Multiple Waypoint Navigation for Mobile Robot Using Control Lyapunov-Barrier Function (CLBF)

We implemented the Control Lyapunov-Barrier Function (CLBF) method on the Autonomous Mobile Robot (AMR) hardware using the inverse kinematics of four mecanum wheels. The CLBF method is used to obtain stability and safety in the system. The stability of the system is defined when the AMR is able to r...

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Bibliographic Details
Published in2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) pp. 230 - 235
Main Authors Fachri, Ridlho Khoirul, Romdlony, Muhammad Zakiyullah, Rosa, Muhammad Ridho
Format Conference Proceeding
LanguageEnglish
Published IEEE 16.06.2022
Subjects
autonomous mobile robot
control lyapunov-barrier function
navigasi waypoint
Navigation
Robot kinematics
Robot vision systems
Safety
Stability analysis
Switches
Wheels
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Summary:We implemented the Control Lyapunov-Barrier Function (CLBF) method on the Autonomous Mobile Robot (AMR) hardware using the inverse kinematics of four mecanum wheels. The CLBF method is used to obtain stability and safety in the system. The stability of the system is defined when the AMR is able to reach the specified equilibrium point and the safety of the system is defined when the AMR is able to avoid the existing unsafe state. Waypoint navigation is used to provide several points of equilibrium so that the robot can move to the desired coordinate points. In this paper, we do not use a local sensor such as an encoder, but use a global sensor, namely a camera, to read the coordinates of the AMR position. We use a microcontroller to receive the coordinates of the x and y positions of the BLOB detection. The test was carried out three times with each time testing through three waypoints and one predetermined unsafe state. This study resulted in the percentage value of the implementation success of 76.47%, this value is the result of a comparison of the path generated by the simulation with Matlab and the path from the AMR real plant.
DOI:10.1109/CyberneticsCom55287.2022.9865390