A Variable-Fractional Order Admittance Controller for pHRI

Abstract

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 tradeoff 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.

Detecting Human Motion Intention during pHRI Using Artificial Neural Networks Trained by EMG Signals

“…For this purpose, acquiring EMG data from different muscle groups such as Anterior Deltoid might be required. Furthermore, we plan to combine our proposed architecture with a variable admittance controller as implemented in [3]. Instead of using an admittance controller with fixed parameters as in this study, the controller parameters will be adjusted on-the-fly according to the needs of human operator, which will be estimated based on kinetic/kinematic information.…”

“…1). Such an architecture, for example, could be beneficial when a human operator in a factory aims to drill a series of holes at the corners of a rectangular workpiece where a cobot can constrain the movements of the operator to help with the task [3]. In such a setting, the operator grabs the power drill attached to the end-effector of the cobot and brings it closer to the surface of the workpiece.…”

Control Techniques for Safe, Ergonomic, and Efficient Human-Robot Collaboration in the Digital Industry: A Survey

“…Li et al develop an impedance controller on the basis of PD control to realize the active compliance, while the passive one is achieved by a specially designed elastic element. The trade-off between stability and transparency is a core challenge addressed also in [87], where a new variable fractional order admittance controller (FOAC) is proposed to handle this trade-off. The designed controller displays stability robustness against the system disturbances.…”

An adaptive admittance controller for collaborative drilling with a robot based on subtask classification via deep learning

“…There are mainly two different approaches for detecting human intention in pHRI: a) rule-based, and b) learning-based. Rule-based approaches have been frequently used to adapt an interaction controller based on recognized human intention to take leader or follower role, or to accelerate or decelerate a co-manipulated object [11,[14][15][16][17][18][19][20][21][22]. In such systems, a predefined and fixed or manually tuned set of heuristics is implemented for detecting human intention in real time.…”

“…Albeit fast and easy to implement, rule-based approaches for detecting human intention tend to have adequate performance under certain circumstances only and cannot easily be generalized to different pHRI tasks of similar nature. This is because rule-based approaches often use task-specific variables such as position of robot's end-effector [22], or extremum values of force, velocity or their derivative to detect human intention [11,[16][17][18][19][20]. The range of such variables are task and environment-dependent.…”