Mobile Robot Navigation And Environment Modeling In Unknown Environment Based On BF-PSO Optimization

With the continuous development of science and technology,the degree of automation of human society and the degree of automation in the continuous improvement of intelligent mobile robots become more and more close to human life,like sweeping robots,service robots,disaster relief robots and people living closely related.The research on the navigation and environment modeling of mobile robots has always been the focus of research among domestic and foreign academic researchers.How can a mobile robot make a reasonable decision like a human race when navigating in an unknown environment has always been the goal that all robotics researchers expect to achieve.And how to make the mobile robot can complete the accurate modeling of the unknown environment in the process of moving is also an important direction of mobile robot research.In this paper,an improved artificial potential field method is proposed to solve the shortcomings of mobile robot navigation by using traditional artificial potential field method.The method improves the artificial potential field method by improving the repulsive force function and adding the rotational force,minimizes the degree of distortions of the repulsive potential field and realizes the global minimum value of the potential field at the target point.The simulation results show that the improvement of the artificial potential field method can improve the smoothness of the moving trajectory of the mobile robot,reduce the irregular jitter around the obstacle,and realize the accessibility of the target point.Aiming at the potential function coefficient of mobile robot,the parameter optimization based on bacterial foraging and particle swarm optimization algorithm(BF-PSO)is proposed.In order to study the optimization effect of BF-PSO algorithm on the optimization of mobile robot navigation,an improved artificial potential field mobile robot navigation experiment based on optimal parameter setting and experience parameter setting is designed respectively.Simulation results show that the BF-PSO parameters can improve the navigation effect of mobile robots.The modeling method of mobile robot environment based on extended kalman filter(EKF)algorithm and unscented kalman filter(UKF)algorithm in unknown environment is studied respectively.The experiment of the two methods has obtained an estimated path of the robot movement and the observed landmark,outputting the error between the estimated path and the real path,and the error between the observed position of the landmark and the real landmark.Experiments show that the robot trajectory obtained by the mobile robot environment modeling method based on the UKF algorithm has better accuracy with the calibrated landmark.