Research On Positioning Technology Of Mobile Robot Autonomous Static Charging

With the rapid development of science and technology,robots are playing an important role in many fields.People are becoming more and more dependent on robots,and their requirements for working time and efficiency performance of robots(such as substation inspection robots,warehousing and logistics AGVs,service robots,etc.)are constantly improving,which requires more intelligent energy supply methods of robots:Robot energy supply has gradually changed from the traditional manual charging mode to the autonomous static charging mode of robot,and its charging mode has also changed from the traditional wired plugging to radio energy transmission.Autonomous static charging of mobile robots using radio energy transmission requires higher precision autonomous navigation ability,which puts forward higher requirements for positioning accuracy of mobile robots.At present,due to the insufficient hardware processing of the robot itself,the influence of external environment(such as uneven ground,wheel slip,etc.)and certain deficiencies in positioning algorithm,the positioning of autonomous static charging of mobile robots will have problems of low accuracy and difficult stability guarantee,which makes the deviation distance between the receiving coil installed on the robot and the transmitting coil of the charging station too large.As a result,its charging efficiency is low and even can not complete the autonomous static charging.Therefore,starting from the perspective of realizing the positioning of the robot with high precision and high stability,this topic improves the charging efficiency of the robot by optimizing the pose of the robot,and carries out the research on the positioning technology of the autonomous static charging of the mobile robot.The main research contents are as follows:(1)Firstly,the charging demand performance index is proposed by analyzing the charging demand of the robot in this topic;Secondly,by establishing a radio energy transmission model to analyze the power transmission characteristics,the positioning accuracy of the performance index meeting the charging requirements was calculated,namely,the positioning accuracy required by the robot to complete the autonomous static charging.Finally,in order to achieve the above positioning accuracy,the positioning error sources affecting the autonomous static charging of the robot were analyzed by establishing the robot positioning model(including the kinematic model and the sensor model,etc.).Aiming at these positioning error sources,the positioning algorithm framework of the autonomous static charging of the mobile robot was designed.(2)The initial position of the mobile robot during the autonomous static charging task is determined.When the autonomous static charging task of the robot is not started,the adaptive Monte Carlo positioning algorithm is used to calculate the robot’s position in the global coordinate system.Gazebo software is used to build a physical environment,and the effectiveness of the algorithm is verified by simulation in the environment,which can make the robot get more accurate initial pose.(3)The navigation path of the robot to the charging station is optimized by the corresponding algorithm,which reduces the influence of internal hardware and external environment on the posture of the robot and improves the positioning accuracy of the autonomous static charging of the robot.In view of internal hardware errors,such as unequal drive wheel diameter and uncertain wheel pitch,which lead to inaccurate linear and rotational motion of the robot,UMBmark algorithm is adopted for system parameter verification to reduce system errors.Experiments show that the positioning accuracy of the robot after verification is significantly improved compared with that before verification.In order to reduce the influence of external environment on the positioning accuracy of the robot,such as uneven ground and wheel slip,an improved untraceless Kalman filter algorithm(UKF)is used to integrate the data of odometer,inertial measurement unit and two-dimensional lidar.The effectiveness of the improved algorithm was verified in the simulation environment.Compared with the traditional UKF filtering algorithm,AFUKF algorithm and cartographer algorithm,the error rate of the proposed algorithm was stable within 7% in the position estimation experiment of autonomous static charging,which showed higher accuracy and met the positioning requirements.