Convergent wheeled robot navigation based on an interpolated potential function and gradient.

The article presents a novel idea to construct a smooth navigation function for a wheeled robot based on grid-based search, that enables replanning in dynamic environments. Since the dynamic constraints of the robot are also considered, the navigation function is combined with the model predictive control (MPC) to guide the robot safely to the defined goal location. The main novelty of this work is the definition of this navigation function and its MPC application with guaranteed closed-loop convergence in finite time for a non-holonomic robot with speed and acceleration constraints. The navigation function consists of an interpolated potential function derived from the grid-based search and a term that guides the orientation of the robot on continuous gradients. The navigation function guarantees convergent trajectories to the desired goal, results in smooth motion between obstacles, has no local minima, and is computationally efficient. The proposed navigation is also suitable in dynamic environments, as confirmed by experiments with a Husky mobile robot. • Navigation function created from the grid-based search enables real-time replanning. • Continuous navigation function is obtained by interpolating discrete grid-based costs. • MPC-based navigation takes velocity and acceleration constraints into account. • MPC with the control sequence generation scheme allows fast on-line computation. • The navigation approach guarantees closed-loop convergence for unicycle-type robots. [ABSTRACT FROM AUTHOR]