In this research, we propose a novel wheel-legged mobile robot to address the problems of insufficient obstacle-crossing performance and poor motion flexibility of mobile robots in non-structural environments. Firstly, we designed the transformable wheel mechanism and tail adaptive mechanism. Secondly, the kinematic model of the robot is established and solved by analyzing the whole motion and wheel-legged switching motion for the operation requirements under different road conditions. By synthesizing the constraint relationships among the modules and analyzing the robot’s obstacle-crossing abilities, we systematically established the mechanical model of the robot when it encounters obstacles. Thirdly, we studied the stability of the robot based on the stable cone method in the case of slope and unilateral transformation wheel deployment and achieved the tipping condition in the critical state. Finally, we used ADAMS software to simulate and analyze the driving process of the robot in various types of terrain and obstacles in order to verify that it has superior performance through obstacles and motion flexibility. The analysis shows that the robot can passively adapt to various complex and variable obstacle-filled terrains with obstacle heights which are much higher than its center of gravity range. The results of the study can provide a reference for the structural optimization and the obstacle-crossing performance improvement of mobile robots.

With the deep integration of informatization and industrialization, neural network technology has gradually penetrated into the field of mechanical design due to the characteristics of self-adaptive, self-organization and self-learning. Through the combination of mechanical design theory, the process of machinery and equipment design gradually tends to be intelligent and automated. First, from neural network technology and its construction of network models in mechanical equipment design, the solution to mechanical optimization design issues will be described in two models of the BP neural network and the Hopfield neural network. For the three main directions of machine tool body, typical components, and industrial robot design, this paper introduces the design and application of neural network technology in machine tool structure optimization and error compensation, gear transmission device and spindle, industrial robot structure and system. On this basis, the future development trend of the neural network technology in the field of mechanical equipment design is discussed, which provides theoretical support for the future intelligent mechanical equipment design.