Research On Path Planning Problems Of Mobile Robots Based On Improved Artificial Fish Swarm Algorithm

Path planning is one of the core fields of mobile robot research,and it is also the key symbol of robot intelligence.The path planning problem is characterized by complexity,constraint and nonlinearity.With the development of swarm intelligence optimization algorithm,this provides a new effective way for mobile robot path planning problem.In this paper,swarm intelligence algorithm is employed to solve robot path planning problems.At first,an effective optimization algorithm is proposed.Based on the novel artificial fish swarm algorithm,a parallel adaptive artificial fish swarm algorithm based on differential evolution is proposed to overcome the weak points such as premature and slow running efficiency.The basic idea of the improved algorithm is to divide the whole population into two subpopulations(groups)with the same size,and different adaptive strategies are applied to the two groups respectively to make one group focus on global search and the other on local search.The two subpopulations evolve independently and individual migrations are conducted regularly to achieve information communication,increase the population diversity and improve convergence rate of proposed algorithm.What's more,when the information on the bulletin board does not change for a certain times,the differential evolution algorithm will be introduced to make the algorithm break away from local optima.Lastly,The behavior selection method of the algorithm is improved,which is to make the probability of the population move to the global optimal position increase gradually with the running of the algorithm,thus will not only reduce the computational complexity but also improve convergence rate in the late stage.Several classic benchmark functions are chosen to verify the superiority of the improved algorithm,and the comparing simulation results demonstrate that the improved algorithm performs better than the basic algorithm,the balance ability of exploitation and exploration is enhanced,and convergence efficiency and optimization precision are improved greatly as well as the stability is strengthened.Then the proposed algorithm is applied to path planning problems.When the environment model is established,a kind of environment division method is put forward.In path evaluation function,path length,smoothness and security degree are considered simultaneously.Firstly,the algorithm is applied to path planning of mobile robots.The experiments of global path planning,path planning with obstacles irregularly added and path planning with the change of target point of mobile robots are conducted independently,the results show that the path length,smoothness and the other indicators have been improved significantly,and the operation efficiency has been greatly improved.Secondly,the algorithm is used to solve air route planning problem of unmanned aerial vehicle(UAV),which is belong to the category of path planning problems of mobile robots,the simulation results indicate that the improved algorithm can get a shorter route to avoid the threat area.The research results show that the proposed algorithm has a great improvement in the overall performance;the feasibility and effectiveness of the algorithm in solving robot path planning problems are verified as well.The work of this paper provides some inspiration and reference for solving the problems of path planning,and has some theoretical significance and practical value.