Abstract
Automated Guided Vehicle (AGV) indoor autonomous cargo handling and commodity transportation are inseparable from AGV autonomous navigation, and positioning and navigation in an unknown environment are the keys of AGV technology. In this paper, the extended Kalman filter algorithm is used to match the sensor observations with the existing features in the map to determine the accurate positioning of the AGV. This paper proposes an improved joint compatibility branch and bound (JCBB) method to divide the data and then randomly extract part of the data in the divided data set, thereby reducing the data association space; then, the JCBB algorithm is used to perform data association and finally merge the associated data. This method can solve the problem of the increased computational complexity of JCBB when the amount of data to be matched is large to achieve the effect of increasing the correlation speed and not reducing the accuracy rate, thereby ensuring the real-time positioning of the AGV.
Highlights
Used in Chinese, represents that a feature point is used in global coordinates in the Automated Guided Vehicle (AGV) coordinate system expresses. [xr(k), yr(k), θr(k)] represents the global coordinate system of AGV at time KXwOwYw. e state vector of the θr(k) representation of AGV in global coordinates xw is shown in Figure 2: it is the schematic diagram of the AGV coordinate system
If there is no initialisation operation at the initial time of AGV, the subsequent dead reckoning will become quite complex, and the accuracy of AGV positioning will be affected, which will lead to the low accuracy of the environment map constructed at x0 0 0 0 T. e covariance matrix corresponding to the position and pose of AGV and the state vector composed of all feature points in the map is as follows: 0 ··· 0
A very important part of SLAM research, is a process of matching the existing environmental characteristics and the observed environmental characteristics of robots to determine whether they have a common source, to filter out the noise points collected by lidar, and to ensure the correct matching of the same environmental features observed at different times. e correct data association will make the robot positioning accurate and ensure the correctness of the environment map established by the robot
Summary
A mobile platform developed for ROS, has high precision, a heavy load, long battery life, and strong scalability. E conversion between the two needs to establish the relationship between the AGV coordinate system and the global coordinate system. E establishment of the AGV motion model is based on the encoder installed on each wheel to count the output pulse number of each wheel. E motion model establishes the relationship between the AGV distance and the angle from the previous time to the moment, as shown in Figure 3 if the AGV state vector is [xr(k), yr(k), θr(k)]. En, the state vector at K + 1 is [xr(k + 1), yr(k + 1), θr(k + 1)]T. e encoder, which is used to measure the moving distance of AGV during driving, is located on the left and right wheels.
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