Path Planning Based On Quantum Genetic Algorithm For Soccer Robot

Path planning for mobile robots is an important problem which means to generate an optimal path from a starting position to a goal position not only guaranteeing a collision-free path with minimum traveling distance but also requiring smoothness and clearances in a rough environment. There are several path planning methods as Visibility Graph, Free-Space Method, Grid Method which are classified as traditional methods. Correspondingly, the intelligent path planning methods develop rapidly which are applied with theory of neural network, fuzzy logic, genetic algorithm and ant colony. One great research tendency is to path plan with several theories'virtues. The paper develops traditional and intelligent algorithms approach to path planning for soccer robots on the base of analyzing those path planning methods.To improve the shooting precision of soccer robot, a shooting path planning algorithm based on dynamic ellipse curve is proposed in this paper. By calculating the robot's position, pose and desired shoot angle dynamically, the soccer robot is controlled to move to target at ellipse curve and shoot quickly. The results of simulation and experiment demonstrate the validity of the algorithm. The path is short and shooting is finished in a proper angle.Genetic algorithm (GA) is an adaptive search algorithm to deal with the problem of optimization premised on the evolutionary process of natural selection and crossover of information between chromosomes within a population. Modeled on the principles of the natural selection and probabilities of the genetic evolution, some problems can be represented as evolutionary processes of chromosomes. With the evolution operation such as selection, mutation and recombination, the optimal solutions can be obtained. GA is applied to solve the path planning of soccer robots. The components of GA are analyzed, including environment representation, chromosome representation, path evaluation, genetic operators design and selecting of algorithm parameters and simulation experiments of path planning based on GA are provided.Quantum Genetic Algorithm (QGA) is applied to probabilities optimization based on quantum computation (QC) which relies on the principles of quantum mechanics like qubit representation and superposition of states. One method of path planning for soccer robots is proposed in this paper integrating the characteristics of genetic algorithm and quantum computation. Quantum coding is applied to genetic algorithm (GA) to develop one path planning method characterized by quantum mechanisms numerical optimization. Quantum rotation gate strategy is used to update the individual, quantum crossover operation and quantum mutation operation are introduced, and therefore high efficiency for optimization is achieved. With the superposition of quantum states, the path planning based on quantum genetic algorithm can reduce population of chromosomes and avoid premature convergence. Simulation result demonstrates the effectiveness of the path planning method based on QGA.