Fuzzy Control Path Planning of Soccer Robot Relying on Quantum Genetic Algorithm

Abstract

When planning the soccer robot path at present, a two-dimensional map is used mainly to optimize the path of the soccer robot's operating field. However, because the two-dimensional robot is selected, which can only utilize the data of plane about a specific environment, the data information of the mobile soccer robot cannot be gathered, impacting greatly the completion of the plan of the robot path. The path planning of the soccer robot is conducted using the quantum genetic algorithm so that the problem can be dealt with. On the premise that there is a lack of full consideration of the accurate motion path between the two points, the inertia weight is dynamically adjusted to overcome the disadvantage of premature convergence of the traditional quantum group algorithm, which can make the weight of the quantum genetic algorithm controllable with adaptability so that the problem of the extremely slow convergence speed of a single quantum in the quantum group and the high group dispersion can be solved. Furthermore, the selected quantum genetic algorithm can realize the real-time update of the soccer robot's position, effectively increase the trajectory change of the particle swarm movement, and to a certain extent effectively increase the search ability and convergence effect of the particle swarm in the global scope, so as to ensure that this algorithm can be more effective than the traditional path planning algorithm in terms of global search ability and convergence speed in the path planning of the soccer robot and has a higher use value.