Intelligent Robot Path Planning Based On Improved Artificial Fish Swarm Algorithm

At present,the research field of intelligent robots is developing rapidly.Robots are widely used in all aspects of human dailylife,and people have increasingly complex requirements for their reliability and safety.The related topics of intelligent robot research are complex,constrained and nonlinear,and involve comprehensive disciplines such as navigation technology,sensor technology,control technology and path planning technology.Path planning technology is an important topic in many research fields of robots.The realization of path planning problems is inseparable from the support of technologies such as environmental modeling,navigation and positioning algorithms.This paper chooses to study the artificial fish swarm algorithm.By reviewing the principle and characteristics of the artificial fish swarm algorithm and the behavioral characteristics of the artificial fish,the influence and function of each parameter on the algorithm are analyzed.By analyzing its convergence,it can be seen that the artificial fish swarm algorithm has fast convergence in the early stage but the search efficiency is not high enough,the convergence speed is slower and the search accuracy is not high,and the local optimal problem processing is not stable enough.Aiming at the blindness of the pre-search of artificial fish swarm algorithm,the artificial fish are scattered and the search accuracy is not high,an elite retention and elimination strategy is proposed.This strategy is derived from the evolutionary idea of biology,and proposes the concept of elite fish school,including the elimination and cloning mechanism of organisms.The elite individuals will be cloned as parents,and the relatively degraded individuals will be eliminated.Experiments prove that this method can improve the efficiency of algorithm search.Sex and accuracy,but there are still deficiencies in the local optimal problem.Aiming at the limitation of single improvement method,this paper adopts the idea of multi-algorithm mixing,and proposes a hybrid artificial fish swarm algorithm based on harmony search algorithm.Firstly,the principle,search method and characteristics of the harmony search algorithm are analyzed.The local fine-tuning ability of the harmony search algorithm is improved to improve the local search accuracy of the artificial fish swarm algorithm.Then the concept of chaotic factor is introduced,and its repeat-free traversal is used.The global search ability improves the global search efficiency of the artificial fish swarm algorithm and forms a new hybrid fish swarm algorithm.After applying the improved hybrid algorithm to the classical function optimization problem of three different functions,it is proved that the algorithm can not only find the global optimal value field quickly,but also jump out of the local optimum,and has good optimization precision and efficiency.Finally,the improved new algorithm is applied to the path planning research.The grid model is used to establish the environment model.The corresponding strategy of robot collision avoidance is adopted,and the appropriate fitness function is proposed.The improved hybrid algorithm is used to simulate the path of the robot.The experimental results show that the improved algorithm can effectively guide the robot to avoid obstacles,and can quickly find the best path or better path,and the improved hybrid algorithm is more efficient than other algorithms in path planning.Sex and reliability,and can handle more complex environmental models.