The Research On Navigation Algorithm For Mobile Robot Based On CML

With the rapid development of science and technology,robot technology is also changing with each passing day.Robots in civil,military and other fields have become increasingly important.Mobile robots in the unknown environment,feel the external environment through their sensors,to achieve movement and complete the specific work.Mobile robot positioning and map creation is a hot topic in the field of robot research,and also an important part of navigation.However,in many environments robots can not use the global positioning system for positioning,and it is very difficult or even impossible to obtain the robot working environment map in advance.In this case robot needs to locate and create the map and navigate in a completely unknown environment under the condition of uncertainty of its own location.This is the simultaneous positioning and map creation(CML)problem of mobile robot.CML is a fundamental basis for the field of intelligent robot research,and these robots are able to complete the key issues of autonomous navigation.We know that the unmanned aerial vehicles,intelligent robots and unmanned areas of the barriers or path planning are based on this.The object of this study is a five-wheeled robot working in the indoor environment,through the laser range finder to achieve the measurement of the distance around indoor obstacles,thus reflecting the distribution of obstacles.Laser range finder is the main external sensor,while gyroscope and encoder are internal sensors.through the Extended Kalman Filter(EKF)algorithm to integrate the two information with mutual correction,so as to realize the autonomous localization、map creation and navigation.(1)Based on the understanding of working mechanism of the RPLIDAR laser range finder,we grasp the straight line extraction method based on the measurement point set.The RPLIDAR based map creation algorithm is constructed and simulated to verify the effectiveness of the algorithm.(2)Study the basic theory of CML(concurrent construction and positioning).The basic concepts of CML,including related terms and basic framework,are discussed.The basic models of CML are discussed,including robot motion model,sensor observation model and actuator noise model.(3)Based on the analysis of Kalman filter for the lack of robot positioning,the advantages of Extended Kalman Filter in robot positioning are compared and analyzed.And then construct the CML algorithm based on Extended Kalman Filter,and simulate it to illustrate the effectiveness of the algorithm.(4)Based on studying the principle of particle filter and FastCML algorithm,the FastCML algorithm based on particle filter is constructed and experimentally simulated to test the effectiveness of the algorithm.(5)Based on the information of robot’s real-time position and the environment obtained from CML algorithm navigation algorithms are put forward and then introduces the robot’s structure of hardware and software above all make the robot autonomously move along the optimal path from the starting point to the target point without collision.