Model predictive unified planning and control of rotary-wing unmanned aerial vehicle

This paper presents an integrated planning and control algorithm for the navigation of an autonomous rotary wing unmanned aerial vehicle (RUAV) in a cluttered environment. The model predictive control (MPC) combined with a potential field-like function integrates the planning and control in a single step. This strategy enables the generation of dynamically feasible motion because the planning is done in the action space of the RUAV. Moreover, it is able to respond promptly to abrupt changes in the environment because the reactive obstacle avoidance method only considers local sensory information. Finally, the shortsightedness of control-based reactive methods can be relieved by the larger planning horizon of the model predictive control. Simulation results show that the proposed integrated planning and control method can complete the navigation mission successfully in cluttered environment.