Autonomous Mobile Robot Motion Control for Hospital Disinfection


  • Qassim Haichel Researcher, Baghdad University, Al- Khwarizmi College of Engineering, Iraq
  • Ahmed Rahman Assistant Professor. Ahmed Rahman, Baghdad University, Al- Khwarizmi College of Engineering, Iraq


mobile robot, Probabilistic Roadmap (PRM), pure pursuit, Kinematic Model


As our society develops, robotic systems become indispensable. In producing automation can be used for scanning the impure areas in hospital.  In this article MATLAB's "Robotics System Toolbox," to simulate robot navigation. This essay attempts to demonstrate the efficacy of two path planning techniques: pure-pursuit and the probabilistic roadmap (PRM). To compare the performances of four maps, whose difficulty was gradually increased. For PRM, the number of nodes was first set after the map had been loaded. Initial and final positions were then established. Following that, the program built a possible network of links between the nodes at the start and goal locations. Finally, the algorithm analyzed this network of connected nodes to return a collision-free path. In pure-pursuit, the algorithm's main goal is to select a suitable look-ahead distance. In its most basic form, The Pure Pursuit algorithm examines the difference in heading between the current vehicle and the objective point along the course. It is a proportional controller. The effectiveness of the Pure Pursuit algorithm implementations was tested in a variety of situations. The algorithm used in all of the tests followed a straight path between high level waypoints. It's necessary to keep in mind that PRM path position was the only navigation sensor employed in these studies when analyzing their results.


M. Mucientes, R. Iglesias, C. v Regueiro, A. Bugarã, and S. Barro, “A fuzzy temporal rule-based velocity controller for mobile robotics,” 2003. [Online]. Available:

F. Diaz-Hermida, M. Pereira-Fariña, J. C. Vidal, and A. Ramos-Soto, “Characterizing Quantifier Fuzzification Mechanisms: a behavioral guide for practical applications,” May 2016, doi: 10.1016/j.fss.2017.07.017.

H. Khayyam, B. Javadi, M. Jalili, and R. N. Jazar, “Artificial Intelligence and Internet of Things for Autonomous Vehicles,” in Nonlinear Approaches in Engineering Applications, Springer International Publishing, 2020, pp. 39–68. doi: 10.1007/978-3-030-18963-1_2.

B. Innocenti, B. López, and J. Salvi, “A multi-agent architecture with cooperative fuzzy control for a mobile robot,” Rob Auton Syst, vol. 55, no. 12, pp. 881–891, Dec. 2007, doi: 10.1016/j.robot.2007.07.007.

“Pioneer 3 Operations Manual with MobileRobots Exclusive Advanced Robot Control & Operations Software,” 2006.

A. Szaka?l, IEEE Hungary Section, M. IEEE Systems, and Institute of Electrical and Electronics Engineers, CINTI 2016?: 17th IEEE International Symposium on Computational Intelligence and Informatics?: proceedings?: 2016 November 17-19, Budapest.

2017 IEEE National Aerospace and Electronics Conference (NAECON)?: 27-30 June 2017.

M. Samuel et al., “A Review of some Pure-Pursuit based Path Tracking Techniques for Control of Autonomous Vehicle,” 2016.

“matlab.” (accessed Aug. 16, 2022).

X. Z. Han et al., “Path-tracking simulation and field tests for an auto-guidance tillage tractor for a paddy field,” Comput Electron Agric, vol. 112, pp. 161–171, Mar. 2015, doi: 10.1016/j.compag.2014.12.025.

T. Elmokadem and A. v. Savkin, “A hybrid approach for autonomous collision-free uav navigation in 3d partially unknown dynamic environments,” Drones, vol. 5, no. 3, Sep. 2021, doi: 10.3390/drones5030057.

H. Y. Zhang, W. M. Lin, and A. X. Chen, “Path planning for the mobile robot: A review,” Symmetry (Basel), vol. 10, no. 10, 2018, doi: 10.3390/sym10100450.

A. A. Ravankar, A. Ravankar, T. Emaru, and Y. Kobayashi, “HPPRM: Hybrid Potential Based Probabilistic Roadmap Algorithm for Improved Dynamic Path Planning of Mobile Robots,” IEEE Access, vol. 8, pp. 221743–221766, 2020, doi: 10.1109/ACCESS.2020.3043333.

IEEE Staff, 2017 IEEE 10th International Workshop on Computational Intelligence and Applications (IWCIA). IEEE, 2017.

Institute of Electrical and Electronics Engineers, 2016 International Conference on Next Generation Intelligent Systems (ICNGIS).

H. Wang, X. Chen, Y. Chen, B. Li, and Z. Miao, “Trajectory Tracking and Speed Control of Cleaning Vehicle Based on Improved Pure Pursuit Algorithm*.”

J. Giesbrecht, D. Mackay, J. Collier, S. Verret, and D. Suffield, “Path Tracking for Unmanned Ground Vehicle Navigation Implementation and Adaptation of the Pure Pursuit Algorithm Defence Research and Recherche et développement Development Canada pour la défense Canada,” 2005.

J. Morales, J. L. Martínez, M. A. Martínez, and A. Mandow, “Pure-pursuit reactive path tracking for nonholonomic mobile robots with a 2D laser scanner,” EURASIP J Adv Signal Process, vol. 2009, 2009, doi: 10.1155/2009/935237.




How to Cite

Qassim Haichel, & Ahmed Rahman. (2022). Autonomous Mobile Robot Motion Control for Hospital Disinfection. American Scientific Research Journal for Engineering, Technology, and Sciences, 90(1), 1–12. Retrieved from