Although integrated joint torque sensors in robots dispel the need for external force/torque sensors at the wrist to measure interactions, an inherent challenge is that they also measure the robot's intrinsic dynamics. This is especially problematic for delicate robot manipulation tasks, where interaction forces may be comparable to the robot intrinsic dynamics. Therefore, the intrinsic dynamics must first be experimentally estimated under no-load conditions, when the measurement only consists of torques due to the transmission of the robot actuator, before external interactions may be measured.
View Article and Find Full Text PDFIn modern logistics, the box-in-box insertion task is representative of a wide range of packaging applications, and automating compliant object insertion is difficult due to challenges in modelling the object deformation during insertion. Using Learning from Demonstration (LfD) paradigms, which are frequently used in robotics to facilitate skill transfer from humans to robots, can be one solution for complex tasks that are difficult to mathematically model. In order to automate the box-in-box insertion task for packaging applications, this study makes use of LfD techniques.
View Article and Find Full Text PDFAccurate kinematic modelling is pivotal in the safe and reliable execution of both contact and non-contact robotic applications. The kinematic models provided by robot manufacturers are valid only under ideal conditions and it is necessary to account for the manufacturing errors, particularly the joint offsets introduced during the assembling stages, which is identified as the underlying problem for position inaccuracy in more than 90% of the situations. This work was motivated by a very practical need, namely the discrepancy in terms of end-effector kinematics as computed by factory-calibrated internal controller and the nominal kinematic model as per robot datasheet.
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