Robust Design of Parameter Identification

International audienceQuality of results computed during parameter identification problems relies on the selection of system's states while performing measurements. This choice usually does not take into account the uncertainty of states and of measures. For identifiability, classical methods focus only on the contribution of model errors on the uncertainty of parameters. We present an alternative approach that tackles this drawback: taking into account influence of all uncertainty sources in order to improve parameter identification robustness to uncertainties. A robotic application example that showcases the differences between approaches is developed as well

Application of hand-eye coordination in reaching with a humanoid

Reaching is the prerequisite for all kinds of physical manipulation by which robots interact with the physical world. In this research project, we try to build an application on the iCub humanoid platform to reach visually seen objects located in workspace. We also investigate humans’ ability to reach to objects using visual feedback and intend for the application to reproduce human-like behavior with a simplified model. This application includes fine motor control, object recognition, spatial transformations, stereovision, gaze control, and hand-eye coordination. We also discuss different methods in both vision and control that we use in the application.Ope

Relative posture-based kinematic calibration of a 6-RSS parallel robot by optical coordinate measurement machine

In this article, a relative posture-based algorithm is proposed to solve the kinematic calibration problem of a 6-RSS parallel robot using the optical coordinate measurement machine system. In the research, the relative posture of robot is estimated using the detected pose with respect to the sensor frame through several reflectors which are fixed on the robot end-effector. Based on the relative posture, a calibration algorithm is proposed to determine the optimal error parameters of the robot kinematic model and external parameters introduced by the optical sensor. This method considers both the position and orientation variations and does not need the accurate location information of the detection sensor. The simulation results validate the superiority of the algorithm by comparing with the classic implicit calibration method. And the experimental results demonstrate that the proposal relative posture-based algorithm using optical coordinate measurement machine is an implementable and effective method for the parallel robot calibration

Volumetric error compensation for industrial robots and machine tools

“A more efficient and increasingly popular volumetric error compensation method for machine tools is to compute compensation tables in axis space with tool tip volumetric measurements. However, machine tools have high-order geometric errors and some workspace is not reachable by measurement devices, the compensation method suffers a curve-fitting challenge, overfitting measurements in measured space and losing accuracy around and out of the measured space. Paper I presents a novel method that aims to uniformly interpolate and extrapolate the compensation tables throughout the entire workspace. By using a uniform constraint to bound the tool tip error slopes, an optimal model with consistent compensation capability is constructed. In addition to machine tools, industrial robots, are also becoming popularly used in manufacturing field. However, typical robot volumetric error compensation methods only consider constant errors such as link length and assembly errors while neglecting complicated kinematic errors such as strain wave gearing and out of rotating plane errors. Paper II presents a high-order joint-dependent model which describes both simple and complicated robot kinematic errors. A laser tracker with advantages of rapid data collection and a self-oriented position retroreflector are used for data collection. The experimental results show that nearly 20% of the robot kinematic errors are joint-dependent which are successfully captured by the proposed method. Paper III continues using the high-order joint-dependent robot error model while utilizing a new retroreflector with the ability of measuring robot position and orientation information simultaneously. More than 60% of measurement time is saved. Both position and orientation accuracy are also further improved”--Abstract, page iv

Robot End Effector Tracking Using Predictive Multisensory Integration

We propose a biologically inspired model that enables a humanoid robot to learn how to track its end effector by integrating visual and proprioceptive cues as it interacts with the environment. A key novel feature of this model is the incorporation of sensorimotor prediction, where the robot predicts the sensory consequences of its current body motion as measured by proprioceptive feedback. The robot develops the ability to perform smooth pursuit-like eye movements to track its hand, both in the presence and absence of visual input, and to track exteroceptive visual motions. Our framework makes a number of advances over past work. First, our model does not require a fiducial marker to indicate the robot hand explicitly. Second, it does not require the forward kinematics of the robot arm to be known. Third, it does not depend upon pre-defined visual feature descriptors. These are learned during interaction with the environment. We demonstrate that the use of prediction in multisensory integration enables the agent to incorporate the information from proprioceptive and visual cues better. The proposed model has properties that are qualitatively similar to the characteristics of human eye-hand coordination

An Overview of Kinematic and Calibration Models Using Internal/External Sensors or Constraints to Improve the Behavior of Spatial Parallel Mechanisms

This paper presents an overview of the literature on kinematic and calibration models of parallel mechanisms, the influence of sensors in the mechanism accuracy and parallel mechanisms used as sensors. The most relevant classifications to obtain and solve kinematic models and to identify geometric and non-geometric parameters in the calibration of parallel robots are discussed, examining the advantages and disadvantages of each method, presenting new trends and identifying unsolved problems. This overview tries to answer and show the solutions developed by the most up-to-date research to some of the most frequent questions that appear in the modelling of a parallel mechanism, such as how to measure, the number of sensors and necessary configurations, the type and influence of errors or the number of necessary parameters