The objective of this paper is to devise a general framework to allow a human operator to physically interact with an object manipulated by a multi-manipulator system in a distributed setting. A two layer solution is devised. In detail, at the top layer an arbitrary virtual dynamics is considered for the object with the virtual input chosen as the solution of an optimal Linear Quadratic Tracking (LQT) problem. In this formulation, both the human and robots' intentions are taken into account, being the former online estimated by Recursive Least Squares (RLS) technique. The output of this layer is a desired trajectory of the object which is the input of the bottom layer and from which desired trajectories for the robot end effectors are computed based on the closed-chain constraints. Each robot, then, implements a time-varying gain adaptive control law so as to take into account model uncertainty and internal wrenches that inevitably raise due to synchronization errors and dynamic and kinematic uncertainties. Remarkably, the overall solution is devised in a distributed setting by resorting to a leader-follower approach and distributed observers. Simulations with three 6-DOFs serial chain manipulators mounted on mobile platforms corroborate the theoretical findings.

Lippi, M., Marino, A. (2021). Human Multi-Robot Physical Interaction: a Distributed Framework. JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, 101(2) [10.1007/s10846-020-01277-y].

Human Multi-Robot Physical Interaction: a Distributed Framework

Martina Lippi
;
2021

Abstract

The objective of this paper is to devise a general framework to allow a human operator to physically interact with an object manipulated by a multi-manipulator system in a distributed setting. A two layer solution is devised. In detail, at the top layer an arbitrary virtual dynamics is considered for the object with the virtual input chosen as the solution of an optimal Linear Quadratic Tracking (LQT) problem. In this formulation, both the human and robots' intentions are taken into account, being the former online estimated by Recursive Least Squares (RLS) technique. The output of this layer is a desired trajectory of the object which is the input of the bottom layer and from which desired trajectories for the robot end effectors are computed based on the closed-chain constraints. Each robot, then, implements a time-varying gain adaptive control law so as to take into account model uncertainty and internal wrenches that inevitably raise due to synchronization errors and dynamic and kinematic uncertainties. Remarkably, the overall solution is devised in a distributed setting by resorting to a leader-follower approach and distributed observers. Simulations with three 6-DOFs serial chain manipulators mounted on mobile platforms corroborate the theoretical findings.
Lippi, M., Marino, A. (2021). Human Multi-Robot Physical Interaction: a Distributed Framework. JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, 101(2) [10.1007/s10846-020-01277-y].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/423030
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