A Path-Planning Performance Comparison of RRT*-AB with MEA* in a 2-Dimensional Environment

Iram Noreen,Amna Khan,Khurshid Asghar,Zulfiqar Habib

doi:10.3390/sym11070945

Iram Noreen, Amna Khan + Show 2 more

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https://doi.org/10.3390/sym11070945

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Abstract

With the advent of mobile robots in commercial applications, the problem of path-planning has acquired significant attention from the research community. An optimal path for a mobile robot is measured by various factors such as path length, collision-free space, execution time, and the total number of turns. MEA* is an efficient variation of A* for optimal path-planning of mobile robots. RRT*-AB is a sampling-based planner with rapid convergence rate, and improved time and space requirements than other sampling-based methods such as RRT*. The purpose of this paper is the review and performance comparison of these planners based on metrics, i.e., path length, execution time, and memory requirements. All planners are tested in structured and complex unstructured environments cluttered with obstacles. Performance plots and statistical analysis have shown that MEA* requires less memory and computational time than other planners. These advantages of MEA* make it suitable for off-line applications using small robots with constrained power and memory resources. Moreover, performance plots of path length of MEA* is comparable to RRT*-AB with less execution time in the 2D environment. However, RRT*-AB will outperform MEA* in high-dimensional problems because of its inherited suitability for complex problems.

Highlights

IntroductionMobile robots have been effectively adapted to carry out vital unmanned tasks in various fields
Over the past decade, mobile robots have been effectively adapted to carry out vital unmanned tasks in various fields
The results show that MEA* [20] performs better than A* and HPA* based on the following parameters: number of turns, execution time, and memory requirements

Summary

Introduction

Mobile robots have been effectively adapted to carry out vital unmanned tasks in various fields. Application areas of path-planning algorithms include but are not confined to security, vigilance [1], planetary exploration [2], route planning of Unmanned Aerial Vehicle (UAV) [3,4], and molecular simulation [5]. Path-planning for mobile robots deals with feasible path generation from a starting position to a goal position by avoiding collision with obstacles in an environment [6]. The criteria of optimal path for mobile robots is often based on one or more features such as shortest distance, low risk, smoothness, maximum area coverage, and fewer energy requirements considering different application constraints [3,6]. The shortest route would be preferred for a robotic vehicle on the road, whereas path smoothness would be required in case of rough terrain [8]. Time and memory-efficient mobile robot path-planning saves mobile robot wear and capital expenditure [9]

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