Design and Implementation of an Autonomous Mobile Robot for Obstacle Avoidance and Path Following

Design and Implementation of an Autonomous Mobile Robot for Obstacle Avoidance and Path Following | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Design and Implementation of an Autonomous Mobile Robot for Obstacle Avoidance and Path Following Amro Babiker Mansoor, Eltaher Mohamed Hussein² This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7856153/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper presents the design and implementation of an autonomous mobile robot developed to perform reliable obstacle avoidance and path-following tasks using low-cost sensors and components. The robot employs an Arduino Mega 2560 microcontroller integrated with infrared (IR) and ultrasonic sensors for real-time detection and navigation. A calibrated 73° ultrasonic orientation was adopted to enhance lateral coverage and minimize blind zones. The robot’s mobility is achieved using DC motors controlled through pulse-width modulation (PWM), while a GPS–GSM module provides telemetry for remote tracking. Experimental results confirmed obstacle detection within 12–13 cm and consistent path recovery under IR feedback. The proposed system demonstrates that robust autonomous navigation can be achieved, offering a flexible and reproducible platform for academic research and robotics education. This design also serves as an accessible reference for undergraduate robotics courses. Autonomous Mobile Robot Obstacle Avoidance Path Following Arduino Mega 2560 Infrared Sensor Ultrasonic Sensor GPS GSM PWM Control Figures Figure 1 Figure 2 Figure 3 I. INTRODUCTION Autonomous mobile robots (AMRs) integrate sensing, control, and computation into platforms capable of independent operation in structured indoor settings. The rapid evolution of embedded systems and microcontrollers has made it possible to build efficient robotic systems using low-cost, open-source technologies. Despite remarkable progress in industrial robotics, achieving reliable obstacle avoidance and accurate path following remains a challenging problem, especially when cost and computational efficiency are critical design factors. Several existing approaches rely on expensive sensors such as LiDAR, stereo cameras, or depth sensors, which, while accurate, limit accessibility for small-scale academic and educational projects. Conversely, the use of ultrasonic and infrared (IR) sensors provides a practical, low-cost alternative for short-range perception and environmental awareness. These sensors are widely available and easily integrated with microcontrollers like Arduino and Raspberry Pi. The present work focuses on the design and implementation of an autonomous mobile robot that performs obstacle detection and path tracking using ultrasonic and IR sensors. The system is based on an Arduino Mega 2560 controller and includes a GSM–GPS module for real-time tracking and remote notification. The robot’s 73° ultrasonic sensor alignment improves lateral sensing coverage and reduces blind zones. The proposed configuration enables consistent obstacle avoidance and smooth path recovery in indoor environments, making it suitable for academic research, prototyping, and robotics education. This paper is derived from the author’s M.Sc. research conducted in 2018. II. RELATED WORK Research on autonomous mobile robots (AMRs) has been an active area of study for more than two decades, with significant contributions focused on navigation, sensing, and control. Early robotic systems utilized high-cost sensors and complex computer vision algorithms to achieve autonomous motion in structured environments. Although these systems demonstrated impressive capabilities, their complexity and cost restricted their application to industrial and research laboratories only. Recent advances in microcontrollers, sensor technology, and embedded computing have enabled the development of compact and affordable robots suitable for educational and research purposes. Several studies have explored ultrasonic and infrared (IR) sensing for real-time obstacle detection. Hybrid approaches that combine ultrasonic and IR sensing have also been proposed, demonstrating that sensor fusion can enhance perception accuracy—especially in confined environments. Despite these efforts, most low-cost systems still face challenges related to blind zones, delayed reaction time, and limited adaptability to irregular surfaces. The work presented in this paper addresses these limitations by introducing a calibrated ultrasonic sensor orientation of 73°, which increases the detection field and reduces blind spots. Moreover, the proposed system integrates GPS–GSM telemetry for real-time position monitoring—an uncommon feature in low-cost academic robots. III. SYSTEM DESIGN AND METHODOLOGY The proposed autonomous mobile robot was designed as a modular, low-cost platform capable of performing obstacle avoidance and path-following tasks. The system consists of four major subsystems: sensing, processing, actuation, and communication. The sensing subsystem integrates an ultrasonic sensor for distance measurement and three infrared (IR) sensors for line detection and path tracking. The ultrasonic transducer operates within a calibrated orientation of 73°, chosen after experimental evaluation to improve lateral coverage and minimize the blind zone ahead of the robot. The processing subsystem is based on an Arduino Mega 2560 microcontroller that processes sensor data, executes the obstacle avoidance and path-following algorithms, and generates PWM signals to control the DC motors through an L298N motor driver. The communication subsystem employs a GPS–GSM module that enables SMS alerts containing geolocation whenever an obstacle is detected within the safety threshold. A. Hardware Components Hardware components include the Arduino Mega 2560 controller, ultrasonic sensor (HC-SR04), IR sensors for line tracking, L298N motor driver, two DC motors for differential drive, and a 12 V DC battery with appropriate voltage regulation. The ultrasonic sensor was mounted at a fixed 73° orientation to maximize the detection field. IR sensors were positioned at the front underside to detect the navigation line. All modules were assembled on an aluminum chassis for improved stability and balance. B. Control Algorithm The control logic follows a cyclic decision-making process. The robot continuously reads the ultrasonic sensor values to determine the distance to nearby obstacles. When the measured distance exceeds 13 cm, the robot proceeds along its path using IR feedback to maintain alignment. If the distance is less than or equal to 13 cm, the robot stops, logs the coordinates, sends an alert through the GSM module, and executes an avoidance sequence consisting of three steps: reverse motion, right turn at a fixed angle, and forward motion to rejoin the original path. loop: d ← ultrasonic_distance() if d ≤ 13 cm then stop() (lat, lon) ← read_GPS() send_SMS(lat, lon) reverse(t_rev) // short reverse turn_right(θ) // fixed angle move_forward(t_fw) // rejoin path else follow_line_with_IR() // maintain alignment end if end loop This simple rule-based logic ensures predictable responses without the need for computationally expensive algorithms such as SLAM or vision-based mapping. C. System Integration All modules were integrated using embedded C on the Arduino IDE. Sensor calibration was conducted under controlled indoor lighting conditions. Serial debugging was employed to validate data exchange between the sensors, controller, and communication modules before final deployment. The complete prototype demonstrated reliable interaction among sensing, actuation, and communication subsystems. IV. IMPLEMENTATION AND RESULTS A. Hardware Implementation The hardware implementation assembled all components on a lightweight aluminum chassis with two rear DC motors for propulsion and a front caster wheel for balance. PWM-based speed calibration maintained straight-line motion and reduced oscillation during corrections. The GSM and GPS modules were positioned on a separate layer to minimize interference from motor currents. The software implementation used the Arduino IDE in embedded C, with a main loop monitoring ultrasonic and IR sensor readings. Based on the inputs, the robot decided whether to move forward, stop, reverse, or turn. Communication routines transmitted SMS alerts with GPS coordinates upon obstacle detection, confirming correct operation of the telemetry subsystem. B. Experimental Setup Experimental evaluation was performed indoors on a flat surface with a predefined line path and controlled lighting. Obstacles of various shapes and materials were placed along the path to evaluate detection performance. The ultrasonic sensor consistently detected obstacles within a range of 12–13 cm—matching the designed safety threshold—while IR sensors maintained path alignment with minimal deviation (< 2 cm). The robot executed the avoidance routine by reversing, turning right, and rejoining the path. V. DISCUSSION The evaluation demonstrates that reliable obstacle avoidance and path-following behaviors can be achieved using low-cost sensing and control components. The calibrated 73° ultrasonic sensor orientation expanded the field of view and minimized blind spots compared to perpendicular arrangements, yielding smoother navigation in narrow or cluttered spaces. Compared to previously reported low-cost systems, the proposed platform achieved similar or better accuracy with reduced complexity. The rule-based algorithm provided sufficient real-time decision making without advanced processing hardware, making the system suitable for education and entry-level research. The GSM–GPS telemetry added a valuable tracking and alerting capability beyond basic line following. VI. CONCLUSION AND FUTURE WORK This paper presented a low-cost autonomous mobile robot capable of obstacle avoidance and path following using readily available sensors. The system integrated an Arduino Mega 2560, ultrasonic and IR sensors, and a GPS–GSM module for tracking, achieving consistent obstacle detection within 12–13 cm and consistent path recovery across repeated trials. The modular design and open-source control architecture make the system appropriate for academic research and robotics education. Future work includes vision-based perception, LiDAR integration, and adaptive control to improve performance in dynamic environments. Declarations Author Contribution A.B.M. (Amro Babiker Mansoor Hamad Alneel) designed and implemented the autonomous mobile robot system, performed experiments, analyzed the results, and wrote the manuscript.E.M.H. (Dr. Eltaher Mohamed Hussein) supervised the research, provided technical guidance, and reviewed and edited the manuscript.Both authors read and approved the final version of the manuscript. ACKNOWLEDGMENT The author expresses his sincere appreciation to Dr. Eltaher Mohamed Hussein for his invaluable supervision, continuous guidance, and insightful support throughout this research work. Data Availability All data supporting the findings of this study are available from the corresponding author upon reasonable request. References Connected Devices, ReadWrite, [Online]. Available: https://www.google.com/amp/readwrite.com/2016/10/09/autonomous-vehicles-in-factories-il4/amp/. Accessed: Dec. 15, 2016. D. Inventions, "Leonardo da Vinci’s self- propelled cart invention," [Online]. Available: http://www.da- vinci- inventions.com/self- propelled- cart.aspx. Accessed: Oct. 3, 2016. T. Vanderbilt, "Bacteria, methane, and other dangers within Siberia’s melting Permafrost," in Autopia, WIRED, [Online]. Available: https://www.wired.com/2012/02/autonomousvehicle- history/. Accessed: Oct. 1, 2016. C. Mendoza, "A brief history of autonomous vehicle technology," WIRED, [Online]. Available: https://www.wired.com/brandlab/2016/03/ a-brief-history-of-autonomous-vehicle-technology/. Accessed: Nov. 11, 2016. B. Chong, R. Yan, and A. Li, "ECE 4760 the autonomous driving car,".[Online]. Available:https://people.ece.cornell.edu/land/courses/ece4760/FinalProjects/s2011/ bcc44_acl84_my259/bcc44_acl84_my259/. Accessed: Oct. 5, 2016. S. Engineer and Maker, "The world’s first Android autonomous vehicle," [Online]. Available: https://platis.solutions/blog/2015/06/29/worlds- first- android- autonomous- vehicle/. Accessed: Nov. 8, 2016. "- 100 % autonomous and electric," Navya, [Online]. Available: http://navya.tech/?lang=en. Accessed: Oct. 21, 2016. E. Ackerman, "Google’s autonomous car takes to the streets," IEEE Spectrum: Technology, Engineering, and Science News, 2010. [Online]. Available:http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/googles- autonomous- cartakes- to- the- streets. Accessed: Oct. 8, 2016. Q. Chen, U. Ozguner, and K. Redmill, "Ohio state university DARPA grand challenge: Developing a completely autonomous vehicle," IEEE Intelligent Systems, vol. 19, no. 5, pp. 8–11, Sep. 2004. "PID Theory Explained- National Instruments", Ni.com, [Online]. Available: http://www.ni.com/white- paper/3782/en/. Accessed: 21- May- 2017. "PID Control and Derivative on Measurement – Control Guru", Controlguru.com, [Online]. Available: http://controlguru.com/pid- control- and- derivative- on- measurement/. Accessed: 21- May- 2017. Jihonyan ,"Machinery prognostics and prognosis oriented maintenance Management",(2015). Odd Jostein Svendsli ,"Atmel's self- programming flash microcontrollers",(2003). Hernando Barragan, "The Untold History of Arduino",(2016). Johnson,Bobbie,"GPS System 'close to breakdown' ",(2009). C. Mendoza, "A brief history of autonomous vehicle technology," WIRED, 2016. Md.Syedul Amin, JubayerJalil and M.B.Reaz. “Accident Detection and Reporting System using GPS, GPRS and GSM Technology” IEEE/OSA/IAPR International Conference on Informatics, Electronics & vision, (2012). Anton A. Huurdeman ,"The Worldwide History of Telecommunications",(2014). Locomotion modes of an hybrid wheel-legged robot G. Besseron, etc,all BidaudLaboratoire de Robotique de Paris (LRP)CNRS FRE 2507- Universit´e Pierre et Marie Curie, Paris 618 route du Panorama- BP61- 92265 Fontenay- aux- Roses,. Introduction to Robot , Vikram Kapila, Associate Professor, Mechanical Engineering. Robot Dynamics and Control, Second Edition, Mark W. Spong, Seth Hutchinson, and M. Vidyasagar. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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3","display":"","copyAsset":false,"role":"figure","size":123840,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRobot prototype and experimental path.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7856153/v1/8045d9efaa07034baa01f516.png"},{"id":97524160,"identity":"508a6b94-abb8-4c75-aa5f-ec55e0d87305","added_by":"auto","created_at":"2025-12-05 11:54:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":564607,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7856153/v1/3345f75c-c6b9-41ca-b4cc-0218c73918c0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Design and Implementation of an Autonomous Mobile Robot for Obstacle Avoidance and Path Following","fulltext":[{"header":"I. INTRODUCTION","content":"\u003cp\u003eAutonomous mobile robots (AMRs) integrate sensing, control, and computation into platforms capable of independent operation in structured indoor settings. The rapid evolution of embedded systems and microcontrollers has made it possible to build efficient robotic systems using low-cost, open-source technologies. Despite remarkable progress in industrial robotics, achieving reliable obstacle avoidance and accurate path following remains a challenging problem, especially when cost and computational efficiency are critical design factors. Several existing approaches rely on expensive sensors such as LiDAR, stereo cameras, or depth sensors, which, while accurate, limit accessibility for small-scale academic and educational projects. Conversely, the use of ultrasonic and infrared (IR) sensors provides a practical, low-cost alternative for short-range perception and environmental awareness. These sensors are widely available and easily integrated with microcontrollers like Arduino and Raspberry Pi. The present work focuses on the design and implementation of an autonomous mobile robot that performs obstacle detection and path tracking using ultrasonic and IR sensors. The system is based on an Arduino Mega 2560 controller and includes a GSM\u0026ndash;GPS module for real-time tracking and remote notification. The robot\u0026rsquo;s 73\u0026deg; ultrasonic sensor alignment improves lateral sensing coverage and reduces blind zones. The proposed configuration enables consistent obstacle avoidance and smooth path recovery in indoor environments, making it suitable for academic research, prototyping, and robotics education. This paper is derived from the author\u0026rsquo;s M.Sc. research conducted in 2018.\u003c/p\u003e"},{"header":"II. RELATED WORK","content":"\u003cp\u003eResearch on autonomous mobile robots (AMRs) has been an active area of study for more than two decades, with significant contributions focused on navigation, sensing, and control. Early robotic systems utilized high-cost sensors and complex computer vision algorithms to achieve autonomous motion in structured environments. Although these systems demonstrated impressive capabilities, their complexity and cost restricted their application to industrial and research laboratories only. Recent advances in microcontrollers, sensor technology, and embedded computing have enabled the development of compact and affordable robots suitable for educational and research purposes. Several studies have explored ultrasonic and infrared (IR) sensing for real-time obstacle detection. Hybrid approaches that combine ultrasonic and IR sensing have also been proposed, demonstrating that sensor fusion can enhance perception accuracy\u0026mdash;especially in confined environments. Despite these efforts, most low-cost systems still face challenges related to blind zones, delayed reaction time, and limited adaptability to irregular surfaces. The work presented in this paper addresses these limitations by introducing a calibrated ultrasonic sensor orientation of 73\u0026deg;, which increases the detection field and reduces blind spots. Moreover, the proposed system integrates GPS\u0026ndash;GSM telemetry for real-time position monitoring\u0026mdash;an uncommon feature in low-cost academic robots.\u003c/p\u003e"},{"header":"III. SYSTEM DESIGN AND METHODOLOGY","content":"\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe proposed autonomous mobile robot was designed as a modular, low-cost platform capable of performing obstacle avoidance and path-following tasks. The system consists of four major subsystems: sensing, processing, actuation, and communication. The sensing subsystem integrates an ultrasonic sensor for distance measurement and three infrared (IR) sensors for line detection and path tracking. The ultrasonic transducer operates within a calibrated orientation of 73\u0026deg;, chosen after experimental evaluation to improve lateral coverage and minimize the blind zone ahead of the robot. The processing subsystem is based on an Arduino Mega 2560 microcontroller that processes sensor data, executes the obstacle avoidance and path-following algorithms, and generates PWM signals to control the DC motors through an L298N motor driver. The communication subsystem employs a GPS\u0026ndash;GSM module that enables SMS alerts containing geolocation whenever an obstacle is detected within the safety threshold.\u003c/p\u003e\u003cp\u003e\u003cb\u003eA. Hardware Components\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHardware components include the Arduino Mega 2560 controller, ultrasonic sensor (HC-SR04), IR sensors for line tracking, L298N motor driver, two DC motors for differential drive, and a 12 V DC battery with appropriate voltage regulation. The ultrasonic sensor was mounted at a fixed 73\u0026deg; orientation to maximize the detection field. IR sensors were positioned at the front underside to detect the navigation line. All modules were assembled on an aluminum chassis for improved stability and balance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eB. Control Algorithm\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe control logic follows a cyclic decision-making process. The robot continuously reads the ultrasonic sensor values to determine the distance to nearby obstacles. When the measured distance exceeds 13 cm, the robot proceeds along its path using IR feedback to maintain alignment. If the distance is less than or equal to 13 cm, the robot stops, logs the coordinates, sends an alert through the GSM module, and executes an avoidance sequence consisting of three steps: reverse motion, right turn at a fixed angle, and forward motion to rejoin the original path.\u003c/p\u003e\u003cp\u003eloop:\u003c/p\u003e\u003cp\u003ed \u0026larr; ultrasonic_distance()\u003c/p\u003e\u003cp\u003eif d\u0026thinsp;\u0026le;\u0026thinsp;13 cm then\u003c/p\u003e\u003cp\u003estop()\u003c/p\u003e\u003cp\u003e(lat, lon) \u0026larr; read_GPS()\u003c/p\u003e\u003cp\u003esend_SMS(lat, lon)\u003c/p\u003e\u003cp\u003ereverse(t_rev) // short reverse\u003c/p\u003e\u003cp\u003eturn_right(θ) // fixed angle\u003c/p\u003e\u003cp\u003emove_forward(t_fw) // rejoin path\u003c/p\u003e\u003cp\u003eelse\u003c/p\u003e\u003cp\u003efollow_line_with_IR() // maintain alignment\u003c/p\u003e\u003cp\u003eend if\u003c/p\u003e\u003cp\u003eend loop\u003c/p\u003e\u003cp\u003eThis simple rule-based logic ensures predictable responses without the need for computationally expensive algorithms such as SLAM or vision-based mapping.\u003c/p\u003e\u003cp\u003e\u003cb\u003eC. System Integration\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAll modules were integrated using embedded C on the Arduino IDE. Sensor calibration was conducted under controlled indoor lighting conditions. Serial debugging was employed to validate data exchange between the sensors, controller, and communication modules before final deployment. The complete prototype demonstrated reliable interaction among sensing, actuation, and communication subsystems.\u003c/p\u003e"},{"header":"IV. IMPLEMENTATION AND RESULTS","content":"\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eA. Hardware Implementation\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe hardware implementation assembled all components on a lightweight aluminum chassis with two rear DC motors for propulsion and a front caster wheel for balance. PWM-based speed calibration maintained straight-line motion and reduced oscillation during corrections. The GSM and GPS modules were positioned on a separate layer to minimize interference from motor currents. The software implementation used the Arduino IDE in embedded C, with a main loop monitoring ultrasonic and IR sensor readings. Based on the inputs, the robot decided whether to move forward, stop, reverse, or turn. Communication routines transmitted SMS alerts with GPS coordinates upon obstacle detection, confirming correct operation of the telemetry subsystem.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. Experimental Setup\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExperimental evaluation was performed indoors on a flat surface with a predefined line path and controlled lighting. Obstacles of various shapes and materials were placed along the path to evaluate detection performance. The ultrasonic sensor consistently detected obstacles within a range of 12\u0026ndash;13 cm\u0026mdash;matching the designed safety threshold\u0026mdash;while IR sensors maintained path alignment with minimal deviation (\u0026lt;\u0026thinsp;2 cm). The robot executed the avoidance routine by reversing, turning right, and rejoining the path.\u003c/p\u003e"},{"header":"V. DISCUSSION","content":"\u003cp\u003eThe evaluation demonstrates that reliable obstacle avoidance and path-following behaviors can be achieved using low-cost sensing and control components. The calibrated 73\u0026deg; ultrasonic sensor orientation expanded the field of view and minimized blind spots compared to perpendicular arrangements, yielding smoother navigation in narrow or cluttered spaces. Compared to previously reported low-cost systems, the proposed platform achieved similar or better accuracy with reduced complexity. The rule-based algorithm provided sufficient real-time decision making without advanced processing hardware, making the system suitable for education and entry-level research. The GSM\u0026ndash;GPS telemetry added a valuable tracking and alerting capability beyond basic line following.\u003c/p\u003e"},{"header":"VI. CONCLUSION AND FUTURE WORK","content":"\u003cp\u003eThis paper presented a low-cost autonomous mobile robot capable of obstacle avoidance and path following using readily available sensors. The system integrated an Arduino Mega 2560, ultrasonic and IR sensors, and a GPS\u0026ndash;GSM module for tracking, achieving consistent obstacle detection within 12\u0026ndash;13 cm and consistent path recovery across repeated trials. The modular design and open-source control architecture make the system appropriate for academic research and robotics education. Future work includes vision-based perception, LiDAR integration, and adaptive control to improve performance in dynamic environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.B.M. (Amro Babiker Mansoor Hamad Alneel) designed and implemented the autonomous mobile robot system, performed experiments, analyzed the results, and wrote the manuscript.E.M.H. (Dr. Eltaher Mohamed Hussein) supervised the research, provided technical guidance, and reviewed and edited the manuscript.Both authors read and approved the final version of the manuscript.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author expresses his sincere appreciation to Dr. Eltaher Mohamed Hussein for his invaluable supervision, continuous guidance, and insightful support throughout this research work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eConnected Devices, ReadWrite, [Online]. Available: https://www.google.com/amp/readwrite.com/2016/10/09/autonomous-vehicles-in-factories-il4/amp/. Accessed: Dec. 15, 2016.\u003c/li\u003e\n\u003cli\u003eD. Inventions, \u0026quot;Leonardo da Vinci\u0026rsquo;s self- propelled cart invention,\u0026quot; [Online]. Available: http://www.da- vinci- inventions.com/self- propelled- cart.aspx. Accessed: Oct. 3, 2016.\u003c/li\u003e\n\u003cli\u003eT. Vanderbilt, \u0026quot;Bacteria, methane, and other dangers within Siberia\u0026rsquo;s melting Permafrost,\u0026quot; in Autopia, WIRED, [Online]. Available: https://www.wired.com/2012/02/autonomousvehicle- history/. Accessed: Oct. 1, 2016.\u003c/li\u003e\n\u003cli\u003eC. Mendoza, \u0026quot;A brief history of autonomous vehicle technology,\u0026quot; WIRED, [Online]. Available: https://www.wired.com/brandlab/2016/03/ a-brief-history-of-autonomous-vehicle-technology/. Accessed: Nov. 11, 2016.\u003c/li\u003e\n\u003cli\u003eB. Chong, R. Yan, and A. Li, \u0026quot;ECE 4760 the autonomous driving car,\u0026quot;.[Online]. Available:https://people.ece.cornell.edu/land/courses/ece4760/FinalProjects/s2011/\nbcc44_acl84_my259/bcc44_acl84_my259/. Accessed: Oct. 5, 2016.\u003c/li\u003e\n\u003cli\u003eS. Engineer and Maker, \u0026quot;The world\u0026rsquo;s first Android autonomous vehicle,\u0026quot; [Online]. Available: https://platis.solutions/blog/2015/06/29/worlds- first- android- autonomous- vehicle/. Accessed: Nov. 8, 2016.\u003c/li\u003e\n\u003cli\u003e\u0026quot;- 100 % autonomous and electric,\u0026quot; Navya, [Online]. Available: http://navya.tech/?lang=en. Accessed: Oct. 21, 2016.\u003c/li\u003e\n\u003cli\u003eE. Ackerman, \u0026quot;Google\u0026rsquo;s autonomous car takes to the streets,\u0026quot; IEEE Spectrum: Technology, Engineering, and Science News, 2010. [Online]. Available:http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/googles- autonomous- cartakes- to- the- streets. Accessed: Oct. 8, 2016.\u003c/li\u003e\n\u003cli\u003eQ. Chen, U. Ozguner, and K. Redmill, \u0026quot;Ohio state university DARPA grand challenge: Developing a completely autonomous vehicle,\u0026quot; IEEE Intelligent Systems, vol. 19, no. 5, pp. 8\u0026ndash;11, Sep. 2004.\u003c/li\u003e\n\u003cli\u003e\u0026quot;PID Theory Explained- National Instruments\u0026quot;, Ni.com, [Online]. Available: http://www.ni.com/white- paper/3782/en/. Accessed: 21- May- 2017.\u003c/li\u003e\n\u003cli\u003e\u0026quot;PID Control and Derivative on Measurement \u0026ndash; Control Guru\u0026quot;, Controlguru.com, [Online]. Available: http://controlguru.com/pid- control- and- derivative- on- measurement/. Accessed: 21- May- 2017.\u003c/li\u003e\n\u003cli\u003eJihonyan ,\u0026quot;Machinery prognostics and prognosis oriented maintenance Management\u0026quot;,(2015).\u003c/li\u003e\n\u003cli\u003eOdd Jostein Svendsli ,\u0026quot;Atmel\u0026apos;s self- programming flash microcontrollers\u0026quot;,(2003).\u003c/li\u003e\n\u003cli\u003eHernando Barragan, \u0026quot;The Untold History of Arduino\u0026quot;,(2016).\u003c/li\u003e\n\u003cli\u003eJohnson,Bobbie,\u0026quot;GPS System \u0026apos;close to breakdown\u0026apos; \u0026quot;,(2009).\u003c/li\u003e\n\u003cli\u003eC. Mendoza, \u0026quot;A brief history of autonomous vehicle technology,\u0026quot; WIRED, 2016.\u003c/li\u003e\n\u003cli\u003eMd.Syedul Amin, JubayerJalil and M.B.Reaz. \u0026ldquo;Accident Detection and Reporting System using GPS, GPRS and GSM Technology\u0026rdquo; IEEE/OSA/IAPR International Conference on Informatics, Electronics \u0026amp; vision, (2012).\u003c/li\u003e\n\u003cli\u003eAnton A. Huurdeman ,\u0026quot;The Worldwide History of Telecommunications\u0026quot;,(2014).\u003c/li\u003e\n\u003cli\u003eLocomotion modes of an hybrid wheel-legged robot G. Besseron, etc,all BidaudLaboratoire de Robotique de Paris (LRP)CNRS FRE 2507- Universit\u0026acute;e Pierre et Marie Curie, Paris 618 route du Panorama- BP61- 92265 Fontenay- aux- Roses,.\u003c/li\u003e\n\u003cli\u003eIntroduction to Robot , Vikram Kapila, Associate Professor, Mechanical Engineering.\u003c/li\u003e\n\u003cli\u003eRobot Dynamics and Control, Second Edition, Mark W. Spong, Seth Hutchinson, and M. Vidyasagar.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Autonomous Mobile Robot, Obstacle Avoidance, Path Following, Arduino Mega 2560, Infrared Sensor, Ultrasonic Sensor, GPS, GSM, PWM Control","lastPublishedDoi":"10.21203/rs.3.rs-7856153/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7856153/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper presents the design and implementation of an autonomous mobile robot developed to perform reliable obstacle avoidance and path-following tasks using low-cost sensors and components. The robot employs an Arduino Mega 2560 microcontroller integrated with infrared (IR) and ultrasonic sensors for real-time detection and navigation. A calibrated 73\u0026deg; ultrasonic orientation was adopted to enhance lateral coverage and minimize blind zones. The robot\u0026rsquo;s mobility is achieved using DC motors controlled through pulse-width modulation (PWM), while a GPS\u0026ndash;GSM module provides telemetry for remote tracking. Experimental results confirmed obstacle detection within 12\u0026ndash;13 cm and consistent path recovery under IR feedback. The proposed system demonstrates that robust autonomous navigation can be achieved, offering a flexible and reproducible platform for academic research and robotics education. This design also serves as an accessible reference for undergraduate robotics courses.\u003c/p\u003e","manuscriptTitle":"Design and Implementation of an Autonomous Mobile Robot for Obstacle Avoidance and Path Following","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 08:42:37","doi":"10.21203/rs.3.rs-7856153/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"49e6b55d-f956-4c7b-b8e1-1b51e938b3c0","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-05T11:53:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-15 08:42:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7856153","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7856153","identity":"rs-7856153","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}