Soccer Robot Path Planning Based On Improved Artificial Potential Field

Soccer Robot is an important subject area of robotics research.In recent years,it has been developing rapidly, and many kinds of Soccer Robot international competitions are organized,among which RoboCup medium-sized group robot soccer is a more well-known gameThe complexity and randomness of environment in robot soccer game make path planning be a priority research on soccer robot,which technology level decide the quality of the game. It refers to the perception, positioning, communications, distributed control technology, is a multi-subject integrated and comprehensive.In this paper, medium-sized group of Robocup robot soccer competition is research background. Firstly, introduced the domestic and international soccer robot and path planning Research and development, mid-sized group of RoboCup robot soccer competition and the composition and software environment of the soccer robot system. As the basis for Path planning in-depth study, a description of the methods used in environmental modeling and obstacle detection methods has been introduced, and classified path planning algorithms, comprehensively analysed their respective characteristics. Secondly, based on a comprehensive analysis of the soccer robot system, the key research on path planning problems is carried on .By analyzing and Comparing the advantages and disadvantages of various methods, chose the artificial potential field method as in-depth study and proposed improved method for overcoming its shortcomings to improve the path planning results. As the artificial potential field method is a local path planning method, there is local minimum problem, and can not satisfy requirements of soccer robot path planning for real-time, safety and accessibility in dynamic environment. For the problems of traditional artificial potential field method in a complex game environment, modified the potential field force function and potential field force direction to avoid the failure of path planning. Taking into account the competitive playing field and the movement of agent, this paper introduced the velocity as the impact of factors into potential field force function, designed the fuzzy controller to regulate the size of artificial potential field force, and proposed dynamic fuzzy artificial potential field method. On this basis, in order to better search the optimal path, introduced genetic algorithms for evolving path, and proposed the dynamic fuzzy evolutionary potential field method. Finally, an simulation experiment was carried on by the method on the MATLAB platform. Analyzed path planning according different regions and state of different location and movement. The improved method can overcome the traditional artificial potential field method of the local minimum problem, while improving planning accuracy and real-time. Simulated experiment certificated the effectiveness of the method.