Dynamics Analysis Of One-Legged Hopping Robots

At present, bio-robots research mainly concentrated in the wheel (tracked) robots and crawling robots. Compared with these robots, legged hopping robots can leap relatively large obstacles and locomote on the more complex terrain, and they are the hotspot object of the bio-robots research fields in recent years.In the paper, the research development of hopping robot is summarized, and the main issue of the paper is illustrated.For the past one-legged hopping robots design limitations, a new mechanical model——kangaroo-like hopping robot with one leg is proposed, and its dynamics modeling and motion simulation were done, the results of the simulation verify the correctness of the dynamics model, which laid a theoretical basis for the robot motion control and structural optimization.For the kangaroo-like hopping robot with one leg, the characteristics of its support phase is second-order nonholonomic constraint system, starting from the non-linear control theory, based on dynamics model, deduced the linear system of equilibrium point for the kangaroo-like hopping robot with one leg in the support phase, and proved that it is linearly controllability in the support phase, which provided a theoretical basis for the robot's stability control.For a one-legged hopping robot system is a nonlinear system, and the periodic motion is an important solution of a class of nonlinear system, explored the searching methods and algorithms of the periodic motion, and a simple model of one-legged hopping robot for example, found its passive periodic motion by using the described methods and algorithms.In the last part of the research, for the existing searching method of periodic motion, its biggest drawback is to manually modify the initial conditions, the manual modification of initial conditions had blindness, that is, after numerous attempts to find the satisfied initial conditions, so that it is very time-consuming and inefficient. Therefore, if prior to determin initial conditions we used an optimal way to find a suitable initial condition, it can improve efficiency, and avoid blindness.