Kalman filter-based yaw angle estimation by fusing inertial and magnetic sensing: a case study using low cost sensors

Sensor Review ◽  
2015 ◽  
Vol 35 (3) ◽  
pp. 244-250 ◽  
Author(s):  
Pedro Neto ◽  
Nuno Mendes ◽  
A. Paulo Moreira
Keyword(s):  
Kalman Filter ◽  
Inertial Sensor ◽  
Low Cost ◽  
Human Robot Interaction ◽  
Magnetic Sensor ◽  
Robot Interaction ◽  
Magnetic Sensing ◽  
Content Type ◽  
Reliable Estimation ◽  
Yaw Angle

Purpose – The purpose of this paper is to achieve reliable estimation of yaw angles by fusing data from low-cost inertial and magnetic sensing. Design/methodology/approach – In this paper, yaw angle is estimated by fusing inertial and magnetic sensing from a digital compass and a gyroscope, respectively. A Kalman filter estimates the error produced by the gyroscope. Findings – Drift effect produced by the gyroscope is significantly reduced and, at the same time, the system has the ability to react quickly to orientation changes. The system combines the best of each sensor, the stability of the magnetic sensor and the fast response of the inertial sensor. Research limitations/implications – The system does not present a stable behavior in the presence of large vibrations. Considerable calibration efforts are needed. Practical implications – Today, most of human–robot interaction technologies need to have the ability to estimate orientation, especially yaw angle, from small-sized and low-cost sensors. Originality/value – Existing methods for inertial and magnetic sensor fusion are combined to achieve reliable estimation of yaw angle. Experimental tests in a human–robot interaction scenario show the performance of the system.

AI and libraries: trends and projections

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Adetoun A. Oyelude
Keyword(s):  
Language Processing ◽  
Service Provision ◽  
Design Methodology ◽  
Human Robot Interaction ◽  
Human Intelligence ◽  
Library Service ◽  
Robot Interaction ◽  
Content Type ◽  
New Horizons ◽  
Advanced Technologies

Purpose This paper aims to focus on the trends and projection for future use of artificial intelligence (AI) in libraries. AI technologies is the latest among the technologies being used in libraries. The technology has systems that have natural language processing, machine learning and pattern recognition capabilities that make service provision easier for libraries. Design/methodology/approach Systematic literature review is done, exploring blogs and wikis, to collect information on the ways in which AI is used and can be futuristically used in libraries. Findings This paper found that uses of AI in libraries entailed enhanced services such as content indexing, document matching, content mapping content summarization and many others. AI possibilities were also found to include improving the technology of gripping, localizing and human–robot interaction and also having artificial superintelligence, the hypothetical AI that surpasses human intelligence and abilities. Originality/value It is concluded that advanced technologies that AI are, will help librarians to open up new horizons and solve challenges that crop up in library service delivery.

The Pransky interview: Dr Cory Kidd, Founder and CEO at Catalia Health

2017 ◽  
Vol 44 (3) ◽  
pp. 259-263 ◽  
Author(s):  
Joanne Pransky
Keyword(s):  
Large Scale ◽  
Information Science ◽  
Industrial Robot ◽  
Human Robot Interaction ◽  
High Tech ◽  
Healthcare Applications ◽  
Robot Interaction ◽  
Wall Street ◽  
Content Type ◽  
Institute Of Technology

Purpose The purpose of this paper is a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry PhD-turned-entrepreneur regarding the evolution, commercialization and challenges of bringing a technological invention to market. Design/methodology/approach The interviewee is Dr Cory Kidd, an inventor, entrepreneur and leading practitioner in the field of human–robot interaction. Dr Kidd shares his 20-year journey of working at the intersection of healthcare and technology and how he applied innovative technologies toward solving large-scale consumer healthcare challenges. Findings Dr Kidd received his BS degree in Computer Science from the Georgia Institute of Technology and earned a National Science Foundation Graduate Research Fellow in Computer and Information Science & Engineering. Dr Kidd received his MS and PhD degrees at the MIT Media Lab in human–robot interaction. While there, he conducted studies that showed the psychological and clinical advantages of using a physical robot over screen-based interactions. While finishing his PhD in 2007, he founded his first company, Intuitive Automata, which created interactive coaches for weight loss. Though Intuitive Automata ceased operations in 2013, Dr Kidd harnessed his extensive knowledge of the healthcare business and the experiences from patient engagement and launched Catalia Health in 2014 with a new platform centered specifically around patient behavior change programs for chronic disease management. Originality/value Dr Kidd is a pioneer of social robotics and has developed groundbreaking technology for healthcare applications that combines artificial intelligence, psychology and medical best practices to deliver everyday care to patients who are managing chronic conditions. He holds patents, including one entitled Apparatus and Method for Assisting in Achieving Desired Behavior Patterns and in an Interactive Personal Health Promoting Robot. Dr Kidd was awarded the inaugural Wall Street Journal and Credit Suisse Technopreneur of the Year in 2010, which is meant to “honor the entry that best applies technology with the greatest potential for commercial success”. He is also the Director of Business Development for the nonprofit Silicon Valley Robotics and is an impact partner for Fresco Capital. He consults, mentors and serves as a Board Member and Advisor to several high-tech startups.

scholarly journals Modelling and Control of a 2-DOF Robot Arm with Elastic Joints for Safe Human-Robot Interaction

2021 ◽  
Vol 8 ◽  
Author(s):  
Hua Minh Tuan ◽  
Filippo Sanfilippo ◽  
Nguyen Vinh Hao
Keyword(s):  
Control Algorithm ◽  
Position Control ◽  
Low Cost ◽  
Dynamic Environment ◽  
Human Robot Interaction ◽  
Robot Arm ◽  
Control Loop ◽  
Robot Interaction ◽  
Elastic Joint ◽  
Elastic Joints

Collaborative robots (or cobots) are robots that can safely work together or interact with humans in a common space. They gradually become noticeable nowadays. Compliant actuators are very relevant for the design of cobots. This type of actuation scheme mitigates the damage caused by unexpected collision. Therefore, elastic joints are considered to outperform rigid joints when operating in a dynamic environment. However, most of the available elastic robots are relatively costly or difficult to construct. To give researchers a solution that is inexpensive, easily customisable, and fast to fabricate, a newly-designed low-cost, and open-source design of an elastic joint is presented in this work. Based on the newly design elastic joint, a highly-compliant multi-purpose 2-DOF robot arm for safe human-robot interaction is also introduced. The mechanical design of the robot and a position control algorithm are presented. The mechanical prototype is 3D-printed. The control algorithm is a two loops control scheme. In particular, the inner control loop is designed as a model reference adaptive controller (MRAC) to deal with uncertainties in the system parameters, while the outer control loop utilises a fuzzy proportional-integral controller to reduce the effect of external disturbances on the load. The control algorithm is first validated in simulation. Then the effectiveness of the controller is also proven by experiments on the mechanical prototype.

Impedance control of a cable-driven SEA with mixed H2/H∞ synthesis

2017 ◽  
Vol 37 (3) ◽  
pp. 296-303 ◽  
Author(s):  
Ningbo Yu ◽  
Wulin Zou
Keyword(s):  
Control Strategy ◽  
Control Method ◽  
Impedance Control ◽  
Low Frequency ◽  
Human Robot Interaction ◽  
Control Input ◽  
Robot Interaction ◽  
Content Type ◽  
Series Elastic Actuator ◽  
Series Elastic

Purpose This paper aims to present an impedance control method with mixed H2/H∞ synthesis and relaxed passivity for a cable-driven series elastic actuator to be applied for physical human–robot interaction. Design/methodology/approach To shape the system’s impedance to match a desired dynamic model, the impedance control problem was reformulated into an impedance matching structure. The desired competing performance requirements as well as constraints from the physical system can be characterized with weighting functions for respective signals. Considering the frequency properties of human movements, the passivity constraint for stable human–robot interaction, which is required on the entire frequency spectrum and may bring conservative solutions, has been relaxed in such a way that it only restrains the low frequency band. Thus, impedance control became a mixed H2/H∞ synthesis problem, and a dynamic output feedback controller can be obtained. Findings The proposed impedance control strategy has been tested for various desired impedance with both simulation and experiments on the cable-driven series elastic actuator platform. The actual interaction torque tracked well the desired torque within the desired norm bounds, and the control input was regulated below the motor velocity limit. The closed loop system can guarantee relaxed passivity at low frequency. Both simulation and experimental results have validated the feasibility and efficacy of the proposed method. Originality/value This impedance control strategy with mixed H2/H∞ synthesis and relaxed passivity provides a novel, effective and less conservative method for physical human–robot interaction control.

Enabling Low-Cost Full Surface Tactile Skin for Human Robot Interaction

2022 ◽  
pp. 1-1
Author(s):  
Xiaoran Fan ◽  
Daewon Lee ◽  
Lawrence Jackel ◽  
Richard Howard ◽  
Daniel Lee ◽  
...  
Keyword(s):  
Low Cost ◽  
Human Robot Interaction ◽  
Robot Interaction

Improving stability in physical human–robot interaction by estimating human hand stiffness and a vibration index

2019 ◽  
Vol 46 (4) ◽  
pp. 529-540 ◽  
Author(s):  
Feifei Bian ◽  
Danmei Ren ◽  
Ruifeng Li ◽  
Peidong Liang
Keyword(s):  
Time Delay ◽  
Real Time ◽  
Time Domain ◽  
Design Methodology ◽  
Interaction Force ◽  
Human Robot Interaction ◽  
Experimental Results ◽  
Human Hand ◽  
Robot Interaction ◽  
Content Type

Purpose The purpose of this paper is to eliminate instability which may occur when a human stiffens his arms in physical human–robot interaction by estimating the human hand stiffness and presenting a modified vibration index. Design/methodology/approach Human hand stiffness is first estimated in real time as a prior indicator of instability by capturing the arm configuration and modeling the level of muscle co-contraction in the human’s arms. A time-domain vibration index based on the interaction force is then modified to reduce the delay in instability detection. The instability is confirmed when the vibration index exceeds a given threshold. The virtual damping coefficient in admittance controller is adjusted accordingly to ensure stability in physical human–robot interaction. Findings By estimating the human hand stiffness and modifying the vibration index, the instability which may occur in stiff environment in physical human–robot interaction is detected and eliminated, and the time delay is reduced. The experimental results demonstrate significant improvement in stabilizing the system when the human operator stiffens his arms. Originality/value The originality is in estimating the human hand stiffness online as a prior indicator of instability by capturing the arm configuration and modeling the level of muscle co-contraction in the human’s arms. A modification of the vibration index is also an originality to reduce the time delay of instability detection.

Human robot interaction for manipulation tasks based on stroke gesture recognition

2017 ◽  
Vol 44 (6) ◽  
pp. 700-710 ◽  
Author(s):  
Jiajun Li ◽  
Jianguo Tao ◽  
Liang Ding ◽  
Haibo Gao ◽  
Zongquan Deng ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Design Methodology ◽  
Spatial Information ◽  
Recognition Accuracy ◽  
Parameter Extraction ◽  
Human Robot Interaction ◽  
Extraction System ◽  
Robot Interaction ◽  
Content Type ◽  
Practical Implications

Purpose The purpose of this paper is to extend the usage of stroke gestures in manipulation tasks to make the interaction between human and robot more efficient. Design/methodology/approach In this paper, a set of stroke gestures is designed for typical manipulation tasks. A gesture recognition and parameter extraction system is proposed to exploit the information in stroke gestures drawn by the users. Findings The results show that the designed gesture recognition subsystem can reach a recognition accuracy of 99.00 per cent. The parameter extraction subsystem can successfully extract parameters needed for typical manipulation tasks with a success rate about 86.30 per cent. The system shows an acceptable performance in the experiments. Practical implications Using stroke gesture in manipulation tasks can make the transmission of human intentions to the robots more efficient. The proposed gesture recognition subsystem is based on convolutional neural network which is robust to different input. The parameter extraction subsystem can extract the spatial information encoded in stroke gestures. Originality/value The author designs stroke gestures for manipulation tasks which is an extension of the usage of stroke gestures. The proposed gesture recognition and parameter extraction system can make use of stroke gestures to get the type of the task and important parameters for the task simultaneously.

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