Anxiety detecting robotic system – towards implicit human-robot collaboration

Robotica ◽  
2004 ◽  
Vol 22 (1) ◽  
pp. 85-95 ◽  
Author(s):  
Pramila Rani ◽  
Nilanjan Sarkar ◽  
Craig A. Smith ◽  
Leslie D. Kirby
Keyword(s):  
Mobile Robot ◽  
Affective State ◽  
Robotic System ◽  
Control Architecture ◽  
Affect Recognition ◽  
Implicit Communication ◽  
Physiological Indices ◽  
Robot Cooperation ◽  
Human Robot Collaboration ◽  
Qualitative Analyses

A novel affect-sensitive human-robot cooperative framework is presented in this paper. Peripheral physiological indices are measured through wearable biofeedback sensors to detect the affective state of the human. Affect recognition is performed through both quantitative and qualitative analyses. A subsumption control architecture sensitive to the affective state of the human is proposed for a mobile robot. Human-robot cooperation experiments are performed where the robot senses the affective state of the human and responds appropriately. The results presented here validate the proposed framework and demonstrate a new way of achieving implicit communication between a human and a robot.

Distributed Embedded Control Architecture of a Leader-Follower Mobile Robot Formation

2015 ◽  
Vol 779 ◽  
pp. 201-204
Author(s):  
Ran Li ◽  
Yun Hua Li
Keyword(s):  
Control System ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Distributed Control ◽  
Electronic System ◽  
Control Architecture ◽  
Distributed Control System ◽  
Embedded Control ◽  
Data Bus ◽  
Robot Cooperation

Mobile robots have been widely used for the good adaptability, payload capability. Robot cooperation brings benefits for the task in a multi-robot team. In this paper, the modular hardware design of a leader-follower mobile robot team is discussed, including the distributed control architecture and the electronic system of each robot of the team. The basic idea behind this paper is to introduce the design of the hardware and distributed control architecture, which mainly manages the distributed control system, consisting of microcontroller modules connected through a data bus. The research has a potential applying prospect in mobile robot tracing and locating in the future.

Task-oriented hierarchical control architecture for swarm robotic system

2016 ◽  
Vol 16 (4) ◽  
pp. 579-596 ◽  
Author(s):  
Yuquan Leng ◽  
Cen Yu ◽  
Wei Zhang ◽  
Yang Zhang ◽  
Xu He ◽  
...  
Keyword(s):  
Hierarchical Control ◽  
Robotic System ◽  
Control Architecture ◽  
Hierarchical Control Architecture ◽  
Task Oriented

Technology jump in the industry: human–robot cooperation in production

2020 ◽  
Vol 47 (5) ◽  
pp. 757-775
Author(s):  
Zoltan Dobra ◽  
Krishna S. Dhir
Keyword(s):  
Design Methodology ◽  
Manufacturing Industry ◽  
Current Status ◽  
Robot System ◽  
Content Type ◽  
New Directions ◽  
Academic Publications ◽  
Robot Cooperation ◽  
Human Robot Collaboration ◽  
Practical Implications

Purpose Recent years have seen a technological change, Industry 4.0, in the manufacturing industry. Human–robot cooperation, a new application, is increasing and facilitating collaboration without fences, cages or any kind of separation. The purpose of the paper is to review mainstream academic publications to evaluate the current status of human–robot cooperation and identify potential areas of further research. Design/methodology/approach A systematic literature review is offered that searches, appraises, synthetizes and analyses relevant works. Findings The authors report the prevailing status of human–robot collaboration, human factors, complexity/ programming, safety, collision avoidance, instructing the robot system and other aspects of human–robot collaboration. Practical implications This paper identifies new directions and potential research in practice of human–robot collaboration, such as measuring the degree of collaboration, integrating human–robot cooperation into teamwork theories, effective functional relocation of the robot and product design for human robot collaboration. Originality/value This paper will be useful for three cohorts of readers, namely, the manufacturers who require a baseline for development and deployment of robots; users of robots-seeking manufacturing advantage and researchers looking for new directions for further exploration of human–machine collaboration.

scholarly journals A Robot Model of OC-Spectrum Disorders: Design Framework, Implementation, and First Experiments

2019 ◽  
Vol 3 ◽  
pp. 40-75 ◽  
Author(s):  
Matthew Lewis ◽  
Naomi Fineberg ◽  
Lola Cañamero
Keyword(s):  
Mental Disorders ◽  
Autonomous Robots ◽  
Control Architecture ◽  
Design Framework ◽  
Obsessive Compulsive ◽  
Stage Of Development ◽  
Initial Stage ◽  
Spectrum Disorders ◽  
Shed Light ◽  
Qualitative Analyses

Computational psychiatry is increasingly establishing itself as a valuable discipline for understanding human mental disorders. However, robot models and their potential for investigating embodied and contextual aspects of mental health have been, to date, largely unexplored. In this article, we present an initial robot model of obsessive-compulsive (OC) spectrum disorders based on an embodied motivation-based control architecture for decision-making in autonomous robots. The OC family of conditions is chiefly characterized by obsessions (recurrent, invasive thoughts) and/or compulsions (an urge to carry out certain repetitive or ritualized behaviors). The design of our robot model follows and illustrates a general design framework that we have proposed to ground research in robot models of mental disorders and to link it with existing methodologies in psychiatry, notably in the design of animal models. To test and validate our model, we present and discuss initial experiments, results, and quantitative and qualitative analyses regarding the compulsive and obsessive elements of OC-spectrum disorders. While this initial stage of development only models basic elements of such disorders, our results already shed light on aspects of the underlying theoretical model that are not obvious simply from consideration of the model.

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