Abstract
This paper describes the autonomous mobile search robot equipped with AI that currently being developed and the results obtained so far during this development process. We describe the theoretical concepts which are utilized as basis of robot system, algorithms used for motion controlling and data processing, implemented hardware and features of software implementation of the applied algorithms. The features of the developed robot are following. At first, it is employment of two lidars, the laser scanning data from which are combined into a single point cloud. Then we used a deep convolutional neural network (DCNN) for certain appropriate objects detection and recognition as well as Dlib tracker for such objects tracking after detection. Besides that, our robot can search for objects under the low light conditions because of usage of IMX219 camera from Sony with additional IR LED system. An NVIDIA Jetson Nano single-board computer was used as the main computational and control unit of the system as well as another board OrangePi PC was utilized for point clouds from two lidars processing. As for the methods for moving control we’ve implemented relatively computationally simple system based on Fuzzy Logic and Google Cartographer system using for SLAM. We have also applied A-star algorithm for better obstacles avoidance. Some functional schemes and additional description are provided in the article for illustration of building blocks of developed ROS based program system for robot location and mapping, moving control and object detection and recognition.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kumar, S.: Platooning Demonstrator with Deep Learning and Computer Vision (2020). https://expectopatronm.github.io/posts/projects/deep_learning_projects/platooning/. Accessed 01 May 2020
Joo, S.-H., Manzoor, S., Rocha, Y.G., Lee, H.U., Kuc, T.-Y.: A Realtime Autonomous Robot Navigation Framework for Human like High-level Interaction and Task Planning in Global Dynamic Environment. ArXiv abs/1905.12942 (2019): n. pag
Surmann, H., Jestel, C., Marchel, R., Musberg, F., Elhadj, H., Ardani, M.: Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments. ArXiv abs/2005.13857 (2020): n. pag
Babaeizadeh, M., Frosio, I., Tyree, S., Clemons, J., Kautz, J.: GA3C: gpu-based A3C for deep reinforcement learning. CoRR, abs/1611.06256 (2016). http://arxiv.org/abs/1611.06256
Chen, W., Sun, J., Li, W., Zhao, D.: A real-time multi-constraints obstacle avoidance method based on LiDAR. ArXiv, abs/2006.00142 (2020)
Chen, M., Wu, Y., He, H.: A Novel Navigation System for an Autonomous Mobile Robot in an Uncertain Environment. ArXiv, abs/2006.04962 (2020)
Wang, X.: Autonomous Mobile Robot Visual SLAM Based on Improved CNN Method (2018)
Burgard, W., Stachniss, C., Arras, K., Bennewitz, M.: SLAM: Simultaneous Localization and Mapping. Albert-Ludwigs-Universität Freiburg, Introduction to Mobile Robotics - SS (2019)
Durrant-Whyte, H., Bailey, T.: Simultaneous Localisation and Mapping (SLAM): Part I The Essential Algorithms. Robot. Autom. Mag. 13 (2006)
Hess, W., Kohler, D., Rapp, H., Andor, D.: Real-time loop closure in 2D LIDAR SLAM. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 1271–1278. IEEE (2016)
Fernández-Madrigal, J.-A., Blanco, J.L.: Simultaneous Localization and Mapping for Mobile Robots: Introduction and Methods (2012). https://doi.org/10.4018/978-1-4666-2104-6
Artificial Intelligence. Fuzzy Logic Systems. https://www.tutorialspoint.com/artificial_intelligence/artificial_intelligence_fuzzy_logic_systems.htm. Accessed 30 Apr 2020
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv 2017. arXiv preprint arXiv:1704.04861
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: SSD: single shot multibox detector. In: Proceedings of the European Conference on Computer Vision (ECCV) (2016)
Zhang, C.: How to run Keras model on Jetson Nano (2019). https://www.dlology.com/blog/how-to-run-keras-model-on-jetson-nano/. Accessed 01 May 2020
Acknowledgement
The article was prepared with financial support of the Russian Foundation for Basic Research and Volgograd Administration, Grant of the RFBR 19-47-340015 and this work was supported by the Ministry of Education and Science of Russia (the project “Development of Virtual 3D Reconstruction of Historical Objects Technique”, scientific theme code 2019-0920, project number in the research management system FZUU-0633-2020-0004).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Alexey, G., Klyachin, V., Eldar, K., Driaba, A. (2021). Autonomous Mobile Robot with AI Based on Jetson Nano. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Proceedings of the Future Technologies Conference (FTC) 2020, Volume 1. FTC 2020. Advances in Intelligent Systems and Computing, vol 1288. Springer, Cham. https://doi.org/10.1007/978-3-030-63128-4_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-63128-4_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63127-7
Online ISBN: 978-3-030-63128-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)