An Effective Path Planning of Intelligent Mobile Robot Using Improved Genetic Algorithm

Abstract

With the rapid development of the robotics industry, the problem of effective and fast path planning for intelligent mobile robots has always been one of the hot spots in the field of robotics research. Intelligent mobile robot path planning is divided into global path planning and local path planning, and its mathematical modeling and adaptive algorithms are different. Therefore, the research of robot path planning based on improved genetic algorithm is of great significance. This paper mainly studies the robot path planning problem based on improved genetic algorithm. Based on the research of the basic genetic algorithm, the improved genetic algorithm is applied to the mobile four-wheel robot to guide the four-wheel robot to complete path planning and other related tasks. Experiments show that the optimization probability and convergence speed of the genetic algorithm can be improved by improving the genetic algorithm. Studies have shown that evolutionary algebra and population size are inversely proportional to the optimal path length, so it is directly proportional to the search ability. However, as the evolutionary algebra and population size increase, the amount of calculation is also increasing, and the calculation time increases. Comprehensive considerations according to various factors, the best value of population size is 60, the best value of mutation probability is 0.09, the best value of crossover probability is 0.8, and the best value of evolutionary algebra is 150 generations.