This paper presents a novel approach for time-optimal path parameterization based on reachability analysis for robotic systems with viscous friction in the dynamics and jerk constraints. The main step of the method is the backward propagation of controllable sets through a linear second-order system. In order to avoid the unbounded growth of the number of constraints, the sets are approximated by a ray shooting algorithm. Using a convex relaxation, the required set expansion can be solved with second-order cone programming. Evaluation results for a 6-degree of freedom (DOF) robot arm highlight the advantages of the method for computing jerk-limited trajectories.

This paper describes our work towards preliminary evaluation of LTE/5G cellular communication for robotics motion planning and control. The work comprises the development of communication architecture, motion planning and a demonstrator that validates the feasibility of the proposed approach. In the demonstrated use case, a stationary robot arm picks and places workpieces from/onto a moving mobile platform while relying only on the mobile platform's localization measurements communicated over LTE connection and not using any external sensors. We describe the approach, obtained results, shortcomings, and promising future directions.

Dynamic Movement Primitives (DMPs) are a well-known tool for encoding robotic motions. Their popularity stems from invariance properties in time and space, the ability to describe complex coordinated motions in multiple degrees of freedom with a relatively small number of parameters, and the linearity in the parameters that describe the motion. The latter allows easily fitting a DMP to motions e.g. demonstrated by a human. DMPs are at their core second order autonomous differential equations. However, feedforward controls of robots with flexible joints are known to require reference trajectories up to the fourth derivative of position. Consequently, classical DMPs are mechanically not compatible with flexible joint robots. In this paper, we propose an extension of DMPs by introducing FlexDMPs. This concept retains the structural properties and benefits of classical DMPs but generates trajectories up to the fourth derivative that can theoretically be tracked ideally (i.e. with zero tracking error) by flexible joint robots. The concept is demonstrated on a high fidelity simulation model of an industrial robot and in experimental results on a collaborative manipulator.

Hands-on training is an indispensable part of surgical practice. As the tools used in the operating room become more intricate, the demand for efficient training methods increases. This work proposes a robotic assistance-as-needed method for training with surgical teleoperated robots. The method adapts the intensity of the assistance according to the trainee's current and past performance while gradually increasing the level of control of the trainee as the training progresses. The work includes an experiment comprising 160 acquisition sessions from 16 novice subjects performing a bimanual teleoperated exercise with a da Vinci Research Kit surgical console. Results capture the subtleties in the task's learning curve with and without robotic assistance and hint at the potential of robotic assistance for complex visuomotor training. Although robotic assistance for motor learning has received mixed results that range from beneficial to detrimental effects, this study shows such assistance may increase the rate of learning of certain skills in complex motor tasks.

Teleoperation systems could benefit from force sensory substitution when kinesthetic force feedback systems are too bulky or expensive, and when they cause instability by magnifying force feedback. We aim to magnify force feedback using sensory substitution via force-controlled tactile skin deformation, using a device with the ability to provide tangential and normal force directly to the fingerpads. The sensory substitution device is able to provide skin deformation force feedback over ten times the maximum stable kinesthetic force feedback on a da Vinci Research Kit teleoperation system. We evaluated the effect of this force magnification in two experimental tasks where the goal was to minimize interaction force with the environment. In a peg transfer task, magnified force feedback using sensory substitution improved participants' performance for force magnifications up to ten times, but decreased performance for higher force magnifications. In a tube connection task, sensory substitution that doubled the force feedback maximized performance; there was no improvement at the larger magnifications. These experiments demonstrate that magnified force feedback using sensory substitution via force-controlled skin deformation feedback can decrease applied forces similarly to magnified kinesthetic force feedback during teleoperation.

Haptically enabled hands-on or tele-operated surgical robotic systems provide a unique opportunity to integrate pre- and intra-operative information into physical actions through active constraints (also known as virtual fixtures). In many surgical procedures, including cardiac interventions, where physiological motion complicates tissue manipulation, dynamic active constraints can improve the performance of the intervention in terms of safety and accuracy. The non-energy-storing class of dynamic guidance constraints attempt to assist the clinician in following a reference path, while guaranteeing that the control system will not generate undesired motion due to stored potential energy. An important aspect that has not received much attention from the researchers is that while these methods help increase the performance, they should by no means distract the user systematically. In this paper, a viscosity-based dynamic guidance constraint is introduced that continuously redirects the tool's motion towards the reference path. The proportionality and continuity of generated forces make the method less distracting and subjectively appealing. The performance is validated and compared with two existing non-energy-storing methods through extensive experimentation.