A variable-fractional order admittance controller for pHRI

In today’s automation driven manufacturing environments, emerging technologies like cobots (collaborative robots) and augmented reality interfaces can help integrating humans into the production workflow to benefit from their adaptability and cognitive skills. In such settings, humans are expected to work with robots side by side and physically interact with them. However, the trade-off between stability and transparency is a core challenge in the presence of physical human robot interaction (pHRI). While stability is of utmost importance for safety, transparency is required for fully exploiting the precision and ability of robots in handling labor intensive tasks. In this work, we propose a new variable admittance controller based on fractional order control to handle this trade-off more effectively. We compared the performance of fractional order variable admittance controller with a classical admittance controller with fixed parameters as a baseline and an integer order variable admittance controller during a realistic drilling task. Our comparisons indicate that the proposed controller led to a more transparent interaction compared to the other controllers without sacrificing the stability. We also demonstrate a use case for an augmented reality (AR) headset which can augment human sensory capabilities for reaching a certain drilling depth otherwise not possible without changing the role of the robot as the decision maker

From discrete task plans to continuous trajectories

We present a logic-based framework to provide robots with high-level reasoning, such as planning. This framework uses the action description language C+ to represent actions and changes, and the system CCALC to reason about them. In particular, we can represent action domains that involve concurrent actions and additive fluents; based on this description, we can compute shortest plans to a planning problem that involves cost constraints. We show the applicability of this framework on two LEGO MINDSTORMS NXT robots: we compute a discrete task plan (possibly with concurrency) with a cost less than a specified value, and transform this plan into a continuous collision-free trajectory