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

TL;DR

This paper introduces a dual-mode model predictive control framework that enhances robot navigation by combining dynamic window approach with reference tracking, ensuring convergence and high-speed obstacle avoidance in unknown environments.

Contribution

It presents a novel dual-mode control algorithm that guarantees convergence and incorporates complex robot models into dynamic window navigation.

Findings

Successful simulation and hardware tests demonstrate real-time feasibility.

Framework effectively handles dynamic constraints and stability.

Achieves high-speed navigation with obstacle avoidance guarantees.

Abstract

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.