Dynamic optimized bandwidth management for teleoperation of collaborative robots

Abstract

Teleoperation is widely used in the daily real-world missions. Particularly, teleoperation is employed to control swarms of robots from a remote site. Engaging multiple robots in complex tasks produces a more refined performance that cannot be achieved by a single robot. However, the quantity of information exchanged between operators and robots highly increases when more agents are employed in the task. The operator sends real-time commands to the robots and expects to receive instantaneous visual and haptic feedback reflecting the environment's characteristics surrounding the swarm. In addition, the necessity of communication among robots requires them to exploit some additional share of the scarce available bandwidth. In this thesis, real-time dynamic optimized bandwidth management in teleoperated collaborative robots is proposed. A main contribution of the proposed algorithm is to account for interesting events “IEs” occurring in the environment and for the change in the quality of collaboration “QoC” of the swarm in optimizing bandwidth allocation. Accordingly, dynamic bandwidth management is adopted in order to account for any deficiency in the collaborative performance of the swarm or in any change in the environment around the collaborating group to efficiently manage the scarcely available bandwidth. The preliminary work consists on managing bandwidth in teleoperation of single robot based on the occurrence of interesting events in the robot’s environment. Then, based on the initial work, we formulate a dynamic optimized bandwidth management algorithm for teleoperation of collaborative robots and present its mathematical solution. For the purpose of evaluating the suggested algorithm against two static algorithms, MATLAB simulations are first performed then experiments are conducted on humanoid robots. The comparison carried out in both validations demonstrates the superiority of dynamic optimization algorithm in terms of task and network performance. The developed algorith