Efficient and Robust Modeling of Nonlinear Mechanical Systems

The development of efficient and robust dynamic models is fundamental in the field of systems and control engineering. In this paper, a new formulation for the dynamic model of nonlinear mechanical systems, that can be applied to different automotive and robotic case studies, is proposed, together with a modeling procedure allowing to automatically obtain the model formulation. Compared with the Euler-Lagrange formulation, the proposed model is shown to give superior performances in terms of robustness against measurement noise for systems exhibiting dependence on some external variables, as well as in terms of execution time when computing the inverse dynamics of the system.

On the Analysis of Stability, Sensitivity and Transparency in Variable Admittance Control for pHRI Enhanced by Virtual Fixtures

The interest in Physical Human-Robot Interaction (pHRI) has significantly increased over the last two decades thanks to the availability of collaborative robots that guarantee user safety during force exchanges. For this reason, stability concerns have been addressed extensively in the literature while proposing new control schemes for pHRI applications. Because of the nonlinear nature of robots, stability analyses generally leverage passivity concepts. On the other hand, the proposed algorithms generally consider ideal models of robot manipulators. For this reason, the primary objective of this paper is to conduct a detailed analysis of the sources of instability for a class of pHRI control schemes, namely proxy-based constrained admittance controllers, by considering parasitic effects such as transmission elasticity, motor velocity saturation, and actuation delay. Next, a sensitivity analysis supported by experimental results is carried out, in order to identify how the control parameters affect the stability of the overall system. Finally, an adaptation technique for the proxy parameters is proposed with the goal of maximizing transparency in pHRI. The proposed adaptation method is validated through both simulations and experimental tests.

Arc-Length-Based Warping for Robot Skill Synthesis from Multiple Demonstrations

In robotics, Learning from Demonstration (LfD) aims to transfer skills to robots by using multiple demonstrations of the same task. These demonstrations are recorded and processed to extract a consistent skill representation. This process typically requires temporal alignment through techniques such as Dynamic Time Warping (DTW). In this paper, we introduce a novel algorithm, named Spatial Sampling (SS), specifically designed for robot trajectories, that enables time-independent alignment of the trajectories by providing an arc-length parametrization of the signals. This approach eliminates the need for temporal alignment, enhancing the accuracy and robustness of skill representation. Specifically, we show that large time shifts in the demonstrated trajectories can introduce uncertainties in the synthesis of the final trajectory, which alignment in the arc-length domain can drastically reduce, in comparison with various state-of-the-art time-based signal alignment algorithms. To this end, we built a custom publicly available dataset of robot recordings to test real-world trajectories.

Phase-free Dynamic Movement Primitives Applied to Kinesthetic Guidance in Robotic Co-manipulation Tasks

When there is a need to define and adapt a robotic task based on a reference motion, Dynamic Movement Primitives (DMP) is a standard and efficient method for encoding it. The nominal trajectory is typically obtained through a Programming by Demonstration (PbD) approach, where the robot is taught a specific task through kinesthetic guidance. Subsequently, the motion is reproduced by the manipulator in terms of both geometric path and timing law. The basic approach for modifying the duration of the execution involves adjusting a time constant characterizing the model. On the contrary, the goal of this paper is to achieve complete decoupling between the geometric information of the task, encoded into the DMP, and the phase law governing the execution, allowing them to be chosen independently. This enables the optimization of the task duration to satisfy constraints such as velocity or acceleration or even to define a phase law dependent on external inputs, such as the force applied by a user in a co-manipulation task. As an example, this mechanism will be exploited to define a rehabilitation activity where the cobot assists humans in performing various pre-planned exercises.