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.
reactive navigation, dynamic environments, convergence, non-point destinations