New Fusion Algorithm provides an alternative approach to Robotic Path planning

TL;DR

This paper introduces a novel fusion algorithm combining optimized A* and artificial potential field methods for efficient, smooth, and cost-effective robotic path planning in dynamic 2D environments, outperforming traditional approaches.

Contribution

The paper proposes a new fusion algorithm that integrates optimized A* and artificial potential field methods for improved robotic path planning.

Findings

The fusion algorithm produces smoother paths.

It is more time-efficient than conventional A*.

The method is cost-effective and feasible in simulations.

Abstract

For rapid growth in technology and automation, human tasks are being taken over by robots as robots have proven to be better with both speed and precision. One of the major and widespread usages of these robots is in the industrial businesses, where they are employed to carry massive loads in and around work areas. As these working environments might not be completely localized and could be dynamically changing, new approaches must be evaluated to guarantee a crash-free way of performing duties. This paper presents a new and efficient fusion algorithm for solving the path planning problem in a custom 2D environment. This fusion algorithm integrates an improved and optimized version of both, A* algorithm and the Artificial potential field method. Firstly, an initial or preliminary path is planned in the environmental model by adopting the A* algorithm. The heuristic function of this A* algorithm is optimized and improved according to the environmental model. This is followed by selecting and saving the key nodes in the initial path. Lastly, on the basis of these saved key nodes, path smoothing is done by artificial potential field method. Our simulation results carried out using Python viz. libraries indicate that the new fusion algorithm is feasible and superior in smoothness performance and can satisfy as a time-efficient and cheaper alternative to conventional A* strategies of path planning.