In this paper, a distributed scheme to allow a human operator to physically interact with a multi-manipulator system is devised. Manipulators are tightly connected to a rigid object and a human operator interacts with it to perform, for example, a cooperative transportation task. The strategy foresees two layers. The top layer is in charge of assigning a compliant behaviour to the object through an admittance model whose reference trajectory is dynamically adjusted to regulate the human-object interaction force. Moreover, since the parameters of the dynamic model of the human arm end-point are supposed to be time-varying and completely unknowns with unknown bounds, a robust adaptive control is envisaged in this layer. The output of this layer is a desired object trajectory which is tracked by the bottom layer. In detail, the latter resorts to a robust adaptive control strategy to both track the object trajectory and control the internal stresses exerted by the manipulators on the object which unavoidably arise due to dynamic and kinematic uncertainties and synchronization errors. Simulations involving a setup with three dual-arm Movo mobile robots corroborate the theoretical findings.
Lippi, M., Marino, A., Chiaverini, S. (2019). A distributed approach to human multi-robot physical interaction. In 2019 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2019) (pp.728-734). 345 E 47TH ST, NEW YORK, NY 10017 USA : IEEE [10.1109/SMC.2019.8914468].
A distributed approach to human multi-robot physical interaction
Lippi, M
;Chiaverini, S
2019-01-01
Abstract
In this paper, a distributed scheme to allow a human operator to physically interact with a multi-manipulator system is devised. Manipulators are tightly connected to a rigid object and a human operator interacts with it to perform, for example, a cooperative transportation task. The strategy foresees two layers. The top layer is in charge of assigning a compliant behaviour to the object through an admittance model whose reference trajectory is dynamically adjusted to regulate the human-object interaction force. Moreover, since the parameters of the dynamic model of the human arm end-point are supposed to be time-varying and completely unknowns with unknown bounds, a robust adaptive control is envisaged in this layer. The output of this layer is a desired object trajectory which is tracked by the bottom layer. In detail, the latter resorts to a robust adaptive control strategy to both track the object trajectory and control the internal stresses exerted by the manipulators on the object which unavoidably arise due to dynamic and kinematic uncertainties and synchronization errors. Simulations involving a setup with three dual-arm Movo mobile robots corroborate the theoretical findings.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.