The problem of real-time motion generation to a target with obstacle avoidance is considered. A second-order dynamical system having the target as a unique globally asymptotically stable equilibrium is modified in the presence of obstacles by an additive signal whose design is based on the prescribed performance control methodology. It is proved that obstacle avoidance and dynamical system stability are guaranteed and that the target remains asymptotically stable. Simulations are utilized to reveal the cases where the proposed scheme outperforms the modulated dynamical system and the constrained optimization priority framework. Experimental results further validate the theoretical findings.

In this article, a passive physical human–robot interaction (pHRI) controller is proposed to enhance pHRI performance in terms of precision, cognitive load, and user effort, in cases partial knowledge of the task is available. Partial knowledge refers to a subspace of $SE(3)$ determined by the desired task, generally mixing both position and orientation variables, and is mathematically approximated by parametric expressions. The proposed scheme, which utilizes the notion of virtual constraints and the prescribed performance control methodology, is proved to be passive with respect to the interaction force, while guaranteeing constraint satisfaction in all cases. The control scheme is experimentally validated and compared with a dissipative control scheme utilizing a KUKA LWR4+ robot in a master–slave task; experiments also include an application to a robotic assembly case.

In this work, we propose a novel framework for generalizing a desired trajectory pattern, encoded using Dynamic Movement Primitives (DMP), subject to kinematic constraints. DMP have been extensively used in robotics for encoding and reproducing kinematic behaviours, thanks to their generalization, stability and robustness properties. However, incorporating kinematic constraints has not yet been fully addressed. To this end, we design an optimization framework, based on the DMP formulation from our previous work, for generalizing trajectory patterns, encoded with DMP subject to kinematic constraints, considering also time-varying target and time duration, via-point and obstacle constraints. Simulations highlight these properties and comparisons are drawn with other approaches for enforcing constraints on DMP. The usefulness and applicability of the proposed framework is showcased in experimental scenarios, including a handover, where the target and time duration vary, and placing scenarios, where obstacles are dynamically introduced in the scene.