Recent Patents on Mechanical Engineering - Volume 17, Issue 1, 2024

Background: In the manufacturing process of lithium batteries for new energy vehicles, silica gel plates shall be used as heating and thermal insulation. This paper takes four groups of silica gel heating plates with different specifications as the research object to predict the temperature distribution of silica gel plates and provide theoretical references for the manufacturing engineering of silica gel heating plates. Objective: Combined with the heat transfer theory, the temperature distribution of silica gel heating plate is predicted by simulation calculation, and the feasibility of this method is verified by experiments, which provide references for the manufacturing engineering of silica gel heating plate. Methods: This article takes four groups of silica gel plates with different specifications as the research objects, and 30 temperature measurement points are collected. The simulation results are compared with the experimental results to verify the method’s feasibility. Results: The average error between the experimental and simulation results was ± 2.6132;ƒ, which was in line with the expected effect of silica gel plates. This paper’s research process and method can provide theoretical references for the manufacturing engineering of silica gel heating plates. Conclusion: Taking the silica gel heating plate provided by the factory as the research object, according to the heat transfer theory, the thermal conductivity equation of the silica gel plate was established, which provided a theoretical model for simulation analysis. The experimental results show that the average error between the simulation and experimental results is ±2.6°C, which is in line with the empirical expectation, and the method is feasible. This paper’s research process and practice can provide theoretical reference for the manufacturing engineering of silica gel heating plate.

Background: Quadruped crawling robots will be faced with stability problems when walking on a raised slope. The stability of robot is affected by gait planning and selection of its foothold in this terrain. The slope reaction force on anterior and posterior legs is uneven. The selection strategy of its foothold should achieve good performance for the stability of the quadruped crawling robot. Objective: Aimed at the uneven problem of slope reaction force on the anterior and posterior legs of the quadruped crawling robot when walking on the raised slope, a patent method for foothold optimization using reinforcement learning based on strategy search is proposed. Methods: The kinematic model of the quadruped crawling robot is created in D-H coordinate method. According to the gait timing sequence method, the frame description of the quadruped crawling robot's gait on the slope is proposed. The fitting polynomial coefficients and fitting curves of all joints of the leg can be obtained by using the polynomial fitting calculation method. The reinforcement learning method based on Q-learning algorithm is proposed to find the optimal foothold by interacting with the slope environment. Comparative simulation and test of other gait and climbing slope gait, the climbing slope gait with and without the Q-learning algorithm is carried out by MATLAB platform. Results: When the quadruped crawling robot adopts the reinforcement learning method based on Qlearning algorithm to select foothold, the robot posture curves are compared without optimization strategy. The result proves that the selection strategy of its foothold is valid. Conclusion: The selection strategy of its foothold with reinforcement learning based on Q-learning algorithm can improve the stability of the quadruped crawling robot on the raised sloped.

Background: The optimal control strategy has been widely used in electro-hydraulic position servo systems to achieve high-precision position tracking. However, the difficulty of selecting the weighted matrices in optimal control often leads to poor tracking accuracy. Objective: This patent proposes an optimal control strategy using a genetic algorithm to improve the tracking accuracy of the electro-hydraulic servo system. Methods: The patent first established the system state equation of the valve-controlled asymmetric cylinder. Secondly, based on linear quadratic optimal control theory and genetic algorithm, an optimal control strategy using a genetic algorithm was proposed. Finally, the simulation and experimental results showed that the designed controller has high position tracking accuracy. Results: The optimal controller using a genetic algorithm was designed using Matlab/Simulink, and the effectiveness of the controller was verified through simulation. Additionally, experimental results showed that the proposed optimal control controller using a genetic algorithm had higher tracking accuracy than the proportional-integral-derivative controller and traditional backstepping controller for a given reference signal. Conclusion: The control technology of the optimal controller using a genetic algorithm was found to be superior to proportional-integral-derivative and traditional backstepping controllers, and the tracking error of the linear quadratic regulator controller was reported to be relatively small. This demonstrated the effectiveness of the optimal control strategy using a genetic algorithm in this patent.

Background: Today, self-driving cars are already on the roads. However, driving safety remains a huge challenge. Trajectory prediction of traffic targets is one of the important tasks of an autonomous driving environment perception system, and its output trajectory can provide necessary information for decision control and path planning. Although there are many patents and articles related to trajectory prediction, the accuracy of trajectory prediction still needs to be improved. Objective: This paper aimed to propose a novel scheme that considers multi-feature independent encoding trajectory prediction (MFIE). Methods: MFIE is an independently coded trajectory prediction algorithm that consists of a spacetime interaction module and trajectory prediction module, and considers speed characteristics and road characteristics. In the spatiotemporal interaction module, an undirected and weightless static traffic graph is used to represent the interaction between vehicles, and multiple graph convolution blocks are used to perform data mining on the historical information of target vehicles, capture temporal features, and process spatial interaction features. In the trajectory prediction module, three long short-term memory (LSTM) encoders are used to encode the trajectory feature, motion feature, and road constraint feature independently. The three hidden features are spliced into a tensor, and the LSTM decoder is used to predict the future trajectory. Results: On datasets, such as Apollo and NGSIM, the proposed method has shown lower prediction error than traditional model-driven and data-driven methods, and predicted more target vehicles at the same time. It can provide a basis for vehicle path planning on highways and urban roads, and it is of great significance to the safety of autonomous driving. Conclusion: This paper has proposed a multi-feature independent encoders’ trajectory prediction data-driven algorithm, and the effectiveness of the algorithm is verified with a public dataset. The trajectory prediction algorithm considering multi-feature independent encoders provides some reference value for decision planning.

Introduction: This article explores a novel approach to developing an environmentally friendly structure through the implementation of eco-design, optimization, and sustainability. The process consists of three key steps. Methods: Firstly, it begins with an understanding of eco-design principles aimed at enhancing the structure's durability by selecting relevant environmental aspects and design guidelines. Secondly, computer-aided design and optimization techniques are employed to identify efficient models, focusing specifically on lightweight structures. Lastly, the article discusses two different processes, namely injection molding and die casting, which can be used to minimize the environmental impact of the structure. Results: The article emphasizes the significance of eco-friendly design in promoting sustainability within the automotive industry. Conclusion: To validate these concepts, the study focuses on vehicle mirrors, which play a crucial role in improving driver visibility, safety, and situational awareness during driving, maneuvering, and parking.

Background: Pedestrian trajectory prediction plays a crucial role in ensuring the safe and efficient operation of autonomous vehicles in urban environments. As autonomous driving technology continues to advance, accurate anticipation of pedestrians' motion trajectories has become increasingly important for informing subsequent decision-making processes. Pedestrians are dynamic and unpredictable agents, and their movements can vary greatly depending on factors, such as their intentions, interactions with other pedestrians or vehicles, and the surrounding environment. Therefore, developing effective methods to predict pedestrian trajectories is essential to enable autonomous vehicles to navigate and interact with pedestrians in a safe and socially acceptable manner. Various methods, both patented and non-patented, have been proposed, including physics-based and probability- based models, to capture the regularities in pedestrian motion and make accurate predictions. Objective: This paper proposes a pedestrian trajectory prediction method that combines a Gaussian mixture model and an artificial potential field. Methods: The study begins with an analysis of pedestrian motion patterns, allowing for the identification of distinct patterns and incorporating speed as an influential factor in pedestrian interactions. Next, a Gaussian mixture model is utilized to model and train the trajectories of pedestrians within each motion pattern cluster, effectively capturing their statistical characteristics. The trained model is then used with a regression algorithm to predict future pedestrian trajectories based on their past positions. To enhance the accuracy and safety of the predicted trajectories, an artificial potential field analysis is employed, considering factors such as collision avoidance and interactions with other entities. By combining the Gaussian mixture model and artificial potential field, this method provides an innovative and patentable approach to pedestrian trajectory prediction. Results: Experimental results on the ETH and UCY datasets demonstrate that the proposed method combining the Gaussian mixture model and artificial potential field outperforms traditional Linear and social force models in terms of prediction accuracy. The method effectively improves accuracy while ensuring collision avoidance. Conclusion: The proposed method combining a Gaussian mixture model and an artificial potential field enhances pedestrian trajectory prediction. It successfully captures the differences between pedestrians and incorporates speed, improving prediction accuracy.

Background: This study investigates the damage in human tissue in regions subjected to stress when the human body experiences localized, instantaneous impact loads. Methods: Utilizing 727 images spanning from the shoulder to the fingertip of a digital human model based on Chinese demographics, the geometric details of tissue structures were derived via tissue segmentation, 3D modeling, and reverse engineering. A stress-induced damage model for the human forearm was created using the finite element simulation software, commercial software COMSOL Multiphysics 5.5 in the college edition. By applying an impact load of 6.4×10⁶ N m² to the load surface, a response time of 1×10⁻³ s was determined. Subsequently, the force transmission mechanism was examined when the human forearm was under stress. This approach represents the unique aspect of our patent study. Results: The modeling and analysis revealed that skin, fat, and muscle -being viscoelastic tissues -undergo deformation upon experiencing stress impacts. This deformation aids in dissipating energy. In transient states, the body does not sustain severe damage, and the impact-induced damage to these tissues is relatively minimal. However, if the force duration is prolonged or if the impact load is exceedingly high, exceeding the critical limit of adhesive tissue may result in penetration of the tissue at the stress point. Notably, tissues beyond the direct impact area remain largely unharmed. Conclusion: Damage due to localized, instantaneous impact loads is primarily concentrated on the immediate stress surface, while regions beyond this point incur minimal to no damage. Calculations indicate that, while such impacts can cause penetrating injuries, the resulting wounds are typically small. With prompt medical intervention, these injuries are not debilitating to the human body.