Navigation Functions with Moving Destinations and Obstacles

Dynamic environments challenge existing robot navigation methods, necessitating either stringent assumptions on workspace variation or sacrificing collision avoidance and convergence guarantees. This paper shows that the navigation function methodology can preserve such guarantees in a dynamic sphere-world with moving obstacles and a time-varying goal, without prior knowledge of environment variation. Assuming bounds on speeds of robot destination and obstacles, and sufficiently higher maximum robot speed, the navigation function gradient can be used produce robot feedback laws that guarantee obstacle avoidance, and theoretical guarantees of bounded tracking errors and eventual convergence to the target in the case where the latter seizes to move. The efficacy of the gradient-based feedback controller derived from the new navigation function construction is demonstrated both in numerical simulations as well as experimentally.
This version of the article has been accepted for publication in Autonomous Robots, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: This article will be embargoed until 02/12/2024.
reactive navigation, dynamic environments, convergence, non-point destinations