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Learning visual path–following skills for industrial robot using deep reinforcement learning

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Abstract

The visual path–following technology is widely used in cutting, laser welding, painting, gluing, and other fields, which is a crucial content of robotics studies. As an important algorithm of artificial intelligence (AI), reinforcement learning provides a new insight for robots to learn path-following skills which has the ability of machine vision and decision making. In order to build a robotic agent with path-following skills, this paper proposes a visual path–following algorithm based on artificial intelligence deep reinforcement learning double deep Q-network (DDQN). The proposed approach allows the robot to learn path-following skill by itself, using a visual sensor in the Robot Operating System (ROS) simulation environment. The robot can learn paths with different textures, colors, and shapes, which enhances the flexibility for different industrial robot application scenarios. Skills acquired in simulation can be directly translated to the real world. In order to verify the performance of the path-following skill, a path randomly hand-drawn on the workpiece is tested by the six-joint robot Universal Robots 5 (UR5). The simulation and real experiment results demonstrate that robots can efficiently and accurately perform path following autonomously using visual information without the parameters of the path and without programming the path in advance.

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Funding

This research is supported by the Key Laboratory Open Fund in Autonomous Region (2020520002) and the Key Research and Development Program in Autonomous Region (202003129).

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Authors and Affiliations

  1. School of Mechanical Engineering, Xinjiang University, Urumqi, 830017, China

    Guoliang Liu, Wenlei Sun, Wenxian Xie & Yangyang Xu

Authors
  1. Guoliang Liu
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  2. Wenlei Sun
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  3. Wenxian Xie
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  4. Yangyang Xu
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Contributions

Guoliang Liu: investigation, conceptualization, code, software, experiment, data curation, and writing of the manuscript. Wenlei Sun: funding acquisition, conceptualization, project administration. Wenxian Xie: software, code, methodology, experiment. Yangyang Xu: ROS platform, system, code, experiment, software.

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Correspondence to Wenlei Sun.

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Liu, G., Sun, W., Xie, W. et al. Learning visual path–following skills for industrial robot using deep reinforcement learning. Int J Adv Manuf Technol 122, 1099–1111 (2022). https://doi.org/10.1007/s00170-022-09800-1

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  • DOI: https://doi.org/10.1007/s00170-022-09800-1

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