Path Planning Technology Of Mobile Robots Based On Swarm Intelligence Algorithm

Mobile robot technology represents the development of high technology and has been deeply applied in various research fields. Mobile robot to detect the environment information and its state by a variety of different sensors. In the obstacle environment, mobile robot complete target task that it can avoid obstacles from its starting point to the target point location, namely path planning technology for a mobile robot. Path planning technology is an important part of the robot navigation technology, and it has important research significance. In the process of path planning, accurate environment model need to be developed.The mobile robot plan out a optimal path from the starting point to the target point, so, research on environment modeling for the mobile robot and path planning algorithm have theoretical and realistic significance. This article analyzes the shortcomings of the traditional artificial fish algorithm, improves artificial fish swarm algorithm in the grid environment and behavior. Then, the improved artificial fish algorithm based on the behavior selection strategy was applied to achieve in the mobile robot path planning problem, compared to the traditional artificial fish algorithm. The main research contents of this paper are described as follows:1. This thesis studies the global path planning for a mobile robot and proposed a gird modeling method for the mobile robot. By studying the position relationship of obstacles between point and the target point in the environment and the starting point and the mobile robot moving space mode turned into two-dimensional plane from three-dimensional space, the two-dimensional plane environment model, is divided by lattice grid method which can be described as a model of the mobile robot path planning environment.2. Aiming at the stationary of traditional artificial fish swarm algorithm field parameters, and leading to the traditional artificial fish swarm algorithm of slow convergence speed and falling into the local master problem, a kind of weighted average distance of artificial fish swarm algorithm is presented. The article analyzed the application of the algorithm in function optimization and traveling salesman problem (TSP) and mobile robot path planning problem. For the mobile robot path planning problem, the artificial fish in the distance with the target as the target function is referred to as the concentration of food. The distance between two adjacent grids is referred to as the largest mobile step. Under the condition of the preset, cluster behavior and collision behavior of the artificial fish algorithm are performed. In the current field of vision domain optimal node is searched to obtain path planning for the mobile robot.3. The traditional artificial fish swarm algorithm is affected by the visual field parameters, and the step size of the artificial fish also constrained the performance of the artificial fish swarm algorithm. The step size of the artificial fish is fixed and it can affect the global search speed of the algorithm is affected. So, a kind of logarithmic function adaptive artificial fish swarm algorithm is proposed. Based on the weighted average distance of artificial fish swarm, the logarithmic function is used as the moving factor of the step size. Also, the algorithm is applied to the function optimization and TSP problem as well as the path planning problem of the mobile robot. Simulation results show that, compared with the traditional artificial fish swarm algorithm and weighted average distance artificial fish swarm algorithm, the presented algorithm has relatively higher optimization ability, and the global search ability is much better.4. The artificial fish swarm algorithm may make the diversity of the population decreased in the optimization problem, it will produce stagnation phenomenon. Based on the weighted average distance artificial fish swarm algorithm and logarithmic function adaptive artificial fish swarm algorithm, the Gauss variation behavior is adopted, and a kind of the Gauss mutation artificial fish swarm algorithm is proposed in this paper. This algorithm not only retains all the characteristics of the two improved algorithms, but also improves the diversity of the population and the ability to obtain the global optimal value. Through the analysis of the traditional artificial fish swarm algorithm and the application of the three fish swarm algorithm in function optimization, the TSP and mobile robot path planning problem. Gaussian mutation artificial fish swarm algorithm has the strongest searching ability and optimization effect.5. Path tracking control system of the mobile robot is designed based on DSP. Through the artificial fish swarm algorithm, the parameters of PID controller are optimized online, and the model of tracking control system is established using MATLAB. The numerical experiment results show that the control system has a good control performance. On the basis of the result, the experimental platform of the mobile robot is designed. Mobile robot obstacle avoidance control system is built though the DSP chip and C++ programming. Also, obstacle avoidance experiment is completed by building environment of obstacle avoidance. The experimental results show that the mobile robot could realize the obstacle avoidance function.