Reduction of Path Length of Notable Path Algorithms Using the KNS Algorithm

Abstract

Path planning refers to designing a reliable, feasible, optimum, safe, and collision-free path with the shortest distance that takes a mobile robot from the start position to the goal point within an environment. To ensure the successful operation of a robot, an effective and efficient Path planning technique that guarantees obstacle avoidance and an optimal path must be adopted. This paper applies a novel path length reduction technique – the Kenneth, Nnanna, and Saleh (KNS) algorithm-to notable path planning algorithms (APF, A*, RRT, and RRT*) to shorten their path length by reducing the waypoints' bends and retaining the obstacle avoidance capability of the algorithms. We simulated applying the technique to different notable algorithms in an environment configured with varying obstacles. We compared the resultant paths with the original paths. The results show that the KNS algorithm is very effective and can significantly reduce the path length of the notable algorithms.