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Manipulation is a key capability in domestic service robots, as can be seenin the rulebooks of last Robocup@Home editions. Currently, object recognitionis performed based mostly on visual information. Some robots use also 3Dinformation such as point clouds or laser scans but, to the knowledge ofauthors, robots don't use physical properties to improve object recognition.Estimation of an object's weight during a manipulation task is something new inthe @Home league and such ability can improve performance of domestic servicerobots. In this work we propose to estimate the weight of the grasped objectusing Sliding Mode Observers. If we consider the manipulator without load asthe nominal system and object's weight as a fault signal, we can estimate suchweight by an appropriate filtering of the output error injection term of thesliding mode observer. To implement our proposal we used MATLAB and SimulinkRobotics System Toolbox, ROS Toolbox and Simscape. To improve computation timewe exported all algorithms to standalone ROS nodes from Simulink models. Testswere performed using two platforms: Justina's left manipulator (a robotdeveloped at Biorobotics Laboratory, UNAM) and Neuronics Katana manipulators.We present results in simulation and discuss the performance of the proposedsystem and the possible sources of error. Finally we present our conclusionsand state the future work.

Mass Estimation in Manipulation Tasks of Domestic Service Robots using Fault Reconstruction Techniques