In this reported work, thermoplastic polyurethane (TPU) was used as a reactive polymer modifying agent to prepare a modified-asphalt, using a high-speed shearing method. Physical performance tests of the TPU-modified asphalt were conducted before and after short-term aging, and the aging resistance was examined by the change in materials properties. In addition, low-temperature rheological properties, thermal properties, the high-temperature storage stability, and the aging mechanism of TPU-modified asphalt were also investigated. The results showed that the addition of TPU improved the aging resistance of base asphalt, which was evidenced by the increased penetration ratio and decreased softening point of the asphalt, after aging. Similarly, Fourier Transform infrared (FTIR) spectroscopy results verified that TPU improved the asphalt aging resistance. It was found that the TPU functional groups played a role in improving thermal properties, high-temperature storage stability, and in the dispersion of modified asphalt.

Intelligent workshop UAV inspection path planning is a typical indoor UAV path planning technology. The UAV can conduct intelligent inspection on each work area of the workshop to solve or provide timely feedback on problems in the work area. The sparrow search algorithm (SSA), as a novel swarm intelligence optimization algorithm, has been proven to have good optimization performance. However, the reduction in the SSA’s search capability in the middle or late stage of iterations reduces population diversity, leading to shortcomings of the algorithm, including low convergence speed, low solution accuracy and an increased risk of falling into local optima. To overcome these difficulties, an improved sparrow search algorithm (namely the chaotic mapping–firefly sparrow search algorithm (CFSSA)) is proposed by integrating chaotic cube mapping initialization, firefly algorithm disturbance search and tent chaos mapping perturbation search. First, chaotic cube mapping was used to initialize the population to improve the distribution quality and diversity of the population. Then, after the sparrow search, the firefly algorithm disturbance and tent chaos mapping perturbation were employed to update the positions of all individuals in the population to enable a full search of the algorithm in the solution space. This technique can effectively avoid falling into local optima and improve the convergence speed and solution accuracy. The simulation results showed that, compared with the traditional intelligent bionic algorithms, the optimized algorithm provided a greatly improved convergence capability. The feasibility of the proposed algorithm was validated with a final simulation test. Compared with other SSA optimization algorithms, the results show that the CFSSA has the best efficiency. In an inspection path planning problem, the CFSSA has its advantages and applicability and is an applicable algorithm compared to SSA optimization algorithms.

The demand for flexible pressure sensors in wearable devices is dramatically increasing. However, challenges still exist in making flexible pressure sensors, including complex or costly fabrication processes and difficulty in mass production. In this paper, a new method is proposed for preparing the flexible pressure sensors that combines an imprinting technique with blade-coating of a graphene–silver nanosheet–polymer nanocomposite. The piezo-resistive type flexible pressure sensor consists of interdigital electrodes and nanocomposite as a sensing layer, as well as a micropillar array structure. The morphology of the sensitive layer of the sensor is characterized by scanning electron microscopy (SEM). The response performance, sensitivity, and stability of the sensor are investigated. The test results show that the initial resistance of the pressure sensor is only 1.6 Ω, the sensitivity is 0.04 kPa⁻¹, and the response time is about 286 ms. In addition, a highly hydrophobic wetting property can be observed on the functional structure surface of the sensor. The contact angle is 137.2 degrees, revealing the self-cleaning property of the sensor. Finally, the prepared sensor is demonstrated as a wearable device, indicating promising potential in practical applications.

Assembly robots have become the core equipment of high-precision flexible automatic assembly systems with a small working range. Among different fields of robot technology, path planning is one of the most important branches. In the present study, an elite smoothing ant colony algorithm (ESACO) is proposed for spatial obstacle avoidance path planning of the grasping manipulator. In this regard, the state transition probability and pheromone update strategies are improved to enhance the search capability of path planning symmetry and the convergence of the algorithm. Then a segmented B-spline curve is presented to eliminate path folding points and generate a smooth path. Finally, a manipulator control system based on the Arduino Uno microcontroller is designed to drive the manipulator according to the planned trajectory. The experimental results show that the performance of the ESACO algorithm in different scenarios has symmetry advantages, and the manipulator can efficiently complete the simulation trajectory with high accuracy. The proposed algorithm provides a feasible scheme for the efficient planning of manipulators in equipment manufacturing workshops.