Search

Inspired by the flexible structure of soft organisms, many researchers have developed different new soft robots using various flexible materials. Compared with traditional rigid robots, soft robots have a small mass, a high degree of freedom, good flexibility, and high safety of human-computer interaction. However, research on designing new types of quadruped soft robots is still a big challenge. This paper proposes an innovative design and control methodology for a pneumatic quadruped soft robot, employing a highly deformable hexagonal structure with shape-preserving material characteristics. First of all, the scheme design of the bionic soft quadruped robot. Based on the movement mechanism of quadrupeds, combined with the deformation concept of the honeycomb hexagonal chamber, the leg structure of the robot is designed, and the corresponding pneumatic driver is designed according to the driving mode, which can drive the legs to perform stretching, bending and other movements. Secondly, the mathematical modeling and finite element simulation. The deformation principle of the hexagonal chamber and the elongation of a single leg are modeled by the geometric analysis method, and then the validity of the modeling is verified by the finite element simulation method. Finally, multiple experiments show the performance of the designed quadruped robot including the static characteristics, output force, and workspace on a single leg. Besides, the control logic program was used to carry out the straight line and curve walking experiments of the quadruped robot. Thus the proposed method provides an efficient and effective design strategy for soft quadruped robots.

Estimating the flying attitude of the missile body during flight is vitally important for the intelligent ammunition. The earlier and more accurate the posture information is obtained, the more conducive it is to the control process of the intelligent ammunition. The magnetometer is commonly used to measure the roll attitude of the missile. However, the cumbersome calibration steps and noise interference of the missile usually cause the delay in the attitude calculation and inaccurate measurement. To solve these issues, an adaptive reconfigurable unscented Kalman filter (ARUKF) method based on energy estimation is proposed. The magnetic field measured by the magnetometer is analyzed in real time based on the roll characteristic of the intelligent ammunition to achieve the initial calibration of the magnetometer parameters. With the obtained calibrated parameters, the filter state variables are estimated by establishing the residual estimation function in the UKF, which guides the reconstruction of the UKF to minimize the computational cost of the attitude estimation process. Simultaneously, the on-board computer system feeds back the action of the rudder system in real time to guide the configuration of the noise parameters in the UKF process, which can effectively reduce the interference of the magnetic field generated by the rudder system on the attitude estimation. The numerical simulation and experiments show that the proposed method performs better in terms of parameter convergence speed and estimation accuracy, compared with the traditional UKF method, which has great application prospect in the field of intelligent ammunition.

The accurate prediction and research about the dynamic characteristic of machine tool during the design process are essential to develop high-performance machines. In order to consider the effect of mechanical modal characteristics on the whole dynamic performance and servo controller of ball screw feed drive fully, the modal characteristics of mechanical transmission are extracted and solved based on reduced state space matrix of ball screw feed system, which is also easy for integration with servo control model. On this basis, the step-by-step optimization method from the global to the local is proposed to optimize the servo controller and overall dynamic performance of ball screw feed drive. This step-by-step optimization method is as follows: first, the global initial optimal tuning of servo controller parameters under the influence of structure modal characteristics is originally realized through the analytic derivation of servo controller based on the extraction of state space modal characteristics. Second, the integrated model of the feed drive system is established. Finally, based on the above global optimization results, the overall dynamic performance, and the servo parameters of the feed drive system under the influence of the modal characteristics are further studied and optimized locally. Taking the ball screw feed drive studied in this paper like the example, its rise time and the settling time of step response are 0.089 and 0.104s before optimization, while these reduce to 0.038 and 0.092s after optimization. Its dynamic response performance after optimization is much better. The optimization result is also validated by the test: the rise time and the settling time by the test are 0.042 and 0.094s, which are close to that from the simulation. Also, the consistency of step response curves from experiment and simulation as well. All these show that: this prediction and optimization method of overall dynamic performance considering the modal characteristic influence of mechanical transmission fully is correct and reliable.

Key nodes detection is conducive to the information promotion and public opinion monitoring. Traditional works mainly focus on the nodes affecting the breadth of information diffusion. However, there are several works concentrate on the depth of information diffusion. Breadth dictates the number of nodes receiving information in one-hop propagation. Depth implies the length of the retweeting cascade of information and the number of social circles getting information. Depth is as important as breadth, for the nodes which contribute to the depth of information diffusion can promote society propagation (SP). If we have a good control of these nodes, information will ignite the community and minority will tip over the cognitive of the group. The purpose of this paper is to identify the influential nodes which can influence the SP in breadth and depth. With the analysis of SP, we proposed backbone node detection based on community structure (BNDCS) to detect these nodes. In this paper, we defined backbone nodes (BNs) by the location feature and the process of information diffusion. Besides, we presented a cohesion subgroup detection method based on hybrid seed expansion. It can adapt the network with no significant community structure and fit the star topology well. We proposed an information tension factor to measure the influence of bridge nodes. Through the experiment contrasts, the BNDCS is superior to compared methods. It showed that the BNs play a critical role in SP. Therefore, monitoring the BNs can prevent the mass incidents occurring and stop the spread of panic.