This article introduces a wall-climbing robot that uses the reverse thrust of the propeller as the adsorption force. The robot is symmetrically distributed in structure and the adsorption force is symmetrically distributed before and after so that it can adapt to the surface of a variety of different media materials and achieve stable adsorption and movement of a variety of wall surfaces. The robot mainly uses the reverse thrust of the aircraft propeller as the adsorption force to achieve wall adsorption. The robot relies on four wheels to move forward. The forward power mainly comes from the combined action of the propeller reverse thrust component and the front wheel driving force. During the movement of the robot, the steering is realized by the front wheel differential control. In this paper, we design the structure of a dual-propeller dynamic adsorption wall mobile robot, plan the movement process of the robot from the ground to the wall, analyze the stable adsorption conditions of the robot wall, and carry out the robot’s motion performance and adaptability test under different ground/wall environments to verify that the robot is stable and feasible.

The differential drive two-wheeled inverted pendulum mobile robot has advantage of high maneuverability on slopes and in narrow spaces. It can be used to carry human beings as well as other goods. This paper mainly discusses mechanical design and dynamic modeling of this type of mobile robot platform. The first prototype is 12 kg of weight with dimensions of 42x36x15 cm. A multi modal control system is developed which includes the human transport control system and goods transport control system mode. An embedded control system is implemented to control two motors for both modes. In human transport mode the master controller receives the data from the data conditioning module and generates the reference for PID controllers whereas in goods transport mode the master controller receives command from the teleoperator. The effectiveness of the developed mobile robot is confirmed by experiments such as climbing slope, pivot around and surpassing small obstacles.

SUMMARY: A robot has been developed to maintain boiler water-cooling tubes. This robot has a double tracked moving mechanism, an ash cleaning device, a slag purging device, a tubes' thickness measurement device, a marking device, and a control system. This robot can climb up and down the tube wall. A method for the robot to complete many tasks for boiler maintenance in one process cycle is presented. The mechanism of the robot is described. Especially, a kind of special magnetic block structure is designed to obtain strong adhering force using permanent magnets. Experiments in laboratory and real thermal power station have verified that the robot cannot only climb on the surface of the tube wall smoothly, but also take heavy payloads for boiler maintenance operation.

The research on rehabilitation robots is gradually moving toward combining human intention recognition with control strategies to stimulate user involvement. In order to enhance the interactive performance between the robot and the human body, we propose a machine-learning-based human motion intention recognition algorithm using sensor information such as force, displacement and wheel speed. The proposed system uses the bi-directional long short-term memory (BILSTM) algorithm to recognize actions such as falling, walking, and turning, of which the accuracy rate has reached 99.61%. In addition, a radial basis function neural network adaptive sliding mode controller (RBFNNASMC) is proposed to track and control the patient’s behavioral intention and the gait of the lower limb exoskeleton and to adjust the weights of the RBF network using the adaptive law. This can achieve a dynamic estimation of the human–robot interaction forces and external disturbances, and it gives the exoskeleton joint motor a suitable driving torque. The stability of the controller is demonstrated using the Lyapunov stability theory. Finally, the experimental results demonstrate that the BILSTM classifier has more accurate recognition than the conventional classifier, and the real-time performance can meet the demand of the control cycle. Meanwhile, the RBFNNASMC controller has a better gait tracking effect compared with the PID controller.

Robot-aided training strategies that allow functional, assist-as-needed, or challenging training have been widely explored. Accurate activity recognition is the basis for implementing alternative training strategies. However, some obstacles to accurate recognition exist. First, scientists do not yet fully understand some rehabilitation activities, such as abnormal gaits and falls; thus, there is no standardized feature for identifying such activities. Second, during the activity identification process, it is difficult to reasonably balance sensitivity and specificity when setting the threshold. Therefore, we proposed a multisensor fusion system and a two-stage activity recognition classifier. This multisensor system integrates explicit information such as kinematics and spatial distribution information along with implicit information such as kinetics and pulse information. Both the explicit and implicit information are analyzed in one discriminant function to obtain a detailed and accurate recognition result. Then, alternative training strategies can be implemented on this basis. Finally, we conducted experiments to verify the feasibility and efficiency of the multisensor fusion system. The experimental results show that the proposed fusion system achieves an accuracy of 99.37%, and the time required to prejudge a fall is approximately 205 milliseconds faster than the response time of single-sensor systems. Moreover, the proposed system also identifies fall directions and abnormal gait types.

In response to the current problem of low intelligence of mobile lower limb motor rehabilitation aids. This paper proposes an intelligent control scheme based on human movement behavior in order to control the rehabilitation robot to follow the patient’s movement. Firstly, a multi-sensor data acquisition system is designed according to the rehabilitation needs of the patient and the movement characteristics of the human body. A mathematical model of movement behavior is then established. By analyzing and processing motion data, the change in the center of gravity of the human body and the behavior intention signal are derived and used as a control command for the robot to follow the human body’s movement. Secondly, in order to improve the control effect of rehabilitation robot following human motion, an adaptive radial basis function neural network sliding mode controller (ARBFNNSMC) is designed based on the robot dynamic model. The adaptive adjustment of switching gain coefficient is performed by radial basis function neural network. The controller can overcome the influence caused by the change of robot control system parameters due to the fluctuation of the center of gravity of human body, enhance the adaptability of the system to other disturbance factors, and improve the accuracy of following human body motion. Finally, the motion following experiment of the rehabilitation robot is performed. The experimental results show that the robot can recognize the motion intention of human body and perform the training goal of following different subjects to complete straight lines and curves. The correctness of human motion behavior model and robot control algorithm is verified, which shows the feasibility of the intelligent control method proposed in this paper.