Dual-mode Dynamic Window Approach to Robot Navigation with Convergence Guarantees
For mobile robot navigation, this work provides a novel method with convergence guarantees, addressing a known bottleneck in the dynamic window approach.
The paper introduces a dual-mode model predictive control framework that combines the dynamic window approach with reference tracking controllers, achieving guaranteed convergence to a goal location while navigating unknown environments at high speeds. The framework is validated through simulation and hardware experiments.
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.