This paper considers the practical implementation of a new maximum likelihood robot identification method, developed by Olsen andPetersen. In particular, the practical issue concerning the estimation of the joint velocities and accelerations from joint angle measurements, and its consequence on the parameter estimation and accuracy, is considered. Simulation and experimental results on a KUKA IR 361 industrial robot are discussed, and compared with models obtained using a much simpler weighted least squares method.
In this paper, we discuss a new experimental robot load identification method that is used in industry. The method is based on periodic robot excitation and the maximum likelihood estimation of the parameters, techniques adopted from Swevers et al. (1997 IEEE Transactions on Robotics and Automation 13(5):730-740). This method provides:(1) accurate estimates of the robot load inertial parameters; and (2) accurate actuator torque predictions. These are both essential for the acceptance of the results in an industrial environment. The key element to the success of this method is the comprehensiveness of the applied model, which includes, besides the dynamics resulting from the robot load and motor inertia, the coupling between the actuator torques, the mechanical losses in the motors and the efficiency of the transmissions. Accurate estimates of the robot link and motor inertial parameters, which can be considered identical for all robots of the same type, are obtained from separate experiments (see Swevers et al.), and used as a priori knowledge for the robot load identification. We present experimental results on a KUKA industrial robot equipped with a calibrated test load.
Abstract-This paper presents a unified task specification formalism and a unified control scheme for the lowest control level of sensor-based robot tasks. The formalism is based on: (i) the integration of any sensor that provides (direct or indirect) distance (and time derivatives) and force information; (ii) the possibility to use multiple "Tool Centre Points", e.g. defined relative to the robot end effector, other links or the environment; (iii) the integration of optimization functions for underconstrained as well as overconstrained specifications with linear constraints; (iv) the integration of on-line estimators; and (v) compatibility with all major lowlevel control approaches.The unified formalism applies to the whole range from industrial manipulators over cooperating robots to humanoid robots, and from pure position control tasks over industrial processes to interaction between a humanoid robot and its environment.
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