This paper presents a modified resolved acceleration control scheme based on deep regression of the convolutional neural network. The resolved acceleration control scheme can achieve precise motion control of robot manipulators by regulating the accelerations of the end-effector, and the conventional scheme needs the position and orientation of the end-effector, which are obtained through the direct kinematics of the robot manipulator. This scheme increases the computational loads and might obtain inaccurate position and orientation due to mechanical errors. To overcome the drawbacks, a camera is used to capture the images of the robot manipulator, and then a deep regression of convolutional neural network is imposed into the resolved acceleration control to obtain the position and orientation of the end-effector. The proposed approach aims to enhance the positioning accuracy, to reduce the computational loads, and to facilitate the deep regression in real-time control. In this study, the proposed approach is applied to a 3-DOF planar parallel robot manipulator, and the results are compared with those by the conventional resolved acceleration control and a visual servo-based control. The results show that those objectives are achieved. Furthermore, the robustness of the proposed approach is tested through only the partial image of the end-effector available, and the proposed approach still works functionally and effectively.

This paper proposes the visual servoing resolved acceleration control scheme applied to a 3-degree of freedom (DOF) planar manipulator, which aims to reduce computational loads of the feedback control loop and the tracking errors of the end-effector in the conventional resolved acceleration control scheme. The computation loads are increased due to the calculations of the direct kinematics and the velocity inversion in the feedback control loop. The tracking errors of the end-effector are increased due to low-accuracy encoders, gearbox backlashes, and manipulator flexibility, etc. The proposed scheme incorporates a visual system, which directly captures the position of the end-effector instead of calculating the direct kinematics and the velocity inversion. Due to the requirement of inverse dynamics in the proposed scheme, the transformation of the Jacobian matrices between the passive and active links is imposed to the Euler-Lagrange's equation so as to derive dynamic equations. In order to show the control performances, three existing torque-based control schemes are also applied to the manipulator. This study investigates the numerical simulations and the experimental validations, and the results show that the proposed scheme can reduce the computation loads and the tracking errors of the end-effector to compare with the three existing schemes.

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Increasing aging population lies great pressure on our country, and the health monitoring of the elderly arises as a very urgent problem to be solved. The elderly's body function is degraded in many aspects, and tumble, a problem elderly people most often encounter, brings unpredictable and serious injury or even leads to life risk behavior to them. In order to reduce the injury caused by falls, it is necessary to design protective devices that conform to the characteristics of the elderly. In this paper, by searching the current status and possible trend of aging population in China, we investigate the accident rate and harm caused by elderly tumble. Consider the head injury of the elderly as an example, a numerical simulation was conducted to analyze the mechanical mechanism of the head injury of the elderly falling. Combining the mechanical mechanism of the head injury, we compared several protective materials and selected the one that optimized our goal of head protection. Therefore, we designed one head-wearing buffering protective device, which is of great significance to the head injury prevention of the elderly falling.