Design and Study of an AI-Powered Autonomous Stair Climbing Robot

Yıl 2023, Cilt: 10 Sayı: 3, 571 - 585, 30.09.2023

Montaser Ramadan Shadi M S Hilles Mohammad Alkhedher

https://doi.org/10.31202/ecjse.1272769

Öz

Mobile robots are frequently utilized in the surveillance sector for both industrial and military purposes. The ability to navigate stairs is crucial for carrying out surveillance jobs like urban search and rescue operations. The research paper shows that the design methodology for a six-wheeled rover robot that can adapt to various stairs and maintain its stability based on the robot's specifications, kinematics restrictions, the maximum height, and the lowest step length needed to climb up and down the stairs is proposed. Based on a Raspberry Pi, camera, and LIDAR distance sensor, the suggested robot has the capacity to measure the stair height before starting to climb. A Convolutional Neural Networks (CNN) deep learning model is developed for the purpose of stair recognition. Additionally, stair alignment was estimated using statistical filtering on pictures and LIDAR distance reading. The robot can then decide whether it can climb the stairs or not based on its kinematics limitations and the height of the stairs as measured by our system. Result shows that our stair detection algorithm achieved an accuracy of 99.46% and a mean average precision of 99.64%. The proposed AI-Powered Robot-based stair recognition system, according to final results, effectively climbed stairs with a height range between 13 and 23 cm.

Anahtar Kelimeler

Raspberry pi, LIDAR, CNN, Robot, AI

Kaynakça

• [1]. D. Jennings and M. Figliozzi, “Study of Sidewalk Autonomous Delivery Robots and Their Potential Impacts on Freight Efficiency and Travel,” Transportation Research Record, vol. 2673, no. 6, pp. 317-326, 2019.
• [2]. T. Hoffmann, G. Prause, “On the Regulatory Framework for Last-Mile Delivery Robots,” Machines, vol. 6, no.3, pp.33. 2018.
• [3]. U. Patil et al., “Deep Learning Based Stair Detection and Statistical Image Filtering for Autonomous Stair Climbing,” 2019 Third IEEE International Conference on Robotic Computing (IRC), Naples, Italy, 2019, pp. 159-166, doi: 10.1109/IRC.2019.00031.
• [4]. PT. T Nguyen., S. W. Yan, J. F. Liao, C. H. Kuo “Autonomous Mobile Robot Navigation in Sparse LiDAR Feature Environments,” Applied Sciences, vol. 11 no. 13, pp. 5963, 2021. https://doi.org/10.3390/app11135963
• [5]. A. Li, J. Cao, S. Li, Z. Huang, J. Wang, G. Liu, “Map Construction and Path Planning Method for a Mobile Robot Based on Multi-Sensor Information Fusion,” Applied Sciences, vol. 12 no. 6, pp. 2931, 2022.
• [6]. S. S. Samsani and M. S. Muhammad, “Socially Compliant Robot Navigation in Crowded Environment by Human Behavior Resemblance Using Deep Reinforcement Learning,” in IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 5223-5230, July 2021, doi: 10.1109/LRA.2021.3071954.
• [7]. S. V. Sreenivasan, and B. H. Wilcox, “Stability and traction control of an actively actuated micro-rover,” Journal of Robotic Systems, vol. 11, pp. 487-502, 1994. https://doi.org/10.1002/rob.4620110604
• [8]. K. Nagatani, A. Ikeda, K. Sato and K. Yoshida, “Accurate estimation of drawbar pull of wheeled mobile robots traversing sandy terrain using built-in force sensor array wheel,” 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, MO, USA, 2009, pp. 2373-2378, doi: 10.1109/IROS.2009.5354566.
• [9]. M. Frigola, A. Casals and J. Amat, “Human-Robot Interaction Based on a Sensitive Bumper Skin,” 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China, 2006, pp. 283-287, doi: 10.1109/IROS.2006.282139.
• [10]. H. Xu, X.Liu, H. Fu, et al, “Visual Contact Angle Estimation and Traction Control for Mobile Robot in Rough-Terrain,” Journal of Intelligent & Robotic Systems, vol. 74, pp. 985-997, 2014.
• [11]. N. Pico, Hr. Jung, J. Medrano, “Climbing control of autonomous mobile robot with estimation of wheel slip and wheel-ground contact angle,” Journal of Mechanical Science and Technology vol.36, pp.959-968, 2022.
• [12]. H. Cevallos, G. Intriago and D. Plaza, “Performance of the Estimators Weighted Least Square, Extended Kalman Filter, and the Particle Filter in the Dynamic Estimation of State Variables of Electrical Power Systems,” 2018 IEEE International Conference on Automation/XXIII Congress of the Chilean Association of Automatic Control (ICA-ACCA), Concepcion, Chile, 2018, pp. 1-6, doi: 10.1109/ICA-ACCA.2018.8609729.
• [13]. Dong Hun Shin and Kyung Hoon Park, “Velocity kinematic modeling for wheeled mobile robots,” Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164), Seoul, Korea (South), 2001, vol. 4, pp. 3516-3522, doi: 10.1109/ROBOT.2001.933162.
• [14]. T. Thueer, A. Krebs and R. Siegwart, "Comprehensive Locomotion Performance Evaluation of All-Terrain Robots," 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China, 2006, pp. 4260-4265, doi: 10.1109/IROS.2006.281954.
• [15]. S. Seok et al., “Design Principles for Energy-Efficient Legged Locomotion and Implementation on the MIT Cheetah Robot,” in IEEE/ASME Transactions on Mechatronics, vol. 20, no. 3, pp. 1117-1129, June 2015, doi: 10.1109/TMECH.2014.2339013.
• [16]. M. Ramadan, M. Al-Khedher, S. Al-Kheder, “Vehicle Tracking Using An Efficient And Low Cost Embedded System With GPS and GSM”, Proceedings of the 4th International Conference on Advanced Computer Theory and Engineering (ICACTE 2011), Dec. 28-30, 2011, Dubai, UAE. [17]. Montaser N. A. Ramadan, Shadi M S Hilles, Mohammad Alkhedher, and Mohammed Ghazal, “Real-time Automated License Plate Recognition and Tracking of Runaway Vehicles,” International Informatics and Software Engineering Conference, Ankara, Turkey, IEEE Xplore, 2022, pp. 1-5
• [18]. M. Ramadan, M. Al-Khedher, S. Al-Kheder, “Intelligent Anti-Theft and Tracking system for Automobiles”, International Journal of Machine Learning and Computing, vol. 2, no. 1, pp. 83-88, 2012.
• [19]. M. Ramadan, M. Al-Khedher, “Wheelchair Control Using 3 DOF Head Gesture Recognition”, International Journal of Mechanical and Production Engineering, vol. 3(4), 2015, pp: 42-47.
• [20]. A. Bisher, M. Al-Khedher, “Intelligent mapping and locomotion system for blind guidance robot”, Proceeding of the 5th International Symposium on Mechatronics and its Applications (ISM08), May 27-29, 2008, Amman, Jordan.
• [21]. M. Al-khedher, “Integrated Robotic System for Indoor Spatial Perception and Navigation for the Blind”. International Review on Modelling and Simulations, vol. 5, no. 2(B), 2012.
• [22]. M. Al Khawaldah, M. Al-Khedher, I. Al-Adwan, A. Al Rawashdeh, “An Autonomous Exploration Strategy for Cooperative Mobile Robots”, Journal of Software Engineering and Applications, vol. 7, no. 3, pp. 142-149, 2014.
• [23]. M. Ghazal, T. Basmaji, M. Yaghi, M. Alkhedher, M. Mahmoud, A. Elbaz, “Cloud-based Monitoring of Thermal Anomalies in Industrial Environments using AI and the Internet of Robotic Things,” Sensors, vol. 20, no. 21, pp. 6348, 2020.
• [24]. M Ghazal, M Yaghi, A Gad, G Bary, M Alhalabi, M Alkhedher, “AI-Powered Service Robotics for Independent Shopping Experiences by Elderly and Disabled People,” Applied Sciences, vol. 11, no. 19, pp.9007, 2021.