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Document type:
Zeitschriftenaufsatz 
Author(s):
Kuehn, J.; Haddadin, S. 
Title:
An Artificial Robot Nervous System To Teach Robots How To Feel Pain And Reflexively React To Potentially Damaging Contacts 
Abstract:
In this letter, we introduce the concept of an artificial Robot Nervous System (aRNS) as a novel way of unifying multimodal physical stimuli sensation with robot pain-reflex movements. We focus on the formalization of robot pain, based on insights from human pain research, as an interpretation of tactile sensation. Specifically, pain signals are used to adapt the equilibrium position, stiffness, and feedforward torque of a pain-based impedance controller. The schemes are experimentally validated...    »
 
Keywords:
Collision avoidance; Neurons; Pain; Robot kinematics; Robot sensing systems; Biologically-Inspired Robots; Biomimetics; Compliance and Impedance Control; Force and Tactile Sensing; Physical Human-Robot Interaction 
Journal title:
IEEE Robotics and Automation Letters 
Year:
2017 
Journal volume:
Month:
Jan 
Journal issue:
Pages contribution:
72-79 
Fulltext / DOI:
Print-ISSN:
2377-3766