In cooperative multi-robot object transportation, several autonomous robots navigate cooperatively in either a static or a dynamic environment to transport an object to a goal location and orientation. The environment may consist of both fixed and removable obstacles and it will be subject to uncertainty and unforeseen changes within the environment. More than one robot may be required for handling heavy and large objects. This paper presents a modified Q-learning approach for object transportation utilizing cooperative and autonomous multiple mobile robots. A modified version of Q-learning is presented, which employs for effective robot coordination. A solution to the action selection conflicts of the robots is presented, which helps to improve the real time performance and robustness of the system. As required in the task, the paper presents an algorithm for object pose estimation, by utilizing the laser range finder and color blob tracking. The developed techniques are implemented in a multi-robot system in laboratory. Experimental results are presented to demonstrate the effectiveness of the developed multi-robot system and its underlying methodologies.
Autonomous robotic valve turning: A hierarchical learning approach