Research On SLAM And Path Planning Algorithm For Mobile Robots

In recent years,intelligent robot technology has been widely used in fields such as driverless cars,drones,and sweeping robots.Mobile robot SLAM and navigation technology are important research directions for intelligent robots.Its key technologies include positioning,map construction,and path planning.The research core of this thesis is the mobile robot SLAM and path planning algorithm.Firstly,the odometer-based motion model is established in this thesis.Due to the problem of error accumulation in odometer model,the lidar observation model is established to eliminate the error.In the aspect of environment map construction,raster map is selected as the map representation model,and the derivation and simulation are carried out.The transformation method between the robot coordinate system and the world coordinate system is given.Secondly,EKF SLAM,RBPF SLAM and Hector_SLAM is introduced in this thesis,the advantages and disadvantages of the three algorithms are compared,and detailed derivation and simulation are carried out.For the problem that the EKF_SLAM algorithm can only be used in weakly nonlinear systems,the RBPF_SLAM algorithm has a large amount of calculation,and the Hector_SLAM algorithm relies heavily on high-precision lidar and other shortcomings,An improved Hector_SLAM algorithm based on information fusion is proposed.By combining the Hector_SLAM algorithm with the odometer and IMU equipment,the accuracy requirements of the lidar are successfully reduced,and the composition accuracy and stability are improved.Aiming at the defect that the artificial potential field method cannot reach the target point under certain circumstances,an improved artificial potential field method is proposed,which makes the mobile robot reach any target point successfully.Then,in terms of path planning for mobile robots,the ant colony algorithm is used as the global path planning algorithm,and the artificial potential field method is used as the local path planning algorithm in this thesis.Aiming at the problem that the ant colony algorithm has slow convergence speed and is easy to fall into local optimality,a new improved ant colony algorithm is proposed.By combining the bee colony algorithm,changing the initial pheromone concentration and optimizing the pheromone update rules,the convergence speed is accelerated and the local optimum is avoided effectively.Aiming at the defect that the artificial potential field method cannot reach the target point under certain circumstances,an improved artificial potential field method is proposed,which makes the mobile robot reach any target point successfully.Simulation experiments are carried out on the path planning algorithm before and after the improvement,which proved that the improved algorithm in this thesis can effectively improve the planning ability and have a good navigation effect.Finally,this thesis introduces the hardware structure of the mobile robot system and the system architecture of ROS robot operating system.Using the built mobile robot system,the improved Hector_SLAM algorithm is successfully used to build an environment map,and the improved ant colony algorithm and artificial potential field method are used to navigate on the obtained map.Successfully complete the path planning,and reach the designated target point.