Research On The Movement Stability And Path Optimization Of Self-balancing Robots

In recent years,the technology of mobile robot has been developed by leaps and bounds,which is not only widely used in production and life,but also has a great impact on many industries,national defense and national economy.On the practical application,the mobile robot can replace the human work in dangerous(toxic,radiation,etc.)and complex environment,even the environment(the space,the deep sea,etc.)human can't reach.The two wheeled self-balancing robot is a kind of widely used mobile robots.It is intelligent machine system of environmental perception which consist of dynamic decision-making and planning and other functions.Self-balancing robot is same as the inverted pendulum,which is an instability,multivariable,nonlinear and strong coupling system.Because the two wheeled robot can complete the complicated task which the multi wheeled robot can't complete,there is a theoretical and practical significance to research them deeply.With the special nature of self-balancing robot,the most fundamental problem is the system research on its stability.The balance and safety of the robot must be ensured,in order to achieve the smooth realization of other technologies and functions and applications.At the same time,to complete other normal action,how to optimize the stability of the robot is worthy of further consideration.In this paper,it is proposed a model based on ant colony algorithm with the weight of controller.On this basis,an improved honeycomb grid method and a multi fusion algorithm based on genetic ant colony algorithm is proposed to optimize the environmental information and to find the optimal path.At last the effect and efficiency was verified through experiments.The research work of this paper mainly includes the following aspects:(1)We proposed and designed a model based on ant colony algorithm to optimize the stability of the robot to select the appropriate controller parameters,which overcame the uncertainty and time consuming of the artificial selection parameters.The optimization method is superior to the traditional method of selecting parameters manually,validated by practical operation of the mobile robot;(2)The traditional grid method is not ideal for the partition of map information in path planning.In this paper,we designed and implemented an improved grid method to deal with the environmental information which combines the natural phenomenon and geometry theory to achieve more feasible space for the mobile robot.Through the adaptive coding method,the map information can be flexibly set,and the continuity of the grid is more conducive,searching the optimal path.The experimental results showed that the improved method can reduce the path length and the search time,improving the safety of the robot;(3)The robot path planning problem was deeply researched.Through a large number of data,we used the ant colony algorithm and genetic algorithm as the basis of multi fusion method.Combined with the advantages and disadvantages of the two algorithms,this paper proposed and designed a genetic ant colony algorithm to solve the problem of path optimization.By iterating 30 times,80 times and 150 times compared with the single ant colony algorithm,the experiments showed that our algorithm is a better to solve the path planning problem in steady-state iterations or steady-state path length.