The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
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Development of Path Tracking Algorithm and Variable Look Ahead Distance Algorithm to Improve the Path-Following Performance of Autonomous Tracked Platform for Agriculture

농업용 무한궤도형 자율주행 플랫폼의 경로 추종 및 추종 성능 향상을 위한 가변형 전방 주시거리 알고리즘 개발

  • Lee, Kyuho (Autonomous Vehicle.Intelligent Robotics, Hongik University) ;
  • Kim, Bongsang (Autonomous Vehicle.Intelligent Robotics, Hongik University) ;
  • Choi, Hyohyuk (Autonomous Vehicle.Intelligent Robotics, Hongik University) ;
  • Moon, Heechang (Mechanical & System Design Engineering, Hongik University)
  • Received : 2022.03.10
  • Accepted : 2022.04.19
  • Published : 2022.05.31
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Abstract

With the advent of the 4th industrial revolution, autonomous driving technology is being commercialized in various industries. However, research on autonomous driving so far has focused on platforms with wheel-type platform. Research on a tracked platform is at a relatively inadequate step. Since the tracked platform has a different driving and steering method from the wheel-type platform, the existing research cannot be applied as it is. Therefore, a path-tracking algorithm suitable for a tracked platform is required. In this paper, we studied a path-tracking algorithm for a tracked platform based on a GPS sensor. The existing Pure Pursuit algorithm was applied in consideration of the characteristics of the tracked platform. And to compensate for "Cutting Corner", which is a disadvantage of the existing Pure Pursuit algorithm, an algorithm that changes the LAD according to the curvature of the path was developed. In the existing pure pursuit algorithm that used a tracked platform to drive a path including a right-angle turn, the RMS path error in the straight section was 0.1034 m and the RMS error in the turning section was measured to be 0.2787 m. On the other hand, in the variable LAD algorithm, the RMS path error in the straight section was 0.0987 m, and the RMS path error in the turning section was measured to be 0.1396 m. In the turning section, the RMS path error was reduced by 48.8971%. The validity of the algorithm was verified by measuring the path error by tracking the path using a tracked robot platform.

Keywords

Acknowledgement

This research was funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA), 'Development of eco-friendly small and medium-sized weeding robots for agricultural assistance in fields such as soy (Project number : 321061-2)' and is currently supported by the publication grant

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