Collaborative swarm for multi-task pairing in unknown environment

Remotely operating teams of autonomous mobile robots for multi-task allocations in an unknown environment is an important and challenging task in many real world applications. However, a few of existing teleoperation methods have been aimed at finishing multiple tasks simultaneously when the environment that the robots are navigating is unknown. In real life, in order to achieve this, a teleoperated team of mobile robots can be split into several collaborative sub-teams according to their characteristics about different tasks. Each sub-team is formed by pairing team robots to a specific task to make sure of an enough capability of automatically sensing environments and accomplishing the specific task. Moreover, the robot-task pairing strategy based on the capability list of robots in a weighted attack guidance table (WAGT) is developed from the proven auction algorithm for multi-robot multi-task cases, which optimizes effects-based allocations based on a heuristic algorithm. In this paper, a control method to teleoperate teams or sub-teams of robots involving the robot-task pairing strategy is proposed to deal with different tasks simultaneously in an unknown environment. Simulation studies illustrate the efficacy of a teleoperator with the proposed control method for multi-task scenarios in an unknown environment.