Extending a Dynamic Friction Model with Nonlinear Viscous and Thermal Dependency for a Motor and Harmonic Drive Gear

In robotic actuation a well identified and modeled friction behavior of the actuator components helps to significantly improve friction compensation, output torque estimation, and dynamic simulations. The friction of two components, i.e. a brush-less DC motor and a harmonic drive gear (HD) is investigated in order to build an accurate dynamic model of the main actuator of the arms of the humanoid David namely the DLR Floating Spring Joint (FSJ). A dedicated testbed is built to precisely identify input and output torques, temperatures, positions, and elasticities of the investigated components at a controlled environment temperature. Extensive test series are performed in the full velocity operating range in a temperature interval from 24 to 50° C. The nonlinear influences of velocity and temperature are identified to be dominant effects. It is proposed how to include these nonlinear velocity and temperature dependencies into a static and a dynamic friction model, e.g. LuGre. Dynamic models of the motor and HD are built with the proposed method and experimentally evaluated. The new models are compared to friction models with linear dependencies and show a significant improvement of correspondence with reality

Learning-based adaption of robotic friction models

In the Fourth Industrial Revolution, wherein artificial intelligence and the automation of machines occupy a central role, the deployment of robots is indispensable. However, the manufacturing process using robots, especially in collaboration with humans, is highly intricate. In particular, modeling the friction torque in robotic joints is a longstanding problem due to the lack of a good mathematical description. This motivates the usage of data-driven methods in recent works. However, model-based and data-driven models often exhibit limitations in their ability to generalize beyond the specific dynamics they were trained on, as we demonstrate in this paper. To address this challenge, we introduce a novel approach based on residual learning, which aims to adapt an existing friction model to new dynamics using as little data as possible. We validate our approach by training a base neural network on a symmetric friction data set to learn an accurate relation between the velocity and the friction torque. Subsequently, to adapt to more complex asymmetric settings, we train a second network on a small dataset, focusing on predicting the residual of the initial network's output. By combining the output of both networks in a suitable manner, our proposed estimator outperforms the conventional model-based approach and the base neural network significantly. Furthermore, we evaluate our method on trajectories involving external loads and still observe a substantial improvement, approximately 60-70\%, over the conventional approach. Our method does not rely on data with external load during training, eliminating the need for external torque sensors. This demonstrates the generalization capability of our approach, even with a small amount of data-only 43 seconds of a robot movement-enabling adaptation to diverse scenarios based on prior knowledge about friction in different settings

Hybrid Force-Impedance Control for Fast End-Effector Motions

Controlling the contact force on various surfaces is essential in many robotic applications such as in service tasks or industrial use cases. Mostly, classical impedance and hybrid motion-force control approaches are employed for these kinds of physical interaction scenarios. In this work, an extended Cartesian impedance control algorithm is developed, which includes geometrical constraints and enables explicit force tracking in a hybrid manner. The unified framework features compliant behavior in the free (motion) task directions and explicit force tracking in the constrained directions. Advantageously, the involved force subspace in contact direction is fully dynamically decoupled from dynamics in the motion subspace. The experimental validation with a torque-controlled robotic manipulator on both flat and curved surfaces demonstrates the performance during highly dynamic desired trajectories and confirms the theoretical claims of the approach

Extensions to Dynamically-Consistent Collision Reaction Control for Collaborative Robots

Since modern robots are supposed to work closely together with humans, physical human-robot interaction is gaining importance. One crucial aspect for safe collaboration is a robust collision reaction strategy that is triggered after an unintentional physical contact. In this work, we propose a dynamically-consistent collision reaction controller, where the reactive motion is performed in one particular desired direction in Cartesian space, without disturbing the remaining ones. This results in more intuitive and more predictable behavior of the end-effector. In addition, the proposed reaction control law is independent of contact and internal observer dynamics used for collision detection. The theoretical claims are validated in simulation and experiments. The proposed reaction controller is experimentally compared with a conventional approach for collision reaction. All experiments have been conducted on a torque controlled KUKA LWR IV+ lightweight robot

Collision Detection, Identification, and Localization on the DLR SARA Robot with Sensing Redundancy

Physical human-robot interaction is known to be a crucial aspect in modern lightweight robotics. Herein, the estimation of external interactions is essential for the effective and safe collaboration. In this work, an extended momentum-based disturbance observer is presented which includes the sensing redundancy related to additional force-torque measurements. The observer eliminates the need for acceleration measurements/estimates and it is able to accurately reconstruct multiple simultaneous contact locations. Moreover, it provides uncoupled, configuration-independent, and singularity-free estimates of the external forces. The performance of the approach is experimentally validated on the SARA robot, the new generation of DLR lightweight robots, involving high resolution force-torque sensors in a redundant arrangement

Joint-Level Control of the DLR Lightweight Robot SARA

Lightweight robots are known to be intrinsically elastic in their joints. The established classical approaches to control such systems are mostly based on motor-side coordinates since the joints are comparatively stiff. However, that inevitably introduces errors in the coordinates that actually matter: the ones on the link side. Here we present a new joint-torque controller that uses feedback of the link-side positions. Passivity during interaction with the environment is formally shown as well as asymptotic stability of the desired equilibrium in the regulation case. The performance of the control approach is experimentally validated on DLR’s new generation of lightweight robots, namely the SARA robot, which enables this step from motor-side-based to link-sided-based control due to sensors with higher resolution and improved sampling rate

Model Predictive Control Applied to Different Time-scale Dynamics of Flexible Joint Robots

Modern Lightweight robots are constructed to be collaborative, which often results in a low structural stiffness compared to conventional rigid robots. Therefore, the controller must be able to handle the dynamic oscillatory effect mainly due to the intrinsic joint elasticity. Singular perturbation theory makes it possible to decompose the flexible joint dynamics into fast and slow subsystems. This model separation provides additional features to incorporate future knowledge of the joint level dynamical behavior within the controller design using the Model Predictive Control (MPC) technique. In this study, different architectures are considered that combine the method of Singular Perturbation and MPC. For Singular Perturbation, the parameters that influence the validity of using this technique to control a flexible-joint robot are investigated. Furthermore, limits on the input constraints for the future trajectory are considered with MPC. The position control performance and robustness against external forces of each architecture are validated experimentally for a flexible joint robot. The experimental validation shows superior performance in practice for the presented MPC framework, especially respecting the actuator torque limits

Shared Control Templates for Assistive Robotics

Light-weight robotic manipulators can be used to restore the manipulation capability of people with a motor disability. However, manipulating the environment poses a complex task, especially when the control interface is of low bandwidth, as may be the case for users with impairments. Therefore, we propose a constraint-based shared control scheme to define skills which provide support during task execution. This is achieved by representing a skill as a sequence of states, with specific user command mappings and different sets of constraints being applied in each state. New skills are defined by combining different types of constraints and conditions for state transitions, in a human-readable format. We demonstrate its versatility in a pilot experiment with three activities of daily living. Results show that even complex, high-dimensional tasks can be performed with a low-dimensional interface using our shared control approach

EDAN - An EMG-controlled Daily Assistant To Help People With Physical Disabilities

Injuries, accidents, strokes, and other diseases can significantly degrade the capabilities to perform even the most simple activities in daily life. A large share of these cases involves neuromuscular diseases, which lead to severely reduced muscle function. However, even though affected people are no longer able to move their limbs, residual muscle function can still be existent. Previous work has shown that this residual muscular activity can suffice to apply an EMG-based user interface. In this paper, we introduce DLR's robotic wheelchair EDAN (EMG-controlled Daily Assistant), which is equipped with a torque-controlled, eight degree-of-freedom light-weight arm and a dexterous, five-fingered robotic hand. Using electromyography, muscular activity of the user is measured,processed and utilized to control both the wheelchair and the robotic manipulator. This EMG-based interface is enhanced with shared control functionality to allow for efficient and safe physical interaction with the environment

Dynamic friction model with thermal and load dependency: modeling, compensation, and external force estimation

A physically-motivated friction model with a parametric description of the nonlinear dependency of the temperature and velocity as well as the dependency on external load is presented. The fully parametric approach extends a static friction model in the gross sliding regime. We show how it can be seamlessly integrated in standard dynamic friction models such as Lund Grenoble (LuGre) and Generalized-Maxwell-Slip (GMS). Parameters of a Harmonic Drive CSD 25 gear are experimentally identified and the final model is evaluated on a dedicated test-bed. We show the integration and effectiveness in dynamic simulation, friction compensation, and external torque estimation