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Friction is the result of complex interactions between contacting surfaces in a nanoscale perspective. Depending on the application, the dierent models available are more or less suitable. Available static friction models are typically considered to be dependent only on relative speed of interacting surfaces. However, it is known that friction can be aected by other factors than speed. In this work, static friction in robot joints is studied with respect to changes in joint angle, load torque and temperature. The eects of these variables are analyzed by means of experiments on a standard industrial robot. Justied by their signicance, load torque and temperature are included in an extended static friction model. The proposed model is validated in a wide operating range, reducing the average error a factor of 6 when compared to a standard static friction model. Abstract-Friction is the result of complex interactions between contacting surfaces in a nanoscale perspective. Depending on the application, the different models available are more or less suitable. Available static friction models are typically considered to be dependent only on relative speed of interacting surfaces. However, it is known that friction can be affected by other factors than speed.In this work, static friction in robot joints is studied with respect to changes in joint angle, load torque and temperature. The effects of these variables are analyzed by means of experiments on a standard industrial robot. Justified by their significance, load torque and temperature are included in an extended static friction model. The proposed model is validated in a wide operating range, reducing the average error a factor of 6 when compared to a standard static friction model.

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This paper considers the problem of wear estimation in a standard industrial robot joint. Motivated by the observation that the wear processes in a robot actuator cause an increase of the friction levels in the joint, static friction observations are used to infer the wear levels. An existing static friction model is extended to include the eects of wear, which are analyzed and modeled. The resulting model can explain friction in a robot joint with respect to changes in speed, load, temperature and wear. Based on this model and a test-cycle that generates static friction observations, a modelbased wear estimator is proposed. The performance of the estimator is found both by means of simulations and experiments on an industrial robot. As it is shown, the method can distinguish the eects of wear even under large temperature variations, opening up for the use of robust joint diagnosis for industrial robots. Abstract: This paper considers the problem of wear estimation in a standard industrial robot joint. Motivated by the observation that the wear processes in a robot actuator cause an increase of the friction levels in the joint, static friction observations are used to infer the wear levels.An existing static friction model is extended to include the effects of wear, which are analyzed and modeled. The resulting model can explain friction in a robot joint with respect to changes in speed, load, temperature and wear. Based on this model and a test-cycle that generates static friction observations, a model-based wear estimator is proposed. The performance of the estimator is found both by means of simulations and experiments on an industrial robot. As it is shown, the method can distinguish the effects of wear even under large temperature variations, opening up for the use of robust joint diagnosis for industrial robots.

One key competence for robot manufacturers is robot control, defined as all the technologies needed to control the electromechanical system of an industrial robot. By means of modeling, identification, optimization, and model-based control it is possible to reduce robot cost, increase robot performance, and solve requirements from new automation concepts and new application processes. Model-based control, including kinematics error compensation, optimal servo reference-and feed-forward generation, and servo design, tuning, and scheduling, has meant a breakthrough for the use of robots in industry. Relying on this breakthrough, new automation concepts such as high performance multi robot collaboration and human robot collaboration can be introduced. Robot manufacturers can build robots with more compliant components and mechanical structures without loosing performance and robots can be used also in applications with very high performance requirements, e.g., in assembly, machining, and laser cutting. In the future it is expected that the importance of sensor control will increase, both with respect to sensors in the robot structure to increase the control performance of the robot itself and sensors outside the robot related to the applications and the automation systems. In this connection sensor fusion and learning functionalities will be needed together with the robot control for easy and intuitive installation, programming, and maintenance of industrial robots.