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Obstacle Avoidance Algorithm for Mobile Robot Based on ROS and Machine Vision

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Neural Computing for Advanced Applications (NCAA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1449))

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Abstract

With the robot gradually stepping into our daily life, more and more attention has been paid to the mobile ability of robot, and obstacle avoidance is a key problem. In this paper, the indoor environment is taken as the application scene, and the visual detection and local dynamic obstacle avoidance are studied respectively. Aiming at the shortcomings of the single external sensor lidar’s incomplete perception of obstacle information, this paper proposes an obstacle avoidance method based on machine vision for mobile obstacle detection under the ROS operating system. The method is improved based on YOLO-v4 in terms of vision, which can meet the real-time requirements of mobile terminal. Combined with dynamic window approach (DWA), the local obstacle avoidance algorithm is improved. In the process of local obstacle avoidance, visual detection information is integrated to increase the ability of local dynamic obstacle avoidance and improve the performance of robot local obstacle avoidance. Finally, the feasibility and validity of the algorithm are verified in the actual environment.

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Acknowledgements

This work was supported by Research and development and application demonstration of key technologies for intelligent manufacturing of robot digital workshop based on the integration of industrial Internet of Things and information physics. Project No. 2017YFE0123000.

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Authors and Affiliations

  1. Chongqing University of Posts and Telecommunications, Chongqing, China

    Yong Li & Yuzhe Liu

  2. Key Laboratory of Industrial Internet of Things and Network Control, Ministry of Education, Chongqing, China

    Yong Li & Yuzhe Liu

Authors
  1. Yong Li
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  2. Yuzhe Liu
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Corresponding author

Correspondence to Yong Li .

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Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Haijun Zhang

  2. Nanfang College of Sun Yat-sen University, Guangzhou, China

    Zhi Yang

  3. Hefei University of Technology, Hefei, China

    Zhao Zhang

  4. Chongqing University, Chongqing, China

    Zhou Wu

  5. South China Normal University, Guangzhou, China

    Tianyong Hao

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Li, Y., Liu, Y. (2021). Obstacle Avoidance Algorithm for Mobile Robot Based on ROS and Machine Vision. In: Zhang, H., Yang, Z., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2021. Communications in Computer and Information Science, vol 1449. Springer, Singapore. https://doi.org/10.1007/978-981-16-5188-5_44

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  • DOI: https://doi.org/10.1007/978-981-16-5188-5_44

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-5187-8

  • Online ISBN: 978-981-16-5188-5

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