Reducing Traverse Space in Path Planning using Snake Model for Robots

Abstract

Path planning for robots is one of the important aspects of robots where they need to get an optimal and obstacle-free path from source to destination. Getting an optimal and obstacle-free path, while traversing is the core research issue in robotics. To address this research issue, several techniques were proposed and implemented in the past, and still, a good amount of work is being carried on. Some well-known algorithms for path planning are ${A}^{*}$, Probabilistic Roadmap Planner (PRM), Rapidly Exploring Random Tree (RRT) and RRT Smooth. Fundamentally, to find an optimal and obstacle-free path, any path planning algorithms needs to explore the whole configuration space, which increases traversing time and efforts. However, the exploration of the whole configuration space can be done efficiently, which result in improving the performance of the path planning algorithms. This paper addresses the use of Snake Model as a preliminary step to path planning algorithms to find optimal and obstacle-free paths for robots efficiently by reducing the traversing in configuration space. Several experiments have been carried out to show the effectiveness of the proposed setup. In the experiments, the Snake model has been applied along with some standard algorithms such as ${A}^{*}$, PRM, RRT and RRT Smooth, where the parameters such as path length, No. of Moves and Time taken are used to record the performance.