scholarly journals Force-Sensing Tensegrity for Investigating Physical Human-Robot Interaction in Compliant Robotic Systems

2021 ◽  
Author(s):  
Andrew R. Barkan ◽  
Akhil Padmanabha ◽  
Sala R. Tiemann ◽  
Albert Lee ◽  
Matthew P. Kanter ◽  
...  
Keyword(s):  
Human Robot Interaction ◽  
Robotic Systems ◽  
Force Sensing ◽  
Robot Interaction

scholarly journals Human-robot Interaction in Collaborative Robotic Systems

2021 ◽  
Vol 24 (4) ◽  
pp. 180-199
Author(s):  
R. R. Galin ◽  
V. V. Serebrennyj ◽  
G. K. Tevyashov ◽  
A. A. Shiroky
Keyword(s):  
Human Robot Interaction ◽  
Robotic Systems ◽  
Organizational Systems ◽  
Optimal Distribution ◽  
Systems Management ◽  
Robot Interaction ◽  
Organizational System ◽  
Collaborative Robotics ◽  
Collaborative Robots ◽  
Formal Problem

Purpose or research is to find solvable tasks for increasing the effectiveness of collaborative interaction between people and robots in ergatic robotic systems, or, in other words, in collaborative robotic systems. Methods. A comprehensive analysis of works published in highly rated peer-reviewed open-access scientific publications was carried out to achieve this goal. Main terms and concepts of collaborative robotics are described in § 1 and their current understanding in the research community is also described. The structure of workspaces in interaction zone of a person and robot is described. The criteria for assigning robot to the class of collaborative ones are also described. The criteria for safe interaction of a person and robot in a single workspace is described in § 2. Various grounds for classifying human-robot interactions in collaborative RTAs are described in § 3. Results. A significant part of published works about collaborative robotics is devoted to the organization of safe man and robot interaction. Less attention is paid to the effectiveness improvement of such interaction. An up-to-date task in the problem of efficiency improvement of collaborative robotic systems is the identification of tasks that have already been solved in other areas - in particular, in the field of organizational systems management. The possibility of using the term "team" for collaborative robots in a collaborative PTC is stated in § 4. A formal problem setting of optimal distribution in teamwork of collaborative robots, similar to the problem of heterogeneous team formation in the theory of organizational systems management is proposed in § 5. Conclusions. Proposed task setting of optimal distribution of works in collaborative robots’ team shows possibility of using results obtained in group of mathematical models of commands formation and functioning for control of collaborative robotic systems in order to increase efficiency of people and robots interaction. It is prospectively to continue the search for adapting models and governance mechanisms to the theory of organizational system management and integrated activities methodology.

scholarly journals Improving HRI with Force Sensing

Machines ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 15
Author(s):  
Akiyoshi Hayashi ◽  
Liz Katherine Rincon-Ardila ◽  
Gentiane Venture
Keyword(s):  
Machine Learning ◽  
Human Robot Interaction ◽  
Force Sensing ◽  
Robot Arm ◽  
Human Communication ◽  
Robot Interaction ◽  
Touch Gestures ◽  
The Future ◽  
Important Field

In the future, in a society where robots and humans live together, HRI is an important field of research. While most human–robot-interaction (HRI) studies focus on appearance and dialogue, touch-communication has not been the focus of many studies despite the importance of its role in human–human communication. This paper investigates how and where humans touch an inorganic non-zoomorphic robot arm. Based on these results, we install touch sensors on the robot arm and conduct experiments to collect data of users’ impressions towards the robot when touching it. Our results suggest two main things. First, the touch gestures were collected with two sensors, and the collected data can be analyzed using machine learning to classify the gestures. Second, communication between humans and robots using touch can improve the user’s impression of the robots.

Biologically inspired damage tolerance in braided pneumatic muscle actuators

2011 ◽  
Vol 23 (3) ◽  
pp. 313-325 ◽  
Author(s):  
S Davis ◽  
Darwin G Caldwell
Keyword(s):  
Human Robot Interaction ◽  
Robotic Systems ◽  
Biologically Inspired ◽  
Robot Interaction ◽  
Pneumatic Muscle ◽  
Joint Compliance ◽  
Soft Actuators ◽  
External Covering ◽  
Muscle Actuators ◽  
Manufacturing Environments

As the operation of robotic systems moves away from solely manufacturing environments to arenas where they must operate alongside humans, so the essential characteristics of their design has transformed. A move from traditional robot designs to more inherently safe concepts is required. Studying biological systems to determine how they achieve safe interactions is one approach being used. This then seeks to mimic the ingredients that make this interaction safe in robotics systems. This is often achieved through softness both in terms of a soft fleshy external covering and through motor systems that introduce joint compliance for softer physical Human-Robot Interaction (pHRI). This has led to the development of new actuators with performance characteristics that at least on a macroscopic level try to emulate the function of organic muscle. One of the most promising among these is the pneumatic Muscle Actuator (pMA). However, as with organic muscle, these soft actuators are more susceptible to damage than many traditional actuators. Whilst organic muscle can regenerate and recover, artificial systems do not possess this ability. This article analyzes how organic muscle is able to operate even after extreme trauma and shows how functionally similar techniques can be used with pMAs.

scholarly journals Human Robot Collaboration: An Augmented Reality Approach—A Literature Review and Analysis

2007 ◽  
Author(s):  
Scott A. Green ◽  
Mark Billinghurst ◽  
XiaoQi Chen ◽  
J. Geoffrey Chase
Keyword(s):  
Augmented Reality ◽  
Situational Awareness ◽  
Human Robot Interaction ◽  
Robotic Systems ◽  
Robot Interaction ◽  
Future Space ◽  
Effective Manner ◽  
Spatial Referencing ◽  
Common Understanding ◽  
Human Robot Collaboration

Future space exploration will demand the cultivation of human-robotic systems, however, little attention has been paid to the development of human-robot teams. Current methods for autonomous plan creation are often complex and difficult to use. So a system is needed that enables humans and robotic systems to naturally and effectively collaborate. Effective collaboration takes place when the participants are able to communicate in a natural and effective manner. Grounding, the common understanding between conversational participants, shared spatial referencing and situational awareness, are crucial components of communication and collaboration. This paper briefly reviews the fields of human-robot interaction and Augmented Reality (AR), the overlaying of computer graphics onto the real worldview. The strengths of AR are discussed and how they might be used for more effective human-robot collaboration is described. Then a description of an architecture that we have developed is given that uses AR as a means for real time understanding of the shared spatial scene. This architecture enables grounding and enhances situational awareness, thus laying the necessary groundwork for natural and effective human-robot collaboration.

scholarly journals Design-Centered HRI Governance for Healthcare Robots

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Yueh-Hsuan Weng ◽  
Yasuhisa Hirata
Keyword(s):  
Decision Making ◽  
Human Robot Interaction ◽  
Robotic Systems ◽  
Privacy Risk ◽  
Robot Interaction ◽  
Daily Lives ◽  
Ethical Concerns ◽  
Recent Developments ◽  
And Robotics

Recent developments have shown that not only are AI and robotics growing more sophisticated, but also these fields are evolving together. The applications that emerge from this trend will break current limitations and ensure that robotic decision making and functionality are more autonomous, connected, and interactive in a way which will support people in their daily lives. However, in areas such as healthcare robotics, legal and ethical concerns will arise as increasingly advanced intelligence functions are incorporated into robotic systems. Using a case study, this paper proposes a unique design-centered approach which tackles the issue of data protection and privacy risk in human-robot interaction.

Human — Robot Interaction During Learning and Telecontrol of the Heterogenic Robotic Systems Based on a Movement Shape Model

2017 ◽  
Vol 18 (7) ◽  
pp. 458-468
Author(s):  
S. E. Chernakova ◽  
◽  
L. A. Stankevich ◽  
S. V. Hlopin ◽  
A. I. Nechaev ◽  
...  
Keyword(s):  
Human Robot Interaction ◽  
Robotic Systems ◽  
Robot Interaction ◽  
Shape Model

Interaction modeling and classification scheme for augmenting the response accuracy of human-robot interaction systems

Work ◽  
2021 ◽  
pp. 1-10
Author(s):  
Hai Tao ◽  
MdArafatur Rahman ◽  
Wang Jing ◽  
Yafeng Li ◽  
Jing Li ◽  
...  
Keyword(s):  
Classification Scheme ◽  
Research Field ◽  
Human Robot Interaction ◽  
Robotic Systems ◽  
Multiple Sources ◽  
Robot Interaction ◽  
Prime Concern ◽  
Interaction Modeling ◽  
Error Classification ◽  
Two Phases

BACKGROUND: Human-Robot Interaction (HRI) is becoming a current research field for providing granular real-time applications and services through physical observation. Robotic systems are designed to handle the roles of humans and assist them through intrinsic sensing and commutative interactions. These systems handle inputs from multiple sources, process them, and deliver reliable responses to the users without delay. Input analysis and processing is the prime concern for the robotic systems to understand and resolve the queries of the users. OBJECTIVES: In this manuscript, the Interaction Modeling and Classification Scheme (IMCS) is introduced to improve the accuracy of HRI. This scheme consists of two phases, namely error classification and input mapping. In the error classification process, the input is analyzed for its events and conditional discrepancies to assign appropriate responses in the input mapping phase. The joint process is aided by a linear learning model to analyze the different conditions in the event and input detection. RESULTS: The performance of the proposed scheme shows that it is capable of improving the interaction accuracy by reducing the ratio of errors and interaction response by leveraging the information extraction from the discrete and successive human inputs. CONCLUSION: The fetched data are analyzed by classifying the errors at the initial stage to achieve reliable responses.

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