Precise identification of dynamic models in robotics is essential to support dynamic simulations, control design, friction compensation, output torque estimation, etc. A longstanding challenge remains in the development and identification of friction models for robotic joints, given the numerous physical phenomena affecting the underlying friction dynamics which result into nonlinear characteristics and hysteresis behaviour in particular. These phenomena proof difficult to be modelled and captured accurately using physical analogies alone. This has motivated researchers to shift from physics-based to data-driven models. Currently, these methods are still limited in their ability to generalize effectively to typical industrial robot deployement, characterized by high- and low-velocity operations and frequent direction reversals. Empirical observations motivate the use of dynamic friction models but these remain particulary challenging to establish. To address the current limitations, we propose to account for unidentified dynamics in the robot joints using latent dynamic states. The friction model may then utilize both the dynamic robot state and additional information encoded in the latent state to evaluate the friction torque. We cast this stochastic and partially unsupervised identification problem as a standard probabilistic representation learning problem. In this work both the friction model and latent state dynamics are parametrized as neural networks and are integrated in the conventional lumped parameter dynamic robot model. The complete dynamics model is directly learned from the noisy encoder measurements in the robot joints. We use the Expectation-Maximisation (EM) algorithm to find a Maximum Likelihood Estimate (MLE) of the model parameters. The effectiveness of the proposed method is validated in terms of open-loop prediction accuracy in comparison with baseline methods, using the Kuka KR6 R700 as a test platform.

Industrial robots are capable of moving at high speed. Each time they come into contact with their environment, e.g. to pick up an object, they decelerate to a near standstill. A solution involving a compliant pneumatic gripper and adapted trajectory plan is presented to initiate contact at a higher speed while remaining within hardware limits. By adding overload clutches in either the robot arm or gripper, tolerance to errors is provided. The key parameters such as gripper compliance and maximum allowed initial impact velocity are identified. Results show that by properly optimizing these parameters, robot picking of rigid objects can be accelerated. The complete high-speed picking solution is experimentally verified. A time reduction of 16% was obtained when making contact at 0.65 m/s.

Robots are more often deployed in unstructured or unpredictable environments. Particularly collisions at high speed can severely damage the drivetrains and joint bearings of robots. In order to avoid such collisions, path planners exist that adapt the robot's original trajectory online if a collision hazard is detected. These methods require additional sensors such as cameras, are computationally costly and never flawless due to occlusions. Another approach is to incorporate a cost function that promotes collision tolerance while planning the initial trajectory. The resulting impact-aware path plan minimizes the chance of robot hardware damage if a collision would occur. Two algorithms are presented to assess collision tolerance in high-speed robots, taking into account factors such as robot pose, impact direction, and maximum intermittent loading of the gearboxes and bearings. The first algorithm is more general while the second assumes the presence of joint overload clutches that decouple upon impact. These algorithms are applied to plan an impact-aware path for a custom 6-axis series clutched actuated robot that serves as use case. Both for the case with and without clutches, a generic impact-aware plan is presented as well as at least one derived, heuristic alternative. Without clutches, trajectories that are perpendicular to the end effector flange were found to be desirable, as they allow the robot to mitigate the highest collision force without overloading the gearboxes or bearings. On the other hand, with clutches, trajectories that are parallel to the end effector flange were found to be more collision tolerant. The effect of impact direction was also experimentally validated using the custom 6-axis robot. Collisions at velocities up to 1.2 m/s were mitigated through the combination of impact-aware path planning and series clutched actuation.