The performance of vehicle body vibration and ride comfort of active or semi-active suspension with proper control is better than that with passive suspension. The key to achieve good control effect is that the suspension control system should have strong real-time learning ability according to changes in the road surface and suspension parameters. In the control strategies adopted by previous researchers, the classical neural network controller has some learning ability, but it is mainly based on offline learning with a large number of samples. In this paper, the deep reinforcement learning strategy is used to solve the above problems. Aiming at the continuity of state space and execution action in vehicle active suspension system, the control of the semi-active suspension is realized by using improved DDPG (Deep Deterministic Policy Gradient) algorithm. To overcome the shortcoming of low efficiency of this algorithm in the initial stage of learning, the DDPG algorithm is improved and using empirical samples in the learning method is proposed. Based on Mujoco, the physical model of semi-active suspension is established, and its dynamic characteristics are analyzed under the condition of various road level and vehicle speed. The simulation results show that compared with the passive suspension, the semi-active suspension based on improved DDPG algorithm with learning method using experienced samples can better adapt to various road level, more effectively reduce the vertical acceleration of the vehicle body and the dynamic deflection of the suspension, and further improve the ride comfort.

The belt conveyor is one of the key equipment in coal mining. Timely identification of belt damage is necessary to ensure coal mine safety production and reduce economic losses. In order to meet the needs of practical application, a new method of belt damage identification is proposed by fusing the temporal and spatial features of the image and directly using the on-site monitoring video image. Based on the deep learning network structure, an end-to-end belt damage detection model is designed. An improved attention mechanism is designed and introduced into the image spatial feature extraction model to solve the problem of complex background in belt surveillance video. In order to reduce the influence of reflection and shadow on recognition accuracy, a method of extracting temporal features of continuous multi frame video images using Temporal Convolutional Networks (TCN) is designed. The experimental results show that the accuracy of the method for belt damage recognition by fusing the temporal and spatial features of the image is more than 20% higher than that of the method for extracting the spatial features of the image alone, which shows that our proposed method is suitable for belt damage detection directly using the on-site belt monitoring video and is practical.

For large and medium-sized cities, the planning and development of urban road networks may not keep pace with the growth of urban vehicles, resulting in traffic congestion on urban roads during peak hours. Take Jinan, a mid-sized city in China’s Shandong Province, for example. In view of the daily traffic jam of the city’s road traffic, through investigation and analysis, the existing problems of the road traffic are found out. Based on real-time, daily road traffic data, combined with the existing road network and the planned road network, the application of a road intelligent transportation system is proposed. Combined with the application of a road intelligent transportation system, this paper discusses the future development of urban road traffic and puts forward improvement suggestions for road traffic planning. This paper has reference value for city development, road network construction, the application of intelligent transportation systems, and road traffic planning.

To enable quadruped robot to walk through unknown rough terrains without any machine vision system, a continuous static gait planning method is proposed in this paper. A algorithm for on-line terrain complexity evaluation according to touchdown times of swing feet is presented to make quadruped robot obtain the information of terrain without relying on machine vision system. Quadruped robot can obtain enough stability margin during walking process by using the proposed body trajectory planning method, and motion continuity of body can be guaranteed. In order to improve the terrain adaptability of quadruped robot, a tunable parameter is set in the body planning method, and the relationship between value of the tunable parameter and terrain complexity estimation is given to further improve terrain adaptability and energy utilization rate. With the proposed static gait generation method, quadruped robot can autonomous plan its static gait in real-time and travel over unknown rough terrains with enough stability margin. The simulation results show the correctness and effectiveness of the proposed method.

The decision tree algorithm is a core technology in data classification mining, and ID3 (Iterative Dichotomiser 3) algorithm is a famous one, which has achieved good results in the field of classification mining. Nevertheless, there exist some disadvantages of ID3 such as attributes biasing multi-values, high complexity, large scales, etc. In this paper, an improved ID3 algorithm is proposed that combines the simplified information entropy based on different weights with coordination degree in rough set theory. The traditional ID3 algorithm and the proposed one are fairly compared by using three common data samples as well as the decision tree classifiers. It is shown that the proposed algorithm has a better performance in the running time and tree structure, but not in accuracy than the ID3 algorithm, for the first two sample sets, which are small. For the third sample set that is large, the proposed algorithm improves the ID3 algorithm for all of the running time, tree structure and accuracy. The experimental results show that the proposed algorithm is effective and viable.

The hydraulic servo actuator with passive compliance (HPCA) is designed for hydraulically-driven quadruped robots. It is characterized by an accumulator that connects to the piston chamber of the hydraulic cylinder to buffer impact forces between a robot’s feet and the ground. This paper studies the energy efficiency of this actuator in the dynamic locomotion of a quadruped robot. Different from the traditional methods of storing potentially recyclable energy using accumulators, the energy-saving principle of HPCA is to utilize the pressure-transition characteristics of the asymmetric hydraulic cylinder with control of the symmetrical valve. The accumulator can store and release oil during the switching of the transition point in each gait cycle of the robot, thereby improving the energy efficiency of the actuator. The influence of the initial inflation pressure and working volume of the accumulator on the energy efficiency of the HPCA is studied by simulation and physical experiments. The results show that the HPCA has a higher energy efficiency that is independent of the physical parameters of the accumulator.