Matteo Parigi Polverini

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Enabling human-robot collaboration raises new challenges in safety-oriented robot design and control. Indices that quantitatively describe human injury due to a human-robot collision are needed to propose suitable pre-collision control strategies. This paper presents a novel model-based injury index built on the concept of dissipated kinetic energy in a(More)
This paper addresses the problem of collision avoidance in human-robot interaction. To this end, we introduce the concept of kinetostatic safety field, a novel safety assessment about the risk in the vicinity of a rigid body (including a robot link or a human body part). The safety field depends on the position and velocity of the body but it is also(More)
Classical control approaches to robot force control have been extensively addressed by research in the last decades and are now considered a paradigm when dealing with force control for industrial robots. With this respect, the present paper exploits the capability of state-of-the-art Quadratic Programming (QP) solvers to specify a simple and intuitive(More)
Fast and sensorless peg-in-hole insertion is a challenging task for a robotic manipulator. In order to deal with the peg-in-hole insertion problem without any need of an external force/torque sensor, this paper proposes to actively accomplish compliance in the insertion task relying on an admittance based control. This is combined with a real-time(More)
This paper addresses the problem of robot implicit force control by means of set invariance theory. The proposed control law improves the invariance control approach by avoiding the definition of a nominal stabilizing controller, while simultaneously ensuring the absence of output overshoots with respect to the reference. The resulting set invariance based(More)
Standard control design for robot implicit force control is a typical example of model-based regulator synthesis. This paper proposes a method to improve closed-loop performance of standard model-based controllers for robot implicit force control in terms of closed-loop model matching, between desired and achieved closed-loop behaviour. To this end, a(More)
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