Abstract
Robotic and automation field is continuously in expansion. Robotic systems are now operating in unknown and dynamic environments. Therefore, they must not only classify sensory pattern but also determine the decision and action to be made. The well making decision of robot will depend on its efficiency when processing raw sensor data. In this work, we propose an innovative approach for robot intelligent perception and decision making process. We investigate the ability of deep learning methods to be brought to bear on robotic system decision making and control. Our challenging researches consist on providing robots the ability to autonomously recognize obstacle without a pre-programming need. For this purpose, we design a deep learning based framework to compute a high-quality convolutional Neural Network (CNN) model for image classification. The designed approach is labeled Enhanced Elite CNN Propagation Method. Simulations demonstrate the effectiveness of robot decision making when exploring its environment based on our approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Tai, L., Li, S., Liu, M.: A deep-network solution towards model-less obstacle avoidance. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2759–2764 (2016)
Sun, D., Kleiner, A., Nebel, B.: Behavior-based multi-robot collision avoidance. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 1668–1673 (2104)
Tai, L., Liu, M.: Deep-learning in mobile robotics—from perception to control systems: A survey on why and why not. abs/1612.07139 (2016)
Shao, J., Loy, C.: Deeply learned attributes for crowded scene understanding. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4657–4666 (2015)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)
Levine, S., Pastor P., Krizhevsky, A., Quillen, D.:Learning hand-eye coordination for robotic grasping with large-scale data collection. In: International Symposium on Experimental Robotics, pp. 173–184. Springer, Berlin (2016)
Yang, Y., Fermuller, C., Li, Y., Aloimonos, Y.: Grasp type revisited: A modern perspective on a classical feature for vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 400–408 (2015)
Duan, Y., Chen, X., Houthooft, R., Schulman, J., Abbeel, P.: Benchmarking deep reinforcement learning for continuous control. In: International Conference on Machine Learning, pp. 1329–1338 (2016)
Gongal, A., Amatya, S., Karkee, M.: Sensors and systems for fruit detection and localization: A review. Comput. Electron. Agric. 116, 8–19 (2015)
Lenz, I., Lee, H., et Saxena, A.: Deep learning for detectingrobotic grasps. Int. J. Robot. Res. 34(4–5), 705–724 (2015)
Ghahramani, Z.: Probabilistic machine learning and artificial intelligence. Nature 521(7553), 452 (2015)
Giusti, A., Guzzi, J.: A machine learning approach to visual perception of forest trails for mobile robots. IEEE Robot. Autom. Lett. 1(2), 661–667 (2016)
Veeriah, V., Zhuang, N., Qi, G.-J.: Differential recurrent neural networks for action recognition. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 4041–4049. IEEE (2015)
Brighton, H., Selina, H.: Introducing Artificial Intelligence: A Graphic Guide, ser. Introducing…Icon Books Limited (2015)
Loussaief, S., Abdelkrim, A.: Deep learning vs. bag of features in machine learning for image classification. In: International Conference on Advanced Systems and Electrical Technologies, IC’ASET (2018)
Loussaief, S., Abdelkrim, A.: Convolutional neural network hyper-parameters optimization based on genetic algorithms. (IJACSA). Int. J. Adv. Comput. Sci. Appl. (2018). https://doi.org/10.14569/ijacsa.2018.091031
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Loussaief, S., Abdelkrim, A. (2019). Robot Intelligent Perception Based on Deep Learning. In: Benavente-Peces, C., Slama, S., Zafar, B. (eds) Proceedings of the 1st International Conference on Smart Innovation, Ergonomics and Applied Human Factors (SEAHF). SEAHF 2019. Smart Innovation, Systems and Technologies, vol 150. Springer, Cham. https://doi.org/10.1007/978-3-030-22964-1_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-22964-1_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-22963-4
Online ISBN: 978-3-030-22964-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)