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Analysis and Evaluation of Path Planning Algorithms for Autonomous Driving of Electromagnetically Actuated Microrobot

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Abstract

In order to overcome the limitations of the A* algorithm in the autonomous control of electromagnetically actuated microrobots, this study introduces three modified path planning algorithms (A*-WAPP, A*-waypoints, A*-WAPP-waypoints) using the concept of Wall Avoiding Path Planning (WAPP) and waypoints. Through the autonomous driving experiment of an electromagnetically actuated microrobot, the three modified path planning algorithms based on A* and the original A* algorithm were evaluated using four performance measures. As a result, it was confirmed whether significant changes exist between the A* algorithm and the A*-based modified algorithms about the fitness for the autonomous driving environment of the electromagnetically actuated microrobot. First, compared to the path of the A* algorithm, A*-WAPP algorithm generated a stable path that dramatically reduced the collision between the microrobot and the obstacle. However, in the autonomous driving of the microrobot, A*-WAPP algorithm increased the driving distance and driving time. On the other hand, A*-waypoints algorithm showed a tendency in reducing the driving distance and driving time of the autonomous driving microrobot by simplifying the generated path, but still showed the collision problem between the microrobot and the obstacle. Finally, the path generated by the A*-WAPP-waypoints algorithm greatly increased the stability of the autonomous driving microrobot and showed great advantages of the decreases in the driving distance and driving time. In conclusion, it was confirmed that the proposed A*-WAPP-waypoints algorithm showed the best path generation results in the autonomous driving microrobot among the three A*-based algorithms.

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Author information

Author notes

  1. Seung-hyun Lim, Sun Woo Sohn, and Hyoryong Lee contributed equally to this work.

Authors and Affiliations

  1. School of Undergraduate Studies Daegu Gyeongbuk Institute of Science & Technology (DGIST), Daegu, 42988, Korea

    Seung-hyun Lim, Sun Woo Sohn, Donghyeon Choi, Eunsil Jang, Minhye Kim & Junhyeong Lee

  2. Department of Robotics Engineering, Daegu Gyeongbuk Institute of Science & Technology (DGIST), Daegu, 42988, Korea

    Hyoryong Lee & Sukho Park

Authors
  1. Seung-hyun Lim
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  2. Sun Woo Sohn
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  3. Hyoryong Lee
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  4. Donghyeon Choi
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  5. Eunsil Jang
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  6. Minhye Kim
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  8. Sukho Park
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Corresponding author

Correspondence to Sukho Park.

Additional information

Recommended by Editor-in-Chief Keum-Shik Hong.

This work was supported by a grant of the Korea Health Technology Development R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI19C0642) and the DGIST Undergraduate Group Research Program (UGRP) grant.

Seung-hyun Lim is pursuing his B.S. degree in School of Undergraduate Studies from DGIST. His research interests include mobile robotics and autonomous control.

Sun Woo Sohn is pursuing her B.S. degree in School of Undergraduate Studies from DGIST. Her research interests include MEMS and autonomous control.

Hyoryong Lee Lee received his B.S. (2017) degree in Mechanical Engineering from Kumoh National Institute of Technology, Korea. In 2017, he enrolled in Daegu Gyeongbuk Institute of Science & Technology as Master Candidate, where he is on the course of combined Master-Ph.D (2018) student in robotics engineering department. His research interests are microactuators and microrobot for the biomedical applications.

Donghyeon Choi is pursuing his B.S. degree in School of Undergraduate Studies from DGIST. His research interests include robot system control, mechanical system and automotive engineering.

Eunsil Jang is pursuing her B.S. degree in School of Undergraduate Studies from DGIST. Her research interests include rehabilitation engineering, 3D modeling and science of emotion & sensibility.

Minhye Kim is attending her B.S. degree in School of Undergraduate Studies from DGIST. Her research interests include autonomous control and robotics.

Junhyeong Lee received his B.S. degree in School of Undergraduate Studies from DGIST in 2019. His research interests include autonomous control and robotics.

Sukho Park earned his Master’s and Ph.D. degrees in Mechanical Engineering from Korea Advanced Institute of Science and Technology (KAIST), Korea, in 1995 and 2000, respectively. From 2000 to 2004, he worked as a senior research engineer at LG Electronics Production Research Center, Korea. From 2004 to 2006, he worked as a senior researcher of Microsystem Research Center in the Korea Institute of Science and Technology. From 2006 to 2016, he worked as a professor of the School of Mechanical Engineering in Chonnam National University and a section head of the robot research initiative (RRI). In 2017, he moved to Daegu Gyeongbuk Institute of Science and Technology (DGIST), where he is now a full professor in Department of Robotics Engineering. His research interests are microactuator/robot and micromanipulation for biomedical instrumental applications.

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Lim, Sh., Sohn, S.W., Lee, H. et al. Analysis and Evaluation of Path Planning Algorithms for Autonomous Driving of Electromagnetically Actuated Microrobot. Int. J. Control Autom. Syst. 18, 2943–2954 (2020). https://doi.org/10.1007/s12555-019-0637-9

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  • DOI: https://doi.org/10.1007/s12555-019-0637-9

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