Research on an intelligent path planning algorithm for robot using electrical control technology

In view of the shortcomings of traditional genetic algorithm in path planning, such as infeasible path, falling into local optimization, too many turning times and so on, an improved adaptive genetic algorithm is proposed. A priori knowledge is used to optimize the initial population, and the initial population that does not intersect with obstacles is obtained. The probability formulas of crossover and mutation are designed to avoid falling into local optimization, so as to improve the convergence speed. The smoothness of the path and the shortest path are introduced into the fitness function as the evaluation criteria to make the planned path more efficient. The simulation results show that: compared with the basic algorithm, when the number of obstacles is 20:00, the path of the improved algorithm is reduced by 4.2%; when the number of obstacles is 115, the path is reduced by 25.1%. With the continuous increase of obstacles, the percentage of path reduction shows an upward trend, and the number of iterations of the algorithm and the number of turning points in the path are better than the basic genetic algorithm.