Surface electromyography (sEMG) is a promising technology that can capture muscle activation signals to control robots through novel human–machine interfaces (HMIs). This technology has already been applied in scenarios such as prosthetic design, assisted robot control, and rehabilitation training. This article provides an overview of sEMG-based robot control, covering two important aspects: (1) sEMG signal processing and classification methods and (2) robot control strategies and methods based on sEMG. First, the article outlines the general steps in sEMG signal processing and summarizes the commonly used methods for data acquisition, pre-processing, and feature extraction. In addition, machine-learning-based pattern recognition methods have been introduced for sEMG signal classification. Subsequently, user intent-based robot control strategies are classified into three categories: full-human continuous control, semi-autonomous continuous control, and discrete control, and their control methods and applicable scenarios are compared. Finally, this article discusses the advantages, disadvantages, and future development prospects of sEMG-based robot control. This review provides a comprehensive overview of sEMG-based robot control, from signal processing and classification methods to robot control strategies and methods, aiming to guide future research on selecting filters, feature sets, and pattern recognition methods and to assist in establishing sEMG-driven robot control frameworks.

Robot positioning accuracy plays an important role in industrial automation applications. In this paper, a method is proposed for the improvement of robot accuracy with an optical tracking system that integrates a least-square numerical algorithm for the identification of kinematic parameters. In the process of establishing the system kinematics model, the positioning errors of the tool and the robot base, and the errors of the Denavit-Hartenberg parameters are all considered. In addition, the linear dependence among the parameters is analyzed. Numerical simulation based on a 6-axis UR robot is performed to validate the effectiveness of the proposed method. Then, the method is implemented on the actual robot, and the experimental results show that the robots can reach desired poses with an accuracy of ±0.35 mm for position and ±0.07° for orientation. Benefitting from the optical tracking system, the proposed procedure can be easily automated to improve the robot accuracy for applications requiring high positioning accuracy such as riveting, drill, and precise assembly.

A method is presented for tip-over stability analysis of a wheeled mobile manipulator. A wheeled mobile manipulator may tip over resulting from its operation. In this study, first a Newton–Euler formulation is applied to formulate the manipulator’s reaction forces and moments exerted onto the mobile platform. Tip-over criterion is derived to judge the system stability. Three load and motion analyses are carried on. The first static load deals with links and payload to show the effect of the horizontal position of the system’s center of gravity (CG). The second and third are the inertial forces resulting from joint speeds and accelerations, respectively. Case study is path planning with tip-over criterion result which can make the system stable along the path. The simulation results demonstrate the effectiveness of the proposed method.

A method for optimizing a mobile platform to form a wheeled manipulator is presented. For a given manipulator, this mobile platform is optimized to have maximum tip-over stability against the reaction forces and moments caused by the movement of the manipulator. This optimization is formulated as a max–min problem, i.e., to maximize a stable region ratio (SRR) over the manipulator's workspace while minimizing a tip-over moment (TOM). For a practical solution, this max–min problem is converted to two subproblems. The first one is the worst-case analysis to determine the maximum positive value of TOM through searching over the manipulator's workspace. A positive value of TOM indicates tip-over instability. The three parameters used for this search are pertaining to the mobile platform itself, i.e., the number of support wheels, the size, and mass of the mobile platform. The second subproblem is to optimize the placement of the manipulator and accessory on the mobile platform against the identified worst case so that the entire manipulator's workspace is stable. The effectiveness of the proposed method is demonstrated by applying it to optimize a mobile drilling and riveting robot.