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.

The low absolute positioning accuracy of robots has limited their applications for many precision tasks. Although the robot accuracy can be improved through kinematic calibration, traditional calibration approaches need either expensive external measurement devices or tedious manual intervention. To overcome these issues, this work proposes a closed-loop kinematic calibration method for robots using a six-point measuring device. This device consists of six displacement sensors to determine the pose of a gauge block as a reference workpiece mounted on the robot end-effector. A comprehensive error model is derived to calibrate the kinematic parameters, including the Denavit–Hartenberg (D–H) parameters of the robot geometry and the pose of the robot base. Then, the Levenberg–Marquard (LM) algorithm is adopted to identify these parameters. An experimental study has been conducted on a UR10 robot, combining a coordinate measuring machine (CMM) to verify the calibration results. It is found that the maximum and average distance errors of the robot movement could be significantly decreased from over 5.60 and 2.50 mm to approximately 0.50 and 0.30 mm, respectively. Benefited from the fact that only six displacement sensors are used, the proposed method is cost-effective. Also, the proposed calibration process can be completed without tedious manual teaching and, thus, easy to achieve autonomous calibration.

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.

Although industrial robots are widely used in production automation, their applications in machining have been limited because of the structural vibrations induced by periodic cutting forces. Since the dynamic characteristics of an industrial robot depends on its configuration, the responses of the robot structure to the cutting forces are affected by how the workpiece is placed within the workspace of the robot. This paper presents a method for workpiece pose optimization for a robotic milling system to improve the quasi-static performance during machining. Since the milling forces are time-varying due to the characteristics of the multi-tooth and discontinuity of milling, these forces can excite vibrations inside the robot structure. To address this issue, a structural dynamics model is established for industrial robots, considering their joint flexibility, and a milling force formulation is incorporated into the robot dynamics model to investigate the forced vibrations of the flexible joints. Then, the quasi-static performance of the robotic machining system is evaluated by the vibration-induced offset of the cutter tool that is mounted on the end-effector. Finally, an optimization approach is given for the workpiece pose to minimize the cutter tool offset under the periodic milling force. A numerical simulation demonstrates that the optimal workpiece pose can significantly reduce the overall tool offset during machining and can lower the variation of the tool offset along the milling path.

This paper presents a dynamic analysis method for robotic integration of tooling systems. This development is motivated by the fact that many modern robotic automation tasks require large and heavy tooling systems. Yet, the integration of these tooling systems is usually done only considering the geometric constraints and weights without resorting to dynamic analysis. To resolve this problem, the equations of motion of a robot with inclusion of a tooling system are derived using the Lagrangian formulation. Three performance indices are introduced to evaluate the influence of the tooling system on the overall dynamics. The first index measures the energy consumption due to the tooling system’s motion, the second index evaluates the influence of the tooling system on the fundamental frequency, and the third one is the dynamic manipulability ellipsoid to measure the acceleration capability of the tool tip. Simulation studies are carried out to provide guidelines for the design of tooling systems. To demonstrate its effectiveness, the proposed method is applied to facilitate the tooling integration used in the robotic riveting for aerospace assembly.

This paper proposes a method for process parameter optimization of a mobile robotic percussive riveting system with flexible joints to guarantee the rivet gun alignment during the operation. This development is motivated by the increasing interest in using industrial robots to replace human operators for percussive impact riveting in aerospace assembly. In percussive riveting, the rivet gun generates repetitive impacts acting on the rivet. These impacts not only deform the rivet but also induce forced vibration to the robot, and thus the robot must hold the gun firmly during riveting. The process parameters for the mobile robotic riveting system include those related to the impact force generation for planning the rivet gun input and those related to the robot pose with respect to the joined panels for planning the mobile platform motion. These parameters are incorporated into a structural dynamic model of the robot under a periodic impact force. Then an approximate analytical solution is formulated to calculate the displacement of the rivet gun mounted on the end effector for its misalignment evaluation. It is found that both the force frequency and the mobile platform position have strong influence on the robotic riveting performance in terms of alignment during operation. Global optimization of these process parameters is carried out to demonstrate the practical application of the proposed method for the planning of the robotic percussive riveting system.