In this paper, we propose a robotic arm grasping system suitable for complex environments. For a robotic arm, in order to achieve its accurate grasp of the target object, not only the vision but also a certain tracking ability should be provided. To improve the grasp quality, we propose a robotic arm grasping system using YOLOv4 combined with a particle filter (PF) algorithm, which can be applied in a nonlinear and non-Gaussian environment. Firstly, the coordinates of the bounding box in the image can be obtained through the YOLOv4 object detection algorithm. Secondly, the coordinates in the world system can be obtained through the eye-to-hand calibration system. Thirdly, a PF model can be established based on the coordinate changes of the target object. Finally, according to the predicted output of the PF, the robotic arm and the target object can reach the specific position at the same time and complete the grab. As the target object, the bowl is applied to experiments for the sake of achieving a more convincing demonstration. The experimental results show that the robotic arm grasping system proposed in this paper can accomplish the successful grasp at a rate of nearly 88%, even at a higher movement speed, which is of great significance to robot applications in various fields.

The accurate state of health (SOH) estimation of lithium-ion batteries enables users to make wise replacement decision and reduce economic losses. SOH estimation accuracy is related to many factors, such as usage time, ambient temperature, charge and discharge rate, etc. Thus, proper extraction of features from the above factors becomes a great challenge. In order to extract battery’s features effectively and improve SOH estimation accuracy, this article proposes a time convolution memory neural network (TCMNN), combining convolutional neural networks (CNN) and long short-term memory (LSTM) by dropout regularization-based fully connected layer. In experiment, the terminal voltage and charging current of the battery during charging process are collected, and input and output data sets are sorted out from the experimental battery data. Due to the limited equipment in the laboratory, only one battery can be charged and discharged at a time; the amount of battery data collected is relatively small, which will affect the extraction of features during the training process. Data augmentation algorithms are applied to solve the problem. Furthermore, in order to improve the accuracy of estimation, exponential smoothing algorithm is used to optimize output data. The results show that the proposed method can well extract and learn the feature relationship of battery cycle charge and discharge process in a long time span. In addition, it has higher accuracy than that of CNN, LSTM, Backpropagation (BP) algorithm, and Grey model-based neural network. The maximum error is limited to 3.79%, and the average error is limited to 0.143%, while the input data dimension is 514.

In view of the problem that the traditional motor test system cannot directly test the transient parameters of the motor and the dynamic arbitrary load loading requirements during motor loading, as well as the high cost of implementation, this research uses STM32+FPGA as the core to form the main control of the motor test system unit, combining the superior control performance of the ARM processor and the high-speed data processing advantages of FPGA. FPGA and STM32 are controlled by the FSMC bus communication and data ping-pong algorithm. Using this method, a small-size control core board in the motor test system is manufactured. It can be embedded in the existing traditional dynamometer system to improve the dynamometer transient parameter test and the dynamic motor loading performance. The experimental results show that the system can basically meet the requirements of the motor transient test and dynamic loading, and can achieve the fastest data refresh rate of 1 ms when measuring the motor’s speed and torque, as well as arbitrary waveform loading within a 100 M sampling frequency, with a loading error of 0.8%. It satisfies the motor transient test and dynamic loading requirements.