Research And Simulation Of The FastSLAM2.0 Algorithm And PGR Navigation Algorithm

By introducing the basic theory on modern robot ,we know the topic is the location of robot. The robot location and mapping is the important issues in the application of robot. To solve this problem better, this paper chose FastSLAM, FastSLAM made SLAM into two processes that robot estimated location and estimated the features' positions. Each particle in the particle filter indicates a robot movement path. We can use the observation information to calculate the weight of each particle and get the appraise of every path ,then we can improve both the veracity and the reliability.In the course of the robot application , another important issue is navigation control (path planning). In the path tracking control issues, as the Cartesian coordinate system of feedback can not be linear or smooth steady feedback controller design methods asymptotic stabilization system. In this paper, we try to use non-linear predictive control calm and restraint in control of the mobile robot kinematics constraints. So we proposed a new navigation algorithm - PGR algorithm.However, Matlab has a high price and have no enough support on service and technique. Basing on above reasons , this article use the Scilab to do large numbers of experiments. During the simulation process, we collect the data prepared by the internal sensor and external sensor, the data can be used to simulate the movement of a robot by the sensor feedback information. SLAM algorithm and navigation algorithm were written into programm files by scilab. Finally, we get the process flow chart of a mobile robot, then build the simulation environment of mobile robot by all data. By the complete design, the mobile robot can easily move in the reality .To control the vehicles movement similar to the vehicles with people driving, thus the movement of no target may also be true in reality.