Perceived safety in physical human–robot interaction—A survey

Rubagotti, Matteo; Tusseyeva, Inara; Baltabayeva, Sara; Summers, Danna; Sandygulova, Anara

doi:10.1016/j.robot.2022.104047

Cited by 67 publications

(35 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The age range of the experimental participants was concentrated between 20 and 30 years old, and there were no experimental participants of older ages, which also made the psychological safety field model less accurate. To eliminate such an effect, we set parameter k r in the psychological safety field model equation, i.e., Equation (13), in order to accommodate different personalities, ages, and people with different experiences.…”

Section: Discussionmentioning

confidence: 99%

“…To solve this problem, many researchers improve the motion planning algorithm by studying the psychological impact of humans on robot motion in the process of HRC and propose perceived safety and human-aware motion planning algorithms to ensure physical HRC (pHRC) [2,13,14]. For example, Kulic et al [15] and Zoghbi et al [16] used PP (pick and place movement) and RR (reach and retrieve motion) tasks to simulate typical robotic tasks during HRC and captured the physiological responses of 36 participants during this process.…”

Section: Iso/ts 15066 Type Of Cobot Operation Main Means Of Risk Redu...mentioning

confidence: 99%

See 1 more Smart Citation

Cobot Motion Planning Algorithm for Ensuring Human Safety Based on Behavioral Dynamics

Liu

Wang

et al. 2022

Sensors

View full text Add to dashboard Cite

Recently, the safety of workers has gained increasing attention due to the applications of collaborative robots (cobot). However, there is no quantitative research on the impact of cobot behavior on humans’ psychological reactions, and these results are not applied to the cobot motion planning algorithms. Based on the concept of the gravity field, this paper proposes a model of the psychological safety field (PSF), designs a comprehensive experiment on different speeds and minimum distances when approaching the head, chest, and abdomen, and obtains the ordinary surface equation of psychological stress about speed and minimum distance by using data fitting. By combining social rules and PSF models, we improve the robot motion planning algorithm based on behavioral dynamics. The validation experiment results show that our proposed improved robot motion planning algorithm can effectively reduce psychological stress. Eighty-seven point one percent (87.1%) of the experimental participants think that robot motion planned by improved robot motion planning algorithms is more “friendly”, can effectively reduce psychological stress, and is more suitable for human–robot interaction scenarios.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Iso/ts 15066 Type Of Cobot Operation Main Means Of Risk Redu...mentioning

confidence: 99%

Cobot Motion Planning Algorithm for Ensuring Human Safety Based on Behavioral Dynamics

Liu

Wang

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Since an actual user experience is difficult to realize in early stages of development, a live demonstration of the robot's capabilities with the user as a spectator can increase the user's involvement in the robot's introduction and influence the way users build their perception of the robot [41]. Further, previous experience with pets show to increase the minimum distance that a human perceives as comfortable for an approaching robot [42], which is a method to assess users' perceived safety in pHRIs [43]. Thus, pet ownership may increase the familiarization in interactions with non-human agents.…”

Section: User Characteristics Influencing the Acceptance Of Assistive...mentioning

confidence: 99%

Acceptance of Remote Assistive Robots with and without Human-in-the-Loop for Healthcare Applications

Nertinger

Kirschner

Naceri

et al. 2022

Int J of Soc Robotics

View full text Add to dashboard Cite

Assistive social robots aim to facilitate outpatient-care including required safety critical measures. Accepting a robot to perform such measures, e.g., operate in close physical interaction for medical examinations, requires human trust towards the robot. Human-in-the-loop (HIL) applications where the robot is teleoperated by a human expert can help the person to accept even risky tasks performed by a robot. Therefore, the assistive humanoid GARMI was designed to enable HIL applications with varying autonomy. In this study, we use GARMI to understand which tasks in the framework of care may be accepted depending on human socio-demographics and user beliefs as well as the level of robot autonomy. Firstly, we seek to understand the general acceptance of GARMI using the Almere questionnaire. Secondly, we ask adults to rate their willingness to use several functionalities of GARMI. Lastly, we investigate the effect of the introduction method of GARMI on user acceptance. We assemble all relevant factors on acceptance to provide direction in the user-centered design process of assistive robots. The results of 166 participants show that alongside others, trust towards the robot and utilitarian variables such as perceived usefulness are the most influencing factors on the acceptance of GARMI and should be considered for the design of robotic semi-autonomous outpatient-services.

show abstract

“…People detection and tracking are key skill for mobile robots in a lot of applications including Human Robot Interaction (HRI) [1], efficient navigation among people [2] or aspects related to the safety when humans and autonomous robots share the same space [3]. In order to carry out these tasks, several approaches and sensory modalities have been used.…”

Section: Introductionmentioning

confidence: 99%

Detecting and tracking using 2D laser range finders and deep learning

Aguirre

García-Silvente

2022

Neural Comput & Applic

View full text Add to dashboard Cite

Detecting and tracking people using 2D laser rangefinders (LRFs) is challenging due to the features of the human leg motion, high levels of self-occlusion and the existence of objects which are similar to the human legs. Previous approaches use datasets that are manually labelled with support of images of the scenes. We propose a system with a calibrated monocular camera and 2D LRF mounted on a mobile robot in order to generate a dataset of leg patterns through automatic labelling which is valid to achieve a robust and efficient 2D LRF-based people detector and tracker. First, both images and 2D laser data are recorded during the robot navigation in indoor environments. Second, the people detection boxes and keypoints obtained by a deep learning-based object detector are used to locate both people and their legs on the images. The coordinates frame of 2D laser is extrinsically calibrated to the camera coordinates allowing our system to automatically label the leg instances. The automatically labelled dataset is then used to achieve a leg detector by machine learning techniques. To validate the proposal, the leg detector is used to develop a Kalman filter-based people detection and tracking algorithm which is experimentally assessed. The experimentation shows that the proposed system overcomes the Angus Leigh’s detector and tracker which is considered the state of the art on 2D LRF-based people detector and tracker.

show abstract

Perceived safety in physical human–robot interaction—A survey

Cited by 67 publications

References 62 publications

Cobot Motion Planning Algorithm for Ensuring Human Safety Based on Behavioral Dynamics

Cobot Motion Planning Algorithm for Ensuring Human Safety Based on Behavioral Dynamics

Acceptance of Remote Assistive Robots with and without Human-in-the-Loop for Healthcare Applications

Detecting and tracking using 2D laser range finders and deep learning

Contact Info

Product

Resources

About