In this paper, we propose a distributed strategy for the estimation of the kinematic and inertial parameters of an unknown body manipulated by a team of mobile robots. We assume that each robot can measure its own velocity, as well as the contact forces exerted during the body manipulation, but neither the accelerations nor the positions of the contact points are directly accessible. Through kinematics and dynamics arguments, the relative positions of the contact points are estimated in a distributed fashion, and an observability condition is defined. Then, the inertial parameters (i.e., mass, relative position of the center of mass and moment of inertia) are estimated using distributed estimation filters and a nonlinear observer in cooperation with suitable control actions that ensure the observability of the parameters. Finally, we provide numerical simulations that corroborate our theoretical analysis.
Distributed Estimation of the Inertial Parameters of an Unknown Load via Multi-Robot Manipulation / Franchi, A; Petitti, A; Rizzo, Alessandro. - (2014), pp. 6111-6116. (Intervento presentato al convegno IEEE 53rd Annual Conference on Decision and Control, CDC 2014 tenutosi a Los Angeles, CA, USA nel December 15-17, 2014) [10.1109/CDC.2014.7040346].
Distributed Estimation of the Inertial Parameters of an Unknown Load via Multi-Robot Manipulation
RIZZO, Alessandro
2014-01-01
Abstract
In this paper, we propose a distributed strategy for the estimation of the kinematic and inertial parameters of an unknown body manipulated by a team of mobile robots. We assume that each robot can measure its own velocity, as well as the contact forces exerted during the body manipulation, but neither the accelerations nor the positions of the contact points are directly accessible. Through kinematics and dynamics arguments, the relative positions of the contact points are estimated in a distributed fashion, and an observability condition is defined. Then, the inertial parameters (i.e., mass, relative position of the center of mass and moment of inertia) are estimated using distributed estimation filters and a nonlinear observer in cooperation with suitable control actions that ensure the observability of the parameters. Finally, we provide numerical simulations that corroborate our theoretical analysis.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.