期刊
2020 20TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS)
卷 -, 期 -, 页码 1231-1236出版社
IEEE
DOI: 10.23919/iccas50221.2020.9268370
关键词
Deep-Q Network; Laser Map; ROS; Gazebo Simulation; Path Exploration
类别
资金
- Korean Ministry of Trade, Industry and Energy (MOTIE) by Korea Institute for Advancement of Technology (KIAT) [N0002428]
- Development Program through the National Research Foundation of Korea (NRF) - Korea government (MSIT) [2019R1F1A1049711]
- National Research Foundation of Korea (NRF)
- Korea Institute of Machinery & Materials (KIMM)
- Korea government (MSIT) [NRF-2020M3C1C1A02084772]
- National Research Foundation of Korea [2019R1F1A1049711] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Nowadays, Deep reinforcement learning has become the front runner to solve problems in the field of robot navigation and avoidance. This paper presents a LiDAR-equipped RC car trained in the GAZEBO environment using the deep reinforcement learning method. This paper uses reshaped LiDAR data as the data input of the neural architecture of the training network. This paper also presents a unique way to convert the LiDAR data into a 2D grid map for the input of training neural architecture. It also presents the test result from the training network in different GAZEBO environment. It also shows the development of hardware and software systems of embedded RC car. The hardware system includes-Jetson AGX Xavier, teensyduino and Hokuyo LiDAR; the software system includes- ROS and Arduino C. Finally, this paper presents the test result in the real world using the model generated from training simulation.
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