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Pressure sensors based on advanced materials have gained high attention for their potential applications in various fields, including sports training. This study focuses on the development of poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE))/graphene composite films as high-performance pressure sensors. The composite films were fabricated using a casting method, and their morphological, structural, thermal, mechanical, and electrical properties were comprehensively characterized. The incorporation of graphene nanoplatelets (GnPs) into the P(VDF-TrFE) matrix led to the formation of a percolated conductive network, resulting in enhanced electrical conductivity and pressure sensitivity. The composite film containing 5 wt% GnP exhibited remarkable sensing capabilities, boasting an elevated sensitivity of 0.85 kPa−1, a rapid response time of 50 ms, and exceptional resilience over 1000 loading-unloading cycles. Moreover, the incorporation of GnP substantially augmented the mechanical properties, with a 65 % enhancement in tensile strength and a 92 % surge in Young's modulus when juxtaposed against pristine P(VDF-TrFE) films. The superior property of these P(VDF-TrFE)/graphene composite pressure sensors, along with their excellent mechanical properties, make them promising candidates for sports training applications, enabling the monitoring and analysis of various biomechanical parameters to optimize athletic performance and prevent injuries.

Abstract Serial industrial robots are widely in demand in the field of large-scale component processing and manufacturing. However, the structure characteristic of serial robots makes it easy to generate chatter during milling and the overhang length of milling cutter has an important influence on the stability of robotic milling. Milling cutter overhang length refers to the length from the tip of the cutter to the protrusion of the spindle, to study the influence of milling cutter overhang length on the stability behavior of robotic milling, the modal parameters of milling cutter under different overhang length were obtained by hammer experiment, and its dynamic characteristics were analyzed. The stability behavior for robotic milling under different cutter overhang lengths was analyzed by using the stability lobe diagrams, and the stability lobe diagrams were verified by robotic milling experiments. The results show that the rigidity and natural frequency of milling cutter decrease with the increase of overhang length. There is a big gap between the stability lobe diagrams and the actual machining state when the cutter overhang length is short, and the low frequency vibration is easy to occur in the robotic milling process when the spindle speed is low, which is most probably because that the absorbed vibration energy by the cutter has been transferred to the robot body before the milling cutter vibrates. When the cutter overhang is increased and the feed direction is along the y-axis of the robot global coordinate system, the stability lobe diagrams by considering the multi-mode coupling effect is more consistent with the actual milling state.

Extended Kalman filter (EKF) is one of the most widely used Bayesian estimation methods in the optimal control area. Recent works on mobile robot control and transportation systems have applied various EKF methods, especially for localization. However, it is difficult to obtain adequate and reliable process-noise and measurement-noise models due to the complex and dynamic surrounding environments and sensor uncertainty. Generally, the default noise values of the sensors are provided by the manufacturer, but the values may frequently change depending on the environment. Thus, this paper mainly focuses on designing a highly accurate trainable EKF-based localization framework using inertial measurement units (IMUs) for the autonomous ground vehicle (AGV) with dead reckoning, with the goal of fusing it with a laser imaging, detection, and ranging (LiDAR) sensor-based simultaneous localization and mapping (SLAM) estimation for enhancing the performance. Convolution neural networks (CNNs), backward propagation algorithms, and gradient descent methods are implemented in the system to optimize the parameters in our framework. Furthermore, we develop a unique cost function for training the models to improve EKF accuracy. The proposed work is general and applicable to diverse IMU-aided robot localization models.