CPS: Breakthrough: Robust Team-Triggered Coordination for Real-Time Control of Networked Cyber-Physical Systems

The overarching project goal is to advance the design of opportunistic state-triggered aperiodic controllers for networked cyber-physical systems.  This poster considers the problem of opportunistic human-robot collaboration to solve multi-objective optimization problems. We consider scenarios where a human decision maker works with a robot in a supervisory manner in order to find the best Pareto solution to a given optimization problem.  The human has a time-invariant function that represents the value she gives to the different outcomes. However, this function is implicit, meaning that the human does not know it in closed form, but can respond to queries about it. We provide event-triggered designs that allow the robot to efficiently query the human about her preferences at discrete instants of time. For different models of human behavior, we establish the  existence of a minimum interexecution time and the global asymptotic convergence of the resulting executions to the solution of the multi-objective optimization problem.