Abstract-This paper describes an approach to the modeling and control of multiagent populations composed of a large number of agents. The complexity of population modeling is avoided by assuming a stochastic approach, under which the agent distribution over the state space is modeled. The dynamics of the state probability density functions is determined, and a control problem of maximizing the probability of robotic presence in a given region is introduced. The Minimum Principle for the optimal control of partial differential equations is exploited to solve this problem, and it is applied to the mission control of a simulated large robotic population.

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Improvement in airport operations using optimization schemes has been an active research area in the recent years. Particular attention has been given to improve taxiway and runway queue operations. However, once these operations are improved by an efficient taxiway schedule, its execution relies on the planning of ramp-area aircraft movements. An important step in the integration of the taxiway schedule with the planning of ramp-area aircraft maneuvers is to understand the constraints imposed on the aircraft trajectories due to the geometry of ramp-area and aircraft kinematics. Data for ramp trajectories are usually unavailable. To address this, we use an inexpensive scaled-down robot experiment to collect some critical data about aircraft trajectories. Ramp movement trajectories are then modeled by stochastic processes since they are heavily dependent on the human operator. We use the stochastic model to analyze the relationship between aircraft pushback time intervals and ramp-area conflicts. We then discuss constraints that can be imposed on aircraft pushback intervals to avoid any conflicts among trajectories.

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