Abstract
Obstacle avoidance is considered as one of the main features of autonomous intelligent systems. There are various methods for obstacle avoidance. In this paper, obstacle avoidance is achieved by the difference between left wheel velocity and right wheel velocity of differential drive robot. The magnitude of difference between the wheel velocities is used to steer the robot in the correct direction. Data is collected by driving the robot manually. Ultrasonic sensors are used for distance measurement and IR sensors are used to collect the data of wheel velocities. This data is used to build a linear machine learning model which uses sonar data as input features. The model is used to predict the wheel velocities of the differential drive robot. The model built is then programmed into Atmega328 microcontroller using Arduino IDE. This enables the mobile robot to steer itself to avoid the obstacles. Since all the components used for this robot are highly available and cost-effective, the robot is economically affordable.
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References
Peng, Yan, Dong Qu, Yuxuan Zhong, Shaorong Xie, Jun Luo, and Jason Gu. “The obstacle detection and obstacle avoidance algorithm based on 2-d lidar.” In 2015 IEEE International Conference on Information and Automation, pp. 1648–1653. IEEE, 2015
Tsai, Cheng-Pei, Chin-Tun Chuang, Ming-Chih Lu, Wei-Yen Wang, Shun-Feng Su, and Shyang-Lih Chang. “Machine-vision based obstacle avoidance system for robot system.” In 2013 International Conference on System Science and Engineering (ICSSE), pp. 273–277. IEEE, 2013
Jin, Yun, Shengquan Li, Juan Li, Hongbing Sun, and Yuanwang Wu. “Design of an Intelligent Active Obstacle Avoidance Car Based on Rotating Ultrasonic Sensors.” In 2018 IEEE 8th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), pp. 753–757. IEEE, 2018
Wu, Ter Feng, Pu Sheng Tsai, Nien Tsu Hu, and Jen Yang Chen. “Use of ultrasonic sensors to enable wheeled mobile robots to avoid obstacles.” In 2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 958–961. IEEE, 2014
Bokade, Ashish U., and V. R. Ratnaparkhe. “Video surveillance robot control using smartphone and Raspberry pi.” In 2016 International Conference on Communication and Signal Processing (ICCSP), pp. 2094–2097. IEEE, 2016
Singh, Diksha, Pooja Zaware, and Anil Nandgaonkar. “Wi-Fi surveillance bot with real time audio & video streaming through Android mobile.” In 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), pp. 746–750. IEEE, 2017
Kadiam, Vineela, and G. Pavani. “Smart Phone Controlled Two Axes Robot for Video Surveillance Using Wireless Internet & Raspberry Pi Processor.” International Journal of Research in Advent Technology 2, no. 10 (2014): 97–100
Zhang, Lun, Stan Z. Li, Xiaotong Yuan, and Shiming Xiang. “Real-time object classification in video surveillance based on appearance learning.” In 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE, 2007
Tai L, Li S, Liu M (2016) A deep-network solution towards model-less obstacle avoidance. In: 2016 IEEE/RSJ International conference on intelligent robots and systems (IROS), pp 2759–2764. IEEE
Yang S, Konam S, Ma C, Rosenthal S, Veloso M, Scherer S (2017) Obstacle avoidance through deep networks based intermediate perception. arXiv preprint. arXiv:1704.08759
Aguilar WG, Casaliglla VP, Pólit JL (2017) Obstacle avoidance for low-cost UAVs. In: 2017 IEEE 11th international conference on semantic computing (ICSC), pp 503–508. IEEE
Duguleana M, Mogan G (2016) Neural networks based reinforcement learning for mobile robots obstacle avoidance. Expert Syst Appl 62:104–115
Chakravarty P, Kelchtermans K, Roussel T, Wellens S, Tuytelaars T, Van Eycken L (2017) CNN-based single image obstacle avoidance on a quadrotor. In: 2017 IEEE international conference on robotics and automation (ICRA), pp 6369–6374. IEEE
Aguilar WG, Casaliglla VP, Pólit JL, Abad V, Ruiz H (2017) Obstacle avoidance for flight safety on unmanned aerial vehicles. International work-conference on artificial neural networks, pp 575–584. Springer
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Naveen, V., Aasish, C., Kavya, M., Vidhyalakshmi, M., Sailaja, K. (2021). Autonomous Obstacle Avoidance Robot Using Regression. In: Chaki, N., Pejas, J., Devarakonda, N., Rao Kovvur, R.M. (eds) Proceedings of International Conference on Computational Intelligence and Data Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 56. Springer, Singapore. https://doi.org/10.1007/978-981-15-8767-2_1
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DOI: https://doi.org/10.1007/978-981-15-8767-2_1
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