scholarly journals Safety assurance of an industrial robotic control system using hardware/software co-verification

2022 ◽  
pp. 102766
Author(s):  
Yvonne Murray ◽  
Martin Sirevåg ◽  
Pedro Ribeiro ◽  
David A. Anisi ◽  
Morten Mossige
2021 ◽  
Vol 11 (10) ◽  
pp. 4437
Author(s):  
Paramin Neranon ◽  
Tanapong Sutiphotinun

One of the challenging aspects of robotics research is to successfully establish a human-like behavioural control strategy for human–robot handover, since a robotic controller is further complicated by the dynamic nature of the human response. This paper consequently highlights the development of an appropriate set of behaviour-based control for robot-to-human object handover by first understanding an equivalent human–human handover. The optimized hybrid position and impedance control was implemented to ensure good stability, adaptability and comfort of the robot in the object handover tasks. Moreover, a questionnaire technique was employed to gather information from the participants concerning their evaluations of the developed control system. The results demonstrate that the quantitative measurement of performance of the human-inspired control strategy can be considered acceptable for seamless human–robot handovers. This also provided significant satisfaction with the overall control performance in the robotic control system, in which the robot can dexterously pass the object to the receiver in a timely and natural manner without the risk of harm or injury by the robot. Furthermore, the survey responses were in agreement with the parallel test outcomes, demonstrating significant satisfaction with the overall performance of the robot–human interaction, as measured by an average rating of 4.20 on a five-point scale.


Author(s):  
Charles R. Bates ◽  
Joshua Oare ◽  
Brady Carey ◽  
Nicholas Gaston

For our Junior Project course we worked with John Anderson, apart of the Mechanical and Manufacturing Engineering and Technology department at Oregon Institute of Technology, to build a Robotic Control System. This system would allow a more cost effective way for teachers and students to create scripts for running a robot in an industry style assembly line. The basic functionality of such a program is to allow the user to create, manage, debug, run and save scripts that will directly manipulate a connected robot. The long term goal was to have a system that will allow any kind of robot to be implemented and have our front end connect to it and work exactly the same. We accomplished this using careful planning to give it the most agile feel possible. We are also attempting to utilize a combination of Microsoft Robotics Studio and direct serial port communication to interact with a Lynxmotion Lynx6 robot.


Author(s):  
Justin Cinkelj ◽  
Justin Činkelj ◽  
Roman Kamnik ◽  
Peter Cepon ◽  
Peter Čepon ◽  
...  

2011 ◽  
Vol 2011 ◽  
pp. 1-8
Author(s):  
Kathleen A. Kramer ◽  
Stephen C. Stubberud

Whether sensor model’s inaccuracies are a result of poor initial modeling or from sensor damage or drift, the effects can be just as detrimental. Sensor modeling errors result in poor state estimation. This, in turn, can cause a control system relying upon the sensor’s measurements to become unstable, such as in robotics where the control system is applied to allow autonomous navigation. A technique referred to as a neural extended Kalman filter (NEKF) is developed to provide both state estimation in a control loop and to learn the difference between the true sensor dynamics and the sensor model. The technique requires multiple sensors on the control system so that the properly operating and modeled sensors can be used as truth. The NEKF trains a neural network on-line using the same residuals as the state estimation. The resulting sensor model can then be reincorporated fully into the system to provide the added estimation capability and redundancy.


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