Preventing Local Minima in Lyapunov-Based Control Scheme through Iterative Repulsive Potential Augmentation

This study introduces the Iterative Repulsive Potential Augmentation (IRPA), a motionplanning framework designed to overcome the local minima problem in Artificial Potential Fields. IRPA builds upon the Lyapunov-based Control Scheme (LbCS) by iteratively increasing the repulsive potential in regions where the robot becomes trapped.
This augmentation is repeated across successive iterations until the robot can successfully escape the local minimum and reach its goal. The approach provides a computationally efficient solution to the local minima problem while preserving the inherent efficiency and scalability advantages of the LbCS.