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.

Robust Disturbance Observer-Based Adaptive Fuzzy Controller for Pneumatic Muscle Actuators

2011 ◽  
Author(s):  
Prashant K. Jamwal ◽  
Sheng Quan Xie ◽  
Sean Quigley
Keyword(s):  
Disturbance Observer ◽  
Robot Control ◽  
Fuzzy Controller ◽  
State Of The Art ◽  
Human Robot Interaction ◽  
Uncertain Environments ◽  
Robot Interaction ◽  
Pneumatic Muscle ◽  
Muscle Actuators ◽  
Varying Environment

Variants of Fuzzy logic controllers (FLC) have been widely used to control the systems characterized by uncertain and ambiguous parameters. Control objectives for such systems become more challenging when they are subjected to uncertain environments. Human-robot interaction is such phenomenon wherein robot control difficulties are further augmented due to human intervention. State of the art of research in FLC has been limited in establishing a trade-off between accuracy and interpretability, since achieving both these performance measures simultaneously is difficult. In the present research, an adaptive FLC has been designed in order to achieve better accuracy and higher interpretability. Supported by another instance of FLC as disturbance observer, the proposed controller has adaptive mechanism specifically designed to alter its parameters. The adaptive FLC has been implemented to control actuation of a pneumatic muscle actuator (PMA). Experimental results show excellent trajectory tracking performance of the PMA in the presence of varying environment.

Humans and humanoids

2018 ◽  
Author(s):  
Giorgio Metta
Keyword(s):  
Humanoid Robot ◽  
Robot Vision ◽  
Physical Space ◽  
Humanoid Robots ◽  
Human Robot Interaction ◽  
Biologically Inspired ◽  
Robot Interaction ◽  
Humanoid Robotics ◽  
Biomimetic Robot ◽  
Compliant Actuation

This chapter outlines a number of research lines that, starting from the observation of nature, attempt to mimic human behavior in humanoid robots. Humanoid robotics is one of the most exciting proving grounds for the development of biologically inspired hardware and software—machines that try to recreate billions of years of evolution with some of the abilities and characteristics of living beings. Humanoids could be especially useful for their ability to “live” in human-populated environments, occupying the same physical space as people and using tools that have been designed for people. Natural human–robot interaction is also an important facet of humanoid research. Finally, learning and adapting from experience, the hallmark of human intelligence, may require some approximation to the human body in order to attain similar capacities to humans. This chapter focuses particularly on compliant actuation, soft robotics, biomimetic robot vision, robot touch, and brain-inspired motor control in the context of the iCub humanoid robot.

scholarly journals Environmental effects on reliability and accuracy of MFCC based voice recognition for industrial human-robot-interaction

2021 ◽  
pp. 095440542110144
Author(s):  
B Birch ◽  
CA Griffiths ◽  
A Morgan
Keyword(s):  
Speech Recognition ◽  
Voice Recognition ◽  
Human Robot Interaction ◽  
Hole Drilling ◽  
Time Warping ◽  
Mel Frequency Cepstral Coefficients ◽  
Robot Interaction ◽  
Extraction Algorithm ◽  
Dynamic Time ◽  
Manufacturing Environments

Collaborative robots are becoming increasingly important for advanced manufacturing processes. The purpose of this paper is to determine the capability of a novel Human-Robot-interface to be used for machine hole drilling. Using a developed voice activation system, environmental factors on speech recognition accuracy are considered. The research investigates the accuracy of a Mel Frequency Cepstral Coefficients-based feature extraction algorithm which uses Dynamic Time Warping to compare an utterance to a limited, user-dependent dictionary. The developed Speech Recognition method allows for Human-Robot-Interaction using a novel integration method between the voice recognition and robot. The system can be utilised in many manufacturing environments where robot motions can be coupled to voice inputs rather than using time consuming physical interfaces. However, there are limitations to uptake in industries where the volume of background machine noise is high.

scholarly journals A Bio-inspired Motivational Decision Making System for Social Robots Based on the Perception of the User

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2691 ◽  
Author(s):  
Marcos Maroto-Gómez ◽  
Álvaro Castro-González ◽  
José Castillo ◽  
María Malfaz ◽  
Miguel Salichs
Keyword(s):  
Decision Making ◽  
Human Robot Interaction ◽  
Learning Mechanisms ◽  
Biologically Inspired ◽  
Robot Interaction ◽  
Detection Mechanism ◽  
Learned Behaviors ◽  
Decision Making System ◽  
Real Robot ◽  
Self Learning

Nowadays, many robotic applications require robots making their own decisions and adapting to different conditions and users. This work presents a biologically inspired decision making system, based on drives, motivations, wellbeing, and self-learning, that governs the behavior of the robot considering both internal and external circumstances. In this paper we state the biological foundations that drove the design of the system, as well as how it has been implemented in a real robot. Following a homeostatic approach, the ultimate goal of the robot is to keep its wellbeing as high as possible. In order to achieve this goal, our decision making system uses learning mechanisms to assess the best action to execute at any moment. Considering that the proposed system has been implemented in a real social robot, human-robot interaction is of paramount importance and the learned behaviors of the robot are oriented to foster the interactions with the user. The operation of the system is shown in a scenario where the robot Mini plays games with a user. In this context, we have included a robust user detection mechanism tailored for short distance interactions. After the learning phase, the robot has learned how to lead the user to interact with it in a natural way.

scholarly journals Human-Like Arm Motion Generation: A Review

2020 ◽  
Author(s):  
Gianpaolo Gulletta ◽  
Wolfram Erlhagen ◽  
Estela Bicho
Keyword(s):  
Human Robot Interaction ◽  
Motion Generation ◽  
Biologically Inspired ◽  
Robot Interaction ◽  
Arm Reaching ◽  
Strong Focus ◽  
Human Likeness ◽  
Human Motor ◽  
Arm Motion ◽  
Personal Robotics

In the last decade, the objectives outlined by the needs of personal robotics have led to the rise of new biologically-inspired techniques for arm motion planning. This paper presents a literature review of the most recent research on the generation of human-like arm movements in humanoid and manipulation robotic systems. Search methods and inclusion criteria are described. The studies are analysed taking into consideration the sources of publication, the experimental settings, the type of movements, the technical approach, and the human motor principles that have been used to inspire and assess human-likeness. Results show that there is a strong focus on the generation of single-arm reaching movements and biomimetic-based methods. However, there has been poor attention to manipulation, obstacle-avoidance mechanisms, and dual-arm motion generation. For these reasons, human-like arm motion generation may not fully respect human behavioural and neurological key features and may result restricted to specific tasks of human-robot interaction. Limitations and challenges are discussed to provide meaningful directions for future investigations.

scholarly journals Human-Like Arm Motion Generation: A Review

Robotics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 102
Author(s):  
Gianpaolo Gulletta ◽  
Wolfram Erlhagen ◽  
Estela Bicho
Keyword(s):  
Human Robot Interaction ◽  
Motion Generation ◽  
Biologically Inspired ◽  
Robot Interaction ◽  
Arm Reaching ◽  
Strong Focus ◽  
Human Likeness ◽  
Human Motor ◽  
Arm Motion ◽  
Personal Robotics

In the last decade, the objectives outlined by the needs of personal robotics have led to the rise of new biologically-inspired techniques for arm motion planning. This paper presents a literature review of the most recent research on the generation of human-like arm movements in humanoid and manipulation robotic systems. Search methods and inclusion criteria are described. The studies are analyzed taking into consideration the sources of publication, the experimental settings, the type of movements, the technical approach, and the human motor principles that have been used to inspire and assess human-likeness. Results show that there is a strong focus on the generation of single-arm reaching movements and biomimetic-based methods. However, there has been poor attention to manipulation, obstacle-avoidance mechanisms, and dual-arm motion generation. For these reasons, human-like arm motion generation may not fully respect human behavioral and neurological key features and may result restricted to specific tasks of human-robot interaction. Limitations and challenges are discussed to provide meaningful directions for future investigations.

Research on Human Cognition for Biologically Inspired Developments

2017 ◽  
pp. 83-116 ◽  
Author(s):  
Marko Wehle ◽  
Alexandra Weidemann ◽  
Ivo Wilhelm Boblan
Keyword(s):  
Role Model ◽  
Cognitive Model ◽  
Human Robot Interaction ◽  
Human Cognition ◽  
Biologically Inspired ◽  
Robot Interaction ◽  
Intelligent Machines ◽  
The World ◽  
Conducting Research ◽  
Interaction Research

Robotic developments are seen as a next level in technology with intelligent machines, which automate tedious tasks and serve our needs without complaints. But nevertheless, they have to be fair and smart enough to be intuitively of use and safe to handle. But how to implement this kind of intelligence, does it need feelings and emotions, should robots perceive the world as we do as a human role model, how far should the implementation of synthetic consciousness lead and actually, what is needed for consciousness in that context? Additionally in Human-Robot-Interaction research, science mainly makes use of the tool phenomenography, which is exclusively subjective, so how to make it qualify for Artificial Intelligence? These are the heading aspects of this chapter for conducting research in the field of social robotics and suggesting a conscious and cognitive model for smart and intuitive interacting robots, guided by biomimetics.

Sign in / Sign up

Export Citation Format

Share Document