Flying robots: modeling, control and decision making

This paper presents a flight management system (FMS) implemented as on-board intelligence for rotorcraft-based unmanned aerial vehicles (RUAV's), in order to gradually refine given abstract mission commands into real-time control signals for each vehicle. A strategy planner uses the probabilistic decision making algorithms to determine suboptimal action at each time step. A graphical interface on ground station enables human intervention. We derive nonlinear dynamics model upon which we design a tracking control layer using nonlinear model predictive control and integrate with a trajectory generator for logistical action planning. The proposed structure has been implemented on Berkeley RUAVs and validated in probabilistic pursuit-evasion games to show the possibility of intelligent flying robots.