Summary. Human-Robot Interaction (HRI) brings new challenges to robotics. We focus in this paper on the decisional issues of HRI enabled robots. We propose a control architecture specifically designed for HRI and present an implemented system that illustrates its main components and their interaction. These components provide integrated abilities to support human-robot collaborative task achievement as well as capacities to elaborate task plans involving humans and robots and to produce legible and socially acceptable behavior.

No abstract

To improve the accuracy of permanent magnet (PM) rotor position estimation for a hybrid stepper motor (HSM), the authors propose a sensorless control design based on the back electromagnetic force (back-EMF) caused by magnetic induction and harmonic rejection via an orthogonal third-order phase-locked loop (PLL 3 rd) and an integral harmonic filter (IHF). The accuracy of estimation is analysed considering PM rotor position estimation errors. The harmonics of the back-EMF signal are eliminated by the IHF before entering the PLL 3 rd, which synchronises and decreases position estimation error. This technique is a simple, reliable, and effective method for implementing sensorless control of an HSM. An industrial test-bench was used to implement the proposed scheme. The experimental results are presented to validate the effectiveness of the proposed estimation method.

This paper proposes a robot calibration method that uses an extended Kalman filter (EKF) and a neural network based on Levenberg-Marquardt combined accelerated particle swarm optimization (LMAPSO) to improve the accuracy of the robot's absolute position. After the EKF optimizes all geometric parameters, the robot position still contains non-geometric errors due to joint clearance, gear backlash, and link deflection that are impossible to model. Therefore, an artificial neural network model (ANN) is designed to compensate for these un-modeled errors. The Levenberg-Marquardt combined accelerated particle swarm optimization (LMAPSO) provides a robust optimization search algorithm to optimize the weight and bias of the neural network based on the training set. An experiment on a five-bar parallel robot shows that geometric and non-geometric calibration reduced the maximum absolute position error from (1.548 to 0.045) mm. The experimental results demonstrate the proposed calibration method's effectiveness with the robot's absolute position accuracy improving by 98%.