Event
Proposal of a sensorless hybrid pose-force control framework for pHRI
1 reports · 1 sources
Nuo Chen, Ya-Jun Pan, and Onur Toker proposed and experimentally validated a framework for physical human-robot interaction (pHRI) that combines a robust force-wrench observer with a hybrid pose-force controller to achieve pose and force tracking of a robotic manipulator's end effector without using force/torque sensors. Experimental results show that the framework achieves robust and accurate force-wrench estimation while maintaining pose and force tracking. To improve observer performance, the authors used a neural network to compensate for system friction and unmodeled dynamics, and employed task null-space decomposition to separate joint velocities in order to reduce the influence of human disturbances on the observer. The authors state that the method can be applied to a variety of robotic manipulator platforms without force/torque sensors.
Report timeline
- Sensorless force/torque observer and hybrid pose-force control framework for physical human-robot interaction
The framework combines a neural-network-enhanced robust wrench observer with a hybrid pose-force controller for physical human-robot interaction (pHRI), enabling end-effector pose and force tracking without force/torque sensors. The neural network compensates for system friction and unmodeled dynamics to improve observer performance, and task-null-space decomposition separates joint velocities and reduces the effect of human disturbance on the observer. Experimental results show that the framework achieves robust and accurate wrench estimation while maintaining reliable pose and force tracking, and can be applied to various robotic arm platforms without force/torque sensors.
arXiv cs.RO ·