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The characteristics of spherical robots, such as under-drive, non-holonomic constraints and strong coupling, make it difficult to establish its motion control model accurately. To improve the anti-interference performance of spherical robots in practical engineering, this paper proposes a spherical robot motion controller based on auto-disturbance rejection control (ADRC) with parameter tuning.

This paper considers the influences of the spherical shell, internal frame and pendulum on the movement of the spherical robot during the rotation to establish the multi-body dynamics model of the XK-I spherical robot. Due to the serious coupling problem of the dynamic model, the motion control state equation is constructed using linearization and decoupling. The XK-I spherical robot PSO-ADRC motion controller with parameter tuning function is designed by combining the state equation with the particle swarm optimization (PSO) algorithm. Finally, experiments are performed to evaluate the feasibility of PSO-ADRC in an actual case compared to ADRC, PSO-PID and PID.

By analyzing the required time to reach the expected value, the control stability and the fluctuation range of the standard deviation after reaching the expected value, the superiority of PSO-ADRC to ADRC, PSO-PID and PID is demonstrated in terms of the speed and anti-interference ability.

The proposed method can be applied to the robot control field.

A parameter-tuning method for auto-disturbance-rejection motion control of the spherical robot is proposed. According to the experimental results, the anti-interference ability of the spherical robot moving on uneven ground is improved. Therefore, it provides a foundation for the autonomous environmental monitoring of the spherical robot equipped with sensors.

This paper aims to present a method to improve the sensitive and low probabilities of false alarm of a manipulator in a human–robot interaction environment, which can improve the performance of the system owing to non-linear uncertainty in the model of the robot controller.

A novel collision detection method based on adaptive residual estimation is proposed, promoting the detection accuracy of the collision of the manipulator during operation. First, a general momentum residual estimator is designed to incorporate the non-linear factors of the manipulator (e.g. joint friction, speed and acceleration) into the residual-related uncertainty of the model. Second, model parameters are estimated through gradient correction. The residual filter is used to determine the dynamic threshold, resulting in higher detection accuracy. Finally, the performance of the residual estimation scheme is evaluated by comparing the dynamic threshold with residual in real-time experiments where a single Universal Robot 5 robot end–effector collides with the obstacle.

Experimental results demonstrate that the collision detection system can improve sensitivity and lead to low probabilities of false alarm of non-linear uncertainty in the model.

The method proposed in this article can be applied to industry and human–robot interaction area.

An adaptive collision detection method is proposed in this paper to address non-linear uncertainties of the model in industrial application.