EAGER: CNS: Misdirection in Robot Teams: Exploiting Organizational Principles for Operational Advantage

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Trust, dependability, cohesion, and capability are integral to an effective team. These attributes are the same for teams of collaborating robots and humans as well as all-robot teams. For a team to remain effective in the presence of faults, failures, and errors from nature or intent, team members must have sufficient perception and understanding of the problem and possible solutions to make appropriate adjustments in their operations. When multiple teams with competing incentives are tasked, a strategy, if available, may be to weaken, influence or sway the attributes of other teams and limit their understanding of their full range of options. Such strategies are widely found, for example, in nature, sporting contests, and military operations, where strategies such as feints and misdirection often appear. This project focuses on one class of higher-level strategies for multi-robots: namely, to misdirect and where feasible to counter misdirection. As multi-robot systems become more autonomous, distributed, networked, numerous, and with more capability to make critical decisions, the prospect for intentional and unintentional misdirection must be anticipated. This project will support robust multi-robotic operations in important strategic domains such as security and military operations. The project studies strategies to enable co-robots, multi-robots and teams of multi-robots to model, generate, and cope with misdirection in various situations. This research direction in robotic control offers a novel approach to resilience in and among these teams to these forms of possible disruption. Computational models, drawn particularly from studies of human endeavors and group behaviors, provide a general framework for understanding, producing, and countering misdirection in robotic systems. A framework of computational models will be designed using recursive schema-theoretic models of behaviors at the individual and team levels, building on decentralized methods of control and communication, to provide robustness in the presence of noisy and chaotic environments.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.