Leader–Follower Approach for Non-Holonomic Mobile Robots Based on Extended Kalman Filter Sensor Data Fusion and Extended On-Board Camera Perception Controlled with Behavior Tree

Abstract

This paper presents a leader–follower mobile robot control approach using onboard sensors. The follower robot is equipped with an Intel RealSense camera mounted on a rotating platform. Camera observations and ArUco markers are used to localize the robots to each other and relative to the workspace. The rotating platform allows the expansion of the perception range. As a result, the robot can use observations that are not within the camera’s field of view at the same time in the localization process. The decision-making process associated with the control of camera rotation is implemented using behavior trees. In addition, measurements from encoders and IMUs are used to improve the quality of localization. Data fusion is performed using the EKF filter and allows the user to determine the robot’s poses. A 3D-printed cuboidal tower is added to the leader robot with four ArUco markers located on its sides. Fiducial landmarks are placed on vertical surfaces in the workspace to improve the localization process. The experiments were performed to verify the effectiveness of the presented control algorithm. The robot operating system (ROS) was installed on both robots.

A review on positioning techniques of mobile robots

“…In this method, it is important to select suitable filters. One can distinguish between particle filters [33] and the well-known Kalman filter [34,35]. Furthermore, it is worth mentioning the errors that can result, for example, from wheel slippage [16].…”

“…The comparison of different technologies to localize a mobile robot, with their advantages and disadvantages, is presented in Table 2. Errors due to the design of the sensors themselves [7], [12], [18], [20], [30][31][32], [35], [48], [49] QRCode/ArUco Low Quick and simple application, easy to change course -new marker Susceptible to soiling [7], [8], [35] RFID Low/ Medium…”

“…Global location determination and simple navigation thanks to extensive maps Limited range (mainly indoors) [12], [17], [28,29], [35], [47] Radio waves Medium Good accuracy, resistant to harsh environmental conditions Application of complex data processing algorithms [20][21][22][23][24][25][26], [49] Vision system High Very high accuracy, can be used virtually anywhere, ambient mapping Mainly based on artificial intelligence, so very sophisticated image recognition algorithms [9][10][11][12], [27], [41], [46] Follow Poor dirt resistance, complex information processing [9], [13], [45,46], [48] Other markers [-] Easy to find (self) robot, with known marker location High density of markers needed for decent work [10], [27], [48] 3. A review of the selected localization methods…”