Robot-assisted rehabilitation therapy has been proven to effectively improve upper limb motor function and daily behavior of patients with motor dysfunction, and the demand has increased at every stage of the rehabilitation recovery. According to the motor relearning program theory, upper limb motor dysfunction can be restored by a certain amount of repetitive training. Robotics devices can be an approach to accelerate the rehabilitation process by maximizing the patients’ training intensity. This paper develops a new end-effector upper limb rehabilitation robot (EULRR) first and then presents a controller that is suitable for the assist-as-needed (AAN) training of the patients when performing the rehabilitation training. The AAN controller is a strategy that helps the patient’s arm to stay close to the given trajectory while allowing for spatial freedom. This controller enables the patient’s arm to have spatial freedom by constructing a virtual channel around the predetermined training trajectory. Patients could move their arm freely in the allowed virtual channel during rehabilitation training while the robot provides assistance when deviating from the virtual channel. The AAN controller is preliminarily tested with a healthy male subject in different conditions based on the EULRR. The experimental results demonstrate that the proposed AAN controller could provide assistance when moving out of the virtual channel and provide no assistance when moving along the trajectory within the virtual channel. In the close future, the controller is planned to be used in elderly volunteers and help to increase the intensity of the rehabilitation therapy by assisting the arm movement and by provoking active participation.

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.

Clinical studies have demonstrated that robot-involved therapy can effectively improve the rehabilitation training effect of motor ability and daily behavior ability of subjects with an upper limb motor dysfunction. This paper presents an impedance-based assist-as-needed controller that can be used in robot-aided rehabilitation training for subjects with an upper extremity dysfunction. Then, the controller is implemented on an end-effector upper extremity rehabilitation robot which could assist subjects in performing training with a spatial trajectory. The proposed controller enables subjects’ arms to have motion freedom by building a fault-tolerant region around the rehabilitation trajectory. Subjects could move their upper limb without any assistance within the fault-tolerant region while the robot would provide assistance according to the subjects’ functional ability when deviating from the fault-tolerant region. Besides, we also put forward the stiffness field around the fault-tolerant region to increase the robot’s assistance when subjects’ hand is moving outside the fault-tolerant region. A series of columnar rigid walls would be constructed in the controller according to the subjects’ functional ability, and the stiffness of the wall increases as the motion performance deteriorates. Furthermore, the controller contains five adjustable parameters. The controller would show different performances by adjusting these parameters and satisfy the requirement of robot-aided rehabilitation training at different rehabilitation stages such as passive, assistant, active, and resistant training. Finally, the controller was tested with an elderly female participant with different controller parameters, and experimental results verified the correctness of the controller and its potential ability to satisfy the training requirements at different rehabilitation stages. In the close future, the proposed controller in this work is planned to be applied on more subjects and also patients who have upper limb motor dysfunctions to demonstrate performance of the controller with different parameters.

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.