Abstract
An ideal physical Human-Robot Interaction (pHRI) should offer the users robotic systems that are easy to handle, intuitive to use, ergonomic and adaptive to human habits and preferences. But the variance in the user behavior is often high and rather unpredictable which hinders the development of such systems. This article introduces a Personalized Adaptive Stiffness controller for pHRI which is calibrated for the user's force profile and validates its performance in an extensive user study with 49 participants on two different tasks. The user study compares the new scheme to conventional fixed stiffness or gravitation compensation controllers on the 7-DOF KUKA LWR IVb by employing two typical joint-manipulation tasks. The results clearly point out the importance of considering task specific parameters and human specific parameters while designing control modes for pHRI. The analysis shows that for simpler tasks a standard fixed controller may perform sufficiently well and that respective task dependency strongly prevails over individual differences. In the more complex task, quantitative and qualitative results reveal differences between the respective control modes, where the Personalized Adaptive Stiffness controller excels in terms of both performance gain and user preference. Further analysis shows that human and task parameters can be combined and quantified by considering the manipulability of a simplified human arm model. The analysis of user's interaction force profiles confirms this finding.
Highlights
As opposed to conventional industrial robotics where the robots are programmed to accomplish a fixed and repetitive task, interactive scenarios demand flexible robotic systems where the robot assists the human worker by collaborating with them, increasingly often through physical human–robot interaction
Substantial variation in human interaction forces coupled with unpredictable human behavior make it difficult to design a suitable physical human–robot interaction (pHRI) system
The Personalized Adaptive Stiffness mode has no significant difference in time of comple tion when compared with Gravity Compensation mode and at the same time the smoothness of Adaptive Stiffness mode is even superior to High Stiffness, having lower number of peaks
Summary
As opposed to conventional industrial robotics where the robots are programmed to accomplish a fixed and repetitive task, interactive scenarios demand flexible robotic systems where the robot assists the human worker by collaborating with them, increasingly often through physical human–robot interaction (pHRI). It is widely assumed that pHRI will improve flexi bility and productivity by taking advantage of the human’s cogni tive and perceptual skills, it is unclear how this interaction in detail may be made more ergonomical and pleasant for the user For this aim, a few number of novel platforms are commercially available that allow the adaptation of the robot controller to make the human–robot interaction smoother. User interaction forces and the physical characteristics of the users such as differences in height, body proportions, left or right handedness, the distance the user keeps with the robot, or varying cognitive skills can introduce substantial variance This demands personalization of the robots to be capable of accom modating user-specific dynamics
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