Page 247 - 2024-Vol20-Issue2

Received: 21 August 2023 | Revised: 7 October 2023 | Accepted: 8 December 2023

DOI: 10.37917/ijeee.20.2.21 Vol. 20 | Issue 2 | December 2024

Open Access

Iraqi Journal for Electrical and Electronic Engineering

Original Article

Efficient Path Planning in Medical Environments:

Integrating Genetic Algorithm and Probabilistic

Roadmap (GA-PRM) for Autonomous Robotics

Sarah Sabeeh1, Israa S. Al-Furati*2
1Computers Engineering Department, University of Basrah, Basrah, Iraq
2Electrical Engineering Department, University of Basrah, Basrah, Iraq

Correspondance
*Israa S. Al-Furati
Electrical Engineering Department,
University of Basrah, Basrah, Iraq
Email:israa.sabri@uobasrah.edu.iq

Abstract
Path-planning is a crucial part of robotics, helping robots move through challenging places all by themselves. In this
paper, we introduce an innovative approach to robot path-planning, a crucial aspect of robotics. This technique combines
the power of Genetic Algorithm (GA) and Probabilistic Roadmap (PRM) to enhance efficiency and reliability. Our
method takes into account challenges caused by moving obstacles, making it skilled at navigating complex environments.
Through merging GA’s exploration abilities with PRM’s global planning strengths, our GA-PRM algorithm improves
computational efficiency and finds optimal paths. To validate our approach, we conducted rigorous evaluations against
well-known algorithms including A*, RRT, Genetic Algorithm, and PRM in simulated environments. The results were
remarkable, with our GA-PRM algorithm outperforming existing methods, achieving an average path length of 25.6235
units and an average computational time of 0.6881 seconds, demonstrating its speed and effectiveness. Additionally, the
paths generated were notably smoother, with an average value of 0.3133. These findings highlight the potential of the
GA-PRM algorithm in real-world applications, especially in crucial sectors like healthcare, where efficient path-planning
is essential. This research contributes significantly to the field of path-planning and offers valuable insights for the
future design of autonomous robotic systems.

Keywords
Genetic Algorithm, Medical Robotics, Path-planning, Probabilistic Roadmaps, Robot Navigation, Static and Dynamic
Obstacles.

I. INTRODUCTION and assisting with diagnostics. Advanced path planning is vi-
tal for these robots to navigate crowded and dynamic medical
In the field of robotics, path planning is essential for au- environments efficiently. Integrating AI-powered path plan-
tonomous robots to make smart decisions while navigating ning into healthcare robotics can reshape workflows, enhance
complex terrains. This skill becomes increasingly impor- patient care, and advance medical technology [2].
tant as robots are used in various industries, including search
and rescue, environmental monitoring, and space exploration. This research focuses on developing an innovative path
Path planning not only affects efficiency but also safety, as it planning algorithm tailored for robots operating in medical en-
allows robots to avoid obstacles and collisions [1]. In the med- vironments. The algorithm aims to adapt to dynamic obstacles,
ical field, robots play a crucial role in supporting healthcare minimize path length, and ensure smooth robot movements. It
professionals by performing tasks like transporting supplies combines Genetic Algorithms and Probabilistic Roadmaps to

This is an open-access article under the terms of the Creative Commons Attribution License,
which permits use, distribution, and reproduction in any medium, provided the original work is properly cited.
©2024 The Authors.
Published by Iraqi Journal for Electrical and Electronic Engineering | College of Engineering, University of Basrah.

https://doi.org/10.37917/ijeee.20.2.21 |https://www.ijeee.edu.iq 243