Observation System of Human Behavior Around a Robot Arm Using a Spherical Image View for Safe Human-Robot Collaboration in a Retail Store

2020 ◽  
Vol 2020 (0) ◽  
pp. 2A1-G10
Author(s):  
Tomoki NAGATANI ◽  
Akishige YUGUCHI ◽  
Gustavo Alfonso GARCIA RICARDEZ ◽  
Jun TAKAMATSU ◽  
Tsukasa OGASAWARA
Keyword(s):  
Human Behavior ◽  
Retail Store ◽  
Observation System ◽  
Robot Arm ◽  
Spherical Image ◽  
Human Robot Collaboration

scholarly journals Bilaterally Shared Haptic Perception for Human-Robot Collaboration in Grasping Operation

2021 ◽  
Vol 33 (5) ◽  
pp. 1104-1116
Author(s):  
Yoshihiro Tanaka ◽  
Shogo Shiraki ◽  
Kazuki Katayama ◽  
Kouta Minamizawa ◽  
Domenico Prattichizzo ◽  
...  
Keyword(s):  
Haptic Perception ◽  
Tactile Feedback ◽  
Tactile Display ◽  
Just Noticeable Difference ◽  
Robot Arm ◽  
Collaborative Task ◽  
Grasping Force ◽  
The Stability ◽  
Tactile Sensations ◽  
Human Robot Collaboration

Tactile sensations are crucial for achieving precise operations. A haptic connection between a human operator and a robot has the potential to promote smooth human-robot collaboration (HRC). In this study, we assemble a bilaterally shared haptic system for grasping operations, such as both hands of humans using a bottle cap-opening task. A robot arm controls the grasping force according to the tactile information from the human that opens the cap with a finger-attached acceleration sensor. Then, the grasping force of the robot arm is fed back to the human using a wearable squeezing display. Three experiments are conducted: measurement of the just noticeable difference in the tactile display, a collaborative task with different bottles under two conditions, with and without tactile feedback, including psychological evaluations using a questionnaire, and a collaborative task under an explicit strategy. The results obtained showed that the tactile feedback provided the confidence that the cooperative robot was adjusting its action and improved the stability of the task with the explicit strategy. The results indicate the effectiveness of the tactile feedback and the requirement for an explicit strategy of operators, providing insight into the design of an HRC with bilaterally shared haptic perception.

scholarly journals An Augmented Discrete-Time Approach for Human-Robot Collaboration

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Peidong Liang ◽  
Lianzheng Ge ◽  
Yihuan Liu ◽  
Lijun Zhao ◽  
Ruifeng Li ◽  
...  
Keyword(s):  
Discrete Time ◽  
Human Motion ◽  
Stiffness Index ◽  
Robot Dynamics ◽  
Robot Arm ◽  
Human Arm ◽  
Human Kinematics ◽  
Human Motor ◽  
New Generation ◽  
Human Robot Collaboration

Human-robot collaboration (HRC) is a key feature to distinguish the new generation of robots from conventional robots. Relevant HRC topics have been extensively investigated recently in academic institutes and companies to improve human and robot interactive performance. Generally, human motor control regulates human motion adaptively to the external environment with safety, compliance, stability, and efficiency. Inspired by this, we propose an augmented approach to make a robot understand human motion behaviors based on human kinematics and human postural impedance adaptation. Human kinematics is identified by geometry kinematics approach to map human arm configuration as well as stiffness index controlled by hand gesture to anthropomorphic arm. While human arm postural stiffness is estimated and calibrated within robot empirical stability region, human motion is captured by employing a geometry vector approach based on Kinect. A biomimetic controller in discrete-time is employed to make Baxter robot arm imitate human arm behaviors based on Baxter robot dynamics. An object moving task is implemented to validate the performance of proposed methods based on Baxter robot simulator. Results show that the proposed approach to HRC is intuitive, stable, efficient, and compliant, which may have various applications in human-robot collaboration scenarios.

scholarly journals Investigation of Human Behavior in Emergent Evacuation from an Underground Retail Store

2014 ◽  
Vol 71 ◽  
pp. 350-356 ◽  
Author(s):  
F.Z. Huo ◽  
W.G. Song ◽  
X.D. Liu ◽  
Z.G. Jiang ◽  
K.M. Liew
Keyword(s):  
Human Behavior ◽  
Retail Store

scholarly journals Do Speed and Proximity Affect Human-Robot Collaboration with an Industrial Robot Arm?

2022 ◽  
Author(s):  
Matthew Story ◽  
Phil Webb ◽  
Sarah R. Fletcher ◽  
Gilbert Tang ◽  
Cyril Jaksic ◽  
...  
Keyword(s):  
Significant Interaction ◽  
Industrial Robot ◽  
Collaborative Work ◽  
Industrial Robots ◽  
Robot Arm ◽  
High Productivity ◽  
The Impact ◽  
Further Development ◽  
Current Guidelines ◽  
Human Robot Collaboration

AbstractCurrent guidelines for Human-Robot Collaboration (HRC) allow a person to be within the working area of an industrial robot arm whilst maintaining their physical safety. However, research into increasing automation and social robotics have shown that attributes in the robot, such as speed and proximity setting, can influence a person’s workload and trust. Despite this, studies into how an industrial robot arm’s attributes affect a person during HRC are limited and require further development. Therefore, a study was proposed to assess the impact of robot’s speed and proximity setting on a person’s workload and trust during an HRC task. Eighty-three participants from Cranfield University and the ASK Centre, BAE Systems Samlesbury, completed a task in collaboration with a UR5 industrial robot arm running at different speeds and proximity settings, workload and trust were measured after each run. Workload was found to be positively related to speed but not significantly related to proximity setting. Significant interaction was not found for trust with speed or proximity setting. This study showed that even when operating within current safety guidelines, an industrial robot can affect a person’s workload. The lack of significant interaction with trust was attributed to the robot’s relatively small size and high success rate, and therefore may have an influence in larger industrial robots. As workload and trust can have a significant impact on a person’s performance and satisfaction, it is key to understand this relationship early in the development and design of collaborative work cells to ensure safe and high productivity.

scholarly journals HARMONIC: A multimodal dataset of assistive human–robot collaboration

2021 ◽  
pp. 027836492110506
Author(s):  
Benjamin A. Newman ◽  
Reuben M. Aronson ◽  
Siddhartha S. Srinivasa ◽  
Kris Kitani ◽  
Henny Admoni
Keyword(s):  
Data Streams ◽  
Eye Gaze ◽  
Mental States ◽  
Environmental Data ◽  
Direct Result ◽  
Robot Arm ◽  
Human Interactions ◽  
Shared Autonomy ◽  
Third Person ◽  
Human Robot Collaboration

We present the Human And Robot Multimodal Observations of Natural Interactive Collaboration (HARMONIC) dataset. This is a large multimodal dataset of human interactions with a robotic arm in a shared autonomy setting designed to imitate assistive eating. The dataset provides human, robot, and environmental data views of 24 different people engaged in an assistive eating task with a 6-degree-of-freedom (6-DOF) robot arm. From each participant, we recorded video of both eyes, egocentric video from a head-mounted camera, joystick commands, electromyography from the forearm used to operate the joystick, third-person stereo video, and the joint positions of the 6-DOF robot arm. Also included are several features that come as a direct result of these recordings, such as eye gaze projected onto the egocentric video, body pose, hand pose, and facial keypoints. These data streams were collected specifically because they have been shown to be closely related to human mental states and intention. This dataset could be of interest to researchers studying intention prediction, human mental state modeling, and shared autonomy. Data streams are provided in a variety of formats such as video and human-readable CSV and YAML files.

Review of Psychology: An introduction to human behavior.

1975 ◽  
Vol 20 (1) ◽  
pp. 75-75
Author(s):  
RALPH H. TURNER
Keyword(s):  
Human Behavior
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