Reliable robot assembly using haptic rendering models in combination with particle filters

In this paper we propose a method for reliable and error tolerant assembly for impedance controlled robots by using a particle filter based approach. Our method applies a haptic rendering model obtained from CAD data only, with which we are able to evaluate relative objects poses implemented as particles. The real world force torque sensor values are compared to the model based haptic rendering information to correct pose uncertainties during assembly. We make use of the KUKA LBR iiwa's intrinsic sensors to measure the position and joint torques representing the real world state. The particle filter is required to compensate pose errors which exceed the assembly clearance during assembly. We show the usefulness of our approach by simulation and real world peg-in-hole tasks.