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

Authors:
raw_author_name: Ankit Kumar
raw_affiliation_string: Department of Information Technology, Guru Ghasidas Vishwavidyalaya, Bilaspur, Chhattisgarh, India.
raw_author_name: Kamred Udham Singh
raw_affiliation_string: School of Computing, Graphic Era Hill University, Dehradun, India.
raw_author_name: Pankaj Dadheech
raw_affiliation_string: Department of Computer Science & Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan (SKIT), Jaipur, Rajasthan, India.
raw_author_name: Aditi Sharma
raw_affiliation_string: Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International University, Pune, Maharashtra, India.
raw_author_name: Ahed Abugabah
raw_affiliation_string: College of Technological Innovation, Zayed University, Abu Dhabi Campus, United Arab Emirates.

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.