Research On Waypoint Navigation Method For Mobile Robot In Unstructured Environment

Autonomous navigation ability of mobile robot is one of the key technologies of its intelligence degree,which is the basis of intelligent operation of mobile robot.At present,a large number of research and application have been achieved in autonomous navigation technology in structured environment,but the unknown nature and complexity of unstructured environment,which brings greater challenges to the autonomous navigation of mobile robots.In response to such problems,this paper proposes a waypoint navigation scheme for mobile robots in an unstructured environment.The waypoint in the navigation process are set by the monitoring software to optimize the starting point-end point navigation method to the starting point-passing points-end point,improve the efficiency of path planning while effectively avoiding the robot from falling into a dead zone.Secondly,the acquisition of the global environment map of unstructured environment requires a lot of manpower and material resources to enter the field survey and mapping,and the environment perception and reconstruction algorithm highly depends on the robot pose accuracy,so the reconstructed environment model in large scenes will be distorted with the drift of the pose.In this regard,this paper proposes to use satellite elevation maps instead of global environment maps for global path planning,and to guide mobile robots to move autonomously in complex environments.In this paper,firstly,the waypoint navigation system of mobile robot is designed,which is built on the multilink crawler mobile platform with strong environmental adaptability,and its kinematic model is established.Then,the road point navigation system of mobile robot is designed,and the main functions of each module of the system are clarified,and further research is carried out.Secondly,in order to realize the precise positioning of the robot,the complete position,attitude and speed information of the mobile robot are estimated based on multi-sensor fusion technology.Based on a full understanding of GNSS,INS and odometry positioning models and error sources,the Extended Kalman Filter algorithm was used to optimize the six degrees of freedom speed,position,attitude and sensor error parameters of the mobile robot according to the advantages and disadvantages of each sensor.Through experimental analysis,this method improves the positioning accuracy and robustness of the mobile robot,at the same time it can still maintain a high positioning accuracy in a certain period of time when the GPS signal is unlocked.Then,this paper adopts A*-PSO adaptive path planning method.Based on the A*algorithm,a nested particle swarm optimization(Particle Swarm Optimization,PSO)algorithm is used to expand the search neighborhood of the traditional A*algorithm and automatically adjust the movement.The step size significantly improves the efficiency of the traditional A*algorithm and implements adaptive path planning.Based on this algorithm,the satellite's global path planning method based on the star elevation map is studied.The global cost map model is calculated by the three terrain factors of the elevation map slope,terrain relief and terrain roughness,and the global path is searched based on this.At the same time,due to the limited accuracy of satellite maps,the global path can only be used as a guide for mobile robots.During the operation of mobile robots,local path planning is carried out by constantly updating and maintaining local environmental information to achieve the purpose of obstacle avoidance.In addition,considering the robot's dynamic and kinematic constraints,the Timed Elastic Band algorithm is used to modify the initial path to optimize the robot's trajectory.Finally,the sensor selection and platform construction of the crawler autonomous mobile robot were completed,and the waypoint navigation experiment of the mobile robot was completed.