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When teams of mobile robots are tasked with different goals in a competitive environment, misdirection and counter-misdirection can provide significant advantages. Researchers have studied different misdirection methods but the number of approaches on counter-misdirection for multi-robot systems is still limited. In this work, a novel counter-misdirection approach for behavior-based multi-robot teams is developed by deploying a new type of agent: counter misdirection agents (CMAs). These agents can detect the misdirection process and “push back” the misdirected agents collaboratively to stop the misdirection process. This approach has been implemented not only in simulation for various conditions, but also on a physical robotic testbed to study its effectiveness. It shows that this approach can stop the misdirection process effectively with a sufficient number of CMAs. This novel counter-misdirection approach can potentially be applied to different competitive scenarios such as military and sports applications. 

This NSF funded project currently underway studies strategies to enable 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. 

Teams of robots tasked with making critical decisions in competitive environments are at risk for being shepherded or misdirected to a location that is advantageous for a competing team. Our lab is working to understand how adversarial teams of robots can successfully move their competition to desired locations in part so that we can then devise practices to counter these strategies and help make team functioning more successful and secure. In this paper, preliminary research is presented that studies how a team of robots can be shepherded or misdirected to a disadvantageous location. We draw inspiration from herding practices as well as deceptive practices seen in higher-order primates and humans. We define behaviors for the target (mark) agents to be moved as well as members of the shepherding team (a pushing agent and pulling shills) and present simulation results showing how these behaviors move robots to a desired location. These behaviors were implemented and trialed on hardware platform. A discussion of ongoing research into understanding misdirection in multi-robot teams concludes this paper. 

Trust, dependability, cohesion, and capability are integral to an effective team. These attributes are the same for teams of robots. 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 in nature and in sporting contests such as feints, misdirection, etc. This talk focuses on one class of higher-level strategies for multi-robots, i.e., to intentionally misdirect using shills or confederates where needed, and the ethical considerations associated with deploying such teams. 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. While benefits are clearly apparent to the team performing the deception, ethical questions surrounding the use of misdirection or other forms of deception are quite real.