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In this paper, a novel, dual-mode model predictive control framework isintroduced that combines the dynamic window approach to navigation withreference tracking controllers. This adds a deliberative component to theobstacle avoidance guarantees present in the dynamic window approach as well asallow for the inclusion of complex robot models. The proposed algorithm allowsfor guaranteed convergence to a goal location while navigating through anunknown environment at relatively high speeds. The framework is applied in bothsimulation and hardware implementation to demonstrate the computationalfeasibility and the ability to cope with dynamic constraints and stabilityconcerns.

Dual-mode Dynamic Window Approach to Robot Navigation with Convergence Guarantees