Optimal task allocation in multi-human multi-robot interaction

Multi-human multi-robot interaction is a complex system in which robots, e.g., unmanned aerial vehicles, may share information with a group of human operators to perform geographically-dispersed priority-based tasks within a specified time. In this complex system, the key is to optimally allocate tasks comprising of high-risk and low-risk information at multiple-levels in order to maximize effectiveness of the entire system given the limited resources. A multi-level programming model is developed in which an agent allocates information received from multiple robots to multiple team leaders who in turn distribute information to operators within their teams. The objective of the agent is to optimally allocate tasks to multiple team leaders to maximize the overall system performance and to minimize the processing cost and time while considering human factors. The developed model is solved using backward induction and details are presented in reverse time sequence. If human factors are included along with the productivity metrics then the performance of the multi-human multi-robot interaction systems can be improved.