The Journal of the Korea institute of electronic communication sciences (한국전자통신학회논문지)

Korea Institute of Electronic Communication Science (한국전자통신학회)
권호 표지
DOI QR코드

DOI QR Code

Image Processing Processor Design for Artificial Intelligence Based Service Robot

인공지능 기반 서비스 로봇을 위한 영상처리 프로세서 설계

  • Received : 2022.06.30
  • Accepted : 2022.08.17
  • Published : 2022.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

As service robots are applied to various fields, interest in an image processing processor that can perform an image processing algorithm quickly and accurately suitable for each task is increasing. This paper introduces an image processing processor design method applicable to robots. The proposed processor consists of an AGX board, FPGA board, LiDAR-Vision board, and Backplane board. It enables the operation of CPU, GPU, and FPGA. The proposed method is verified through simulation experiments.

다양한 분야에 서비스 로봇이 적용됨에 따라 각 임무에 적합한 영상처리 알고리즘을 빠르고 정확하게 수행할 수 있는 영상처리 프로세서에 관한 관심이 높아지고 있다. 본 논문에서는 로봇에 적용 가능한 영상처리 프로세서 설계방법을 소개한다. 제안한 프로세서는 CPU, GPU, FPGA가 융합된 형태로 AGX 보드, FPGA 보드, LiDAR-Vision 보드, Backplane 보드로 구성된다. 제안한 방법은 시뮬레이션 실험을 통해 검증한다.

Keywords

Acknowledgement

이 논문은 2021학년도 조선대학교 학술연구비의 지원을 받아 연구되었음.

References

  1. J. Fan, Z. Pai, and L. Shufei, "Vision-based holistic scene understanding towards proactive human-robot collaboration," Robotics and Computer-Integrated Manufacturing, vol. 75, 2022, pp. 102304. https://doi.org/10.1016/j.rcim.2021.102304
  2. M. Bujanca, P. Gafton, S. Saeedi, A. Nisbet, B. Bodin, M. OBoyle, and S. Furber, "SLAMBench 3.0: Systematic automated reproducible evaluation of SLAM systems for robot vision challenges and scene understanding," In 2019 International Conference on Robotics and Automation, Montreal, Canada, 2019, pp. 6351-6358.
  3. M. Naseer, S. Khan, and F. Porikli, "Indoor scene understanding in 2.5/3d for autonomous agents: A survey," IEEE access, vol. 7, 2018, pp. 1859-1887. https://doi.org/10.1109/ACCESS.2018.2886133
  4. M. Browne and S. Ghidary, "Convolutional neural networks for image processing: an application in robot vision," In Australasian Joint Conference on Artificial Intelligence, Sydney, Australia, 2003, pp. 641-652.
  5. Z. Cao, T. Liao, W. Song, Z. Chen, and C. Li, "Detecting the shuttlecock for a badminton robot: A YOLO based approach," Expert Systems with Applications, vol. 164, 2021, pp. 113833. https://doi.org/10.1016/j.eswa.2020.113833
  6. J. Bong, J. Lee, L. Jo, H. Jo, and G. Jeong, "Mobile Manipulator, Vision-based Manipulation, Rotational Inertia, Image Processing, Pick and Place," J. of the Korea Institute of Electronics Communications Sciences, vol. 17, no. 3, 2022, pp. 507-514.
  7. J. Moon, J. Moon, and S. Bea, "Control for Manipulator of an Underwater Robot Using Meta Reinforcement Learning," J. of the Korea Institute of Electronics Communications Sciences, vol. 16, no. 1, 2021, pp. 95-100.
  8. D. Madusanka, R. Gopura, Y. Amarasinghe, and G. Mann, "Hybrid vision based reach-to-grasp task planning method for trans-humeral prostheses," IEEE Access, vol. 5, 2017, pp. 16149-16161. https://doi.org/10.1109/ACCESS.2017.2727502
  9. D. Ebert and D. Henrich, "Safe human-robot-cooperation: Image-based collision detection for industrial robots," In IEEE/RSJ international conference on intelligent robots and systems, Lausanne, Switzerland, 2002, pp. 1826-1831.
  10. M. Vestias and H. Neto, "Trends of CPU, GPU and FPGA for high-performance computing," In 24th International Conference on Field Programmable Logic and Applications, Belfast, United Kingdom, 2014, pp. 1-6.
  11. S. Asano, T. Maruyama, and Y. Yamaguchi, "Performance comparison of FPGA, GPU and CPU in image processing," In international conference on field programmable logic and applications, Czech Republic, 2009, pp. 126-13.