(yi−ȳ) 2 , whereȳ denotes empirical mean andŷ i denotes the prediction of the target variable given a feature variable x i .

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Section: Forward-backward Algorithm [26]mentioning

confidence: 99%

“…3) Find the maximizer θ * : As seen in (13) and (14), each term has a distinct set of parameters. Hence, we can determine the maximizer for each term independently from the other terms.…”

Section: Forward-backward Algorithm [26]mentioning

confidence: 99%

See 1 more Smart Citation

A Path Planning Framework for a Flying Robot in Close Proximity of Humans

Yoon

Widdowson

Marinho

et al. 2019

2019 American Control Conference (ACC)

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“…Additionally, fueled by recently proposed specifications (ISO/TS 15066:2016-Robots and robotic devices-Collaborative robots), it will likely be essential in the near future for the realization of the "intimate collaboration" or the "close proximity interaction" paradigms described by [2,3], respectively. In particular, studies have shown recently that human motion prediction technologies have the potential to improve the efficiency of the team and the perception of safety and comfort by the user in different settings in which persons and robots share the same physical workspace [4,5]. Likewise, human motion prediction may also be relevant in assistive devices (active prosthesis/orthosis/exoskeletons) to switch between different locomotion modes [6,7], by changing the control algorithms or by modifying the mechanical properties of the assistive devices [8].…”

Section: Introductionmentioning

confidence: 99%

“…Sci. 2019, 9, x FOR PEER REVIEW 2 of 16 and comfort by the user in different settings in which persons and robots share the same physical workspace [4,5]. Likewise, human motion prediction may also be relevant in assistive devices (active prosthesis/orthosis/exoskeletons) to switch between different locomotion modes [6,7], by changing the control algorithms or by modifying the mechanical properties of the assistive devices [8].…”

Section: Introductionmentioning

confidence: 99%

Walking Turn Prediction from Upper Body Kinematics: A Systematic Review with Implications for Human-Robot Interaction

López

Álvarez

2019

Applied Sciences

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Prediction of walking turns allows to improve human factors such as comfort and perceived safety in human-robot interaction. The current state-of-the-art suggests that upper body kinematics can be used for that purpose and contains evidence about the reliability and the quantitative anticipation that can be expected from different variables. However, the experimental methodology has not been consistent throughout the different works and the related data has not always been given in an explicit form, with different studies containing partial, complementary or even contradictory results. In this paper, with the purpose of providing a uniform view of the topic that can trigger new developments in the field, we performed a systematic review of the relevant literature addressing three main questions: (i) Which upper body kinematic variables permit to anticipate a walking turn? (ii) How long in advance can we anticipate the turn from them? (iii) What is the expected contribution of walking turn prediction systems from upper body kinematics for human-robot interaction? We have found that head yaw was the most reliable kinematical variable from the upper body to predict walking turns about 200ms. Trunk roll anticipates walking turns by a similar amount of time, but with less reliability. Both approaches may benefit human-robot interaction in close proximity, helping the robot to exhibit appropriate proxemic behavior interacting at intimate, personal or social distances. From the point of view of safety, they have to be considered with caution. Trunk yaw is not valid to anticipate turns. Gaze Yaw seems to be the earliest predictor, although existing evidence is still inconclusive.

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A Taxonomy of Factors Influencing Perceived Safety in Human–Robot Interaction

Akalın

Kiselev

Kristoffersson

et al. 2023

Int J of Soc Robotics

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Safety is a fundamental prerequisite that must be addressed before any interaction of robots with humans. Safety has been generally understood and studied as the physical safety of robots in human–robot interaction, whereas how humans perceive these robots has received less attention. Physical safety is a necessary condition for safe human–robot interaction. However, it is not a sufficient condition. A robot that is safe by hardware and software design can still be perceived as unsafe. This article focuses on perceived safety in human–robot interaction. We identified six factors that are closely related to perceived safety based on the literature and the insights obtained from our user studies. The identified factors are the context of robot use, comfort, experience and familiarity with robots, trust, the sense of control over the interaction, and transparent and predictable robot actions. We then made a literature review to identify the robot-related factors that influence perceived safety. Based the literature, we propose a taxonomy which includes human-related and robot-related factors. These factors can help researchers to quantify perceived safety of humans during their interactions with robots. The quantification of perceived safety can yield computational models that would allow mitigating psychological harm.

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VR Environment for the Study of Collocated Interaction Between Small UAVs and Humans

Cited by 5 publications

References 7 publications

A Path Planning Framework for a Flying Robot in Close Proximity of Humans

A Path Planning Framework for a Flying Robot in Close Proximity of Humans

Walking Turn Prediction from Upper Body Kinematics: A Systematic Review with Implications for Human-Robot Interaction

A Taxonomy of Factors Influencing Perceived Safety in Human–Robot Interaction

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