Enhanced Route navigation control system for turtlebot using human-assisted mobility and 3-D SLAM optimization

Ankit Kumar; Kamred Udham Singh; Pankaj Dadheech; Aditi Sharma; Ahmed I Alutaibi; Ahed Abugabah; Arwa Mohsen Alawajy

doi:10.1016/j.heliyon.2024.e26828

Enhanced Route navigation control system for turtlebot using human-assisted mobility and 3-D SLAM optimization

Heliyon. 2024 Feb 28;10(5):e26828. doi: 10.1016/j.heliyon.2024.e26828. eCollection 2024 Mar 15.

Authors

Ankit Kumar¹, Kamred Udham Singh², Pankaj Dadheech³, Aditi Sharma⁴, Ahmed I Alutaibi⁵, Ahed Abugabah⁶, Arwa Mohsen Alawajy⁷

Affiliations

¹ Department of Information Technology, Guru Ghasidas Vishwavidyalaya, Bilaspur, Chhattisgarh, India.
² School of Computing, Graphic Era Hill University, Dehradun, India.
³ Department of Computer Science & Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan (SKIT), Jaipur, Rajasthan, India.
⁴ Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International University, Pune, Maharashtra, India.
⁵ Department of Computer Engineering, College of Computer and Information Sciences, Majmaah University, Majmaah, 11952, Saudi Arabia.
⁶ College of Technological Innovation, Zayed University, Abu Dhabi Campus, United Arab Emirates.
⁷ Computer Science Department, College of Computer and Information Science, King Saud University, Riyadh, Saudi Arabia.

Abstract

An autonomous, power-assisted Turtlebot is presented in this paper in order to enhance human mobility. The turtlebot moves from its initial position to its final position at a predetermined speed and acceleration. We propose an intelligent navigation system that relies solely on individual instructions. When there is no individual present, the Turtlebot remains stationary. Turtlebot utilizes a rotating Kinect sensor in order to perceive its path. Various angles were examined in order to demonstrate the effectiveness of the system in experiments conducted on a U-shaped experimental pathway. The Turtlebot was used as an experimental device during these trials. Based on the U-shaped path, deviations from different angles were measured to evaluate its performance. SLAM (Simultaneous Localization and Mapping) experiments were also explored. We divided the SLAM problem into components and implemented the Kalman filter on the experimental path to address it. The Kalman filter focused on localization and mapping challenges, utilizing mathematical processes considering both the system's knowledge and the measurement tool. This approach allowed us to achieve the most accurate system state estimation possible. The significance of this work extends beyond the immediate application, as it lays the groundwork for advancements in wheelchair navigation research by Dynamic Control. The experiments conducted on a U-shaped pathway not only validate the efficacy of our algorithm but also provide valuable insights into the intricacies of navigating in both forward and reverse directions. These insights are pivotal for refining the navigation algorithm, ultimately contributing to the development of more robust and user-friendly systems for individuals with mobility challenges. The data used for this purpose included actuator input, vehicle location, robot movement sensors, and sensor readings representing the world state. The study provides a strong foundation for future wheelchair navigation research by Dynamic Control. Consequently, we found that navigating the Turtlebot in the reverse direction resulted in a 5%-6% increase in diversion compared to forward navigation, providing valuable insight into further improvement of the navigation algorithm.

Keywords: Kalman filter; Navigation system; Route; SLAM; Smart wheelchair.