Browse > Article
http://dx.doi.org/10.6109/jkiice.2021.25.3.377

Mapless Navigation Based on DQN Considering Moving Obstacles, and Training Time Reduction Algorithm  

Yoon, Beomjin (Electrical System Integrated Team, Renault Technology Korea)
Yoo, Seungryeol (School of Mechanical Engineering, Korea University of Technology and Education)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.3, 2021 , pp. 377-383 More about this Journal
Abstract
Recently, in accordance with the 4th industrial revolution, The use of autonomous mobile robots for flexible logistics transfer is increasing in factories, the warehouses and the service areas, etc. In large factories, many manual work is required to use Simultaneous Localization and Mapping(SLAM), so the need for the improved mobile robot autonomous driving is emerging. Accordingly, in this paper, an algorithm for mapless navigation that travels in an optimal path avoiding fixed or moving obstacles is proposed. For mapless navigation, the robot is trained to avoid fixed or moving obstacles through Deep Q Network (DQN) and accuracy 90% and 93% are obtained for two types of obstacle avoidance, respectively. In addition, DQN requires a lot of learning time to meet the required performance before use. To shorten this, the target size change algorithm is proposed and confirmed the reduced learning time and performance of obstacle avoidance through simulation.
Keywords
Reinforcement neural network; DQN; Mobile robot; Autonomous driving; Obstacle;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 A. Li, J. Wang, M. Xu, and Z. Chen, "DP-SLAM: A visual SLAM with moving probability towards dynamic environments," Information Science, vol. 556, pp. 128-142, 2021.   DOI
2 A. Rohacza, S. WeiBenfels, and S. Strassburger, "Concept for the comparison of intralogistics designs with real factory layout using augmented reality SLAM and marker-based tracking," in Procedia CIRP, vol. 93, pp. 341-346, 2020.   DOI
3 J. Kim, S. Jung, T. Jeon, and S. Kim, "SLAM based on feature map for autonomous vehicle," Journal of the Korea Institute of Information and Communication Engineering, vol. 13, no. 7, pp. 1437-1443, 2009.   DOI
4 A. Cheong, E. Foo, M. Lau, J. Chen, and H. Gan, "Development of a Robotics Waiter System for the food and beverage industry," in Proceedings of The Third International Conference on Advances in Mechanical and Robotics Engineering, pp. 21-25, 2015.
5 D. V. Lu, D. H. Hershberger, and W. D. Smart, "Layered costmaps for context-sensitive navigation," in IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS), pp. 709-715, 2014.
6 R. P. Monroe, S. A. Miller, and A. T. Bradley, "Behavioral mapless navigation using rings," in IEEE International Conference on Robotics and Automation, 2012.
7 G. Hinton, N. Srivastava, and K. Swersky, Lecture 6a overview of mini-batch gradient descent, [Internet]. pp. 29. Available: http://www.cs.toronto.edu/-tijmen/csc321/.
8 L. Tei, G. Paolo, and M. Liu, "Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation," in IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS), pp. 23-30, 2017.
9 K. J. Gil, ROS Machine Learning Package [Internet]. Available: https://github.com/ROBOTIS-GIT/emanual/blob/master/docs/en/platform/turtlebot3/machine_learning.md.
10 L. Qiang, D. Nanxum, L. Huican, and W. Heng, "A model-free mpless navigation method for mobile robot using reinforcement learning," in Chinese Control And Decision Conference(CCDC), pp. 3410-3415, 2018.