  In this paper, a novel, dual-mode model predictive control framework is
introduced that combines the dynamic window approach to navigation with
reference tracking controllers. This adds a deliberative component to the
obstacle avoidance guarantees present in the dynamic window approach as well as
allow for the inclusion of complex robot models. The proposed algorithm allows
for guaranteed convergence to a goal location while navigating through an
unknown environment at relatively high speeds. The framework is applied in both
simulation and hardware implementation to demonstrate the computational
feasibility and the ability to cope with dynamic constraints and stability
concerns.
