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This paper aims to propose a novel method to identify the parameters of robotic manipulators using the torque exerted by the robot joint motors (measured by current sensors).

Previous studies used additional sensors like force sensor and inertia measurement unit, or additional payload mounted on the end-effector to perform parameter identification. The settings of these previous works were complicated. They could only identify part of the parameters. This paper uses the torque exerted by each joint while performing Fourier periodic excited trajectories. It divides the parameters into a linear part and a non-linear part, and uses linear least square (LLS) parameter estimation and dual-swarm-based particle swarm optimization (DPso) to compute the linear and non-linear parts, respectively.

The settings are simpler and can identify the dynamic parameters, the viscous friction coefficients and the Coulomb friction coefficients of two joints at the same time. A SIASUN 7-Axis Flexible Robot is used to experimentally validate the proposal. Comparison between the predicted torque values and ground-truth values of the joints confirms the effectiveness of the method.

The proposed method identifies two joints at the same time with satisfying precision and high efficiency. The identification errors of joints do not accumulate.

This paper aims to improve the accuracy of robot payload identification and decrease the complexity in its industrial application by developing a new method based on the actuator current.

Instead of previous general robot dynamic modeling of the actuators, links, together with payload inertial parameters, the paper discovers that the difference of the actuator torque between the robot moving along the same trajectory with and without carrying payload can be described as a function of the payload inertial parameters directly. Then a direct dynamic identification model of payload is built, a set of specialized novel exciting trajectories are designed for accurate identification and the least square method is applied for the estimation of the load parameters.

The experiments confirm the effectiveness of the proposed method in robot payload identification. The identification accuracy is greatly improved compared with that of existing methods based on the actuator current and is close to the accuracy of the methods that direct use the wrist-mounted force-torque sensor.

As the provided experiments indicate, the proposed method expands the application range and greatly improves the accuracy, hence making payload identification fully operational in the industrial application.

The novelty of such an identification method is that it does not require the rotor inertias and inertial parameters of links as a prior knowledge, and the specially designed trajectories provide completed decoupling of the load parameters.

A minimum‐time path‐following algorithm for industrial robots is presented in this paper.

The algorithm generates off‐line a trajectory that, by exploiting knowledge of the dynamic model, takes into account the actuators' torque limits while preserving the geometric path.

The algorithm has been designed, implemented and extensively tested on a Comau SMART H4 robot, a closed‐chain six‐degree‐of‐freedom industrial manipulator.

The algorithm is currently part of the new generation of industrial controllers of the Comau robots, the C4G controller. It is a feature added as with the name SmartMove4.

The paper presents a new minimum‐time path‐following algorithm for industrial robots that, by exploiting knowledge of the dynamic model, takes into account the actuators' torque limits while preserving the geometric path.

This paper aims to investigate consumer behaviour in response to social norms under pay-what-you-want (PWYW) pricing. Specifically, it explores the critical role of social norms such as norm priming and consumer prior trust in the retailer on consumers’ perceived price fairness, trust, willingness to pay, purchase intentions and intentions to spread negative word of mouth about the retailer.

Data on dependent measures were collected through the scenario-based online experimental approach and assessed using MANOVA analysis.

Results confirm the significance of norms by indicating the critical role of norm belief on consumer responses. Also, increasing the salience of norms by priming them usually intensifies negative behaviour, and pre-existing trust in the retailer serves as an imperfect cushion against consumer negative reactions to norm violation, but this effect is observed to be decreasing with increase in prior trust.

Further research should consider the contextual (time, place, media) influences and assumptions to increase the generalizability of the findings.

To the best of the authors’ knowledge, this is the first paper to explicitly examine the effects of social-norm compliance by sellers on consumer behaviour in the context of PWYW pricing.