The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
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Reliable Autonomous Reconnaissance System for a Tracked Robot in Multi-floor Indoor Environments with Stairs

다층 실내 환경에서 계단 극복이 가능한 궤도형 로봇의 신뢰성 있는 자율 주행 정찰 시스템

  • Juhyeong Roh (Robotics Program, KAIST) ;
  • Boseong Kim (Electrical Engineering, KAIST) ;
  • Dokyeong Kim (Division of Future Vehicle, KAIST) ;
  • Jihyeok Kim (Electrical Engineering, KAIST) ;
  • D. Hyunchul Shim (Electrical Engineering, KAIST)
  • Received : 2024.05.01
  • Accepted : 2024.05.21
  • Published : 2024.05.31
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Abstract

This paper presents a robust autonomous navigation and reconnaissance system for tracked robots, designed to handle complex multi-floor indoor environments with stairs. We introduce a localization algorithm that adjusts scan matching parameters to robustly estimate positions and create maps in environments with scarce features, such as narrow rooms and staircases. Our system also features a path planning algorithm that calculates distance costs from surrounding obstacles, integrated with a specialized PID controller tuned to the robot's differential kinematics for collision-free navigation in confined spaces. The perception module leverages multi-image fusion and camera-LiDAR fusion to accurately detect and map the 3D positions of objects around the robot in real time. Through practical tests in real settings, we have verified that our system performs reliably. Based on this reliability, we expect that our research team's autonomous reconnaissance system will be practically utilized in actual disaster situations and environments that are difficult for humans to access, thereby making a significant contribution.

Keywords

Acknowledgement

This project was financially supported by the Institute of Civil Military Technology Cooperation funded by the Defense Acquisition Program Administration and Ministry of Trade, Industry and Energy of Korean government under grant No.UM22206RD2.

References

  1. C. Kang, "Intelligent mobile robot technology and application," vol. 70, no. 2, pp. 9-13, Feb., 2021, [Online], https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE10525417.  10525417
  2. Y. Sun, L. Guan, Z. Chang, C. Li, and Y. Gao, "Design of a low-cost indoor navigation system for food delivery robot based on multi-sensor information fusion," Sensors, vol. 19, no. 22, Nov., 2019, DOI: 10.3390/s19224980. 
  3. T. Kim, G. Kang, D. Lee, and D. H. Shim, "Development of an Indoor Delivery Mobile Robot for a Multi-Floor Environment," IEEE Access, vol. 12, pp. 45202-45215, 2024, DOI: 10.1109/ACCESS.2024.3381489. 
  4. K. Thamrongaphichartkul, N. Worrasittichai, T. Prayongrak, and S. Vongbunyong, "A Framework of IoT Platform for Autonomous Mobile Robot in Hospital Logistics Applications," 2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), Bangkok, Thailand, pp. 1-6, 2020, DOI: 10.1109/iSAI-NLP51646.2020.9376823. 
  5. G. Fragapane, R. de Koster, F. Sgarbossa, and J. O. Strandhagen, "Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda," European Journal of Operational Research, vol. 294, no. 2, pp. 405-426, 2021, DOI: 10.1016/j.ejor.2021.01.019. 
  6. S. Narayan, M. Aquif, A. R. Kalim, D. Chagarlamudi, and M. H. Vignesh, "Search and Reconnaissance Robot for Disaster Management," Machines, Mechanism and Robotics, Springer, Singapore, 2021, pp. 187-201, DOI: 10.1007/978-981-16-0550-5_17.
  7. S. Li, C. Feng, Y. Niu, L. Shi, Z. Wu, and H. Song, "A fire reconnaissance robot based on SLAM position, thermal imaging technologies, and AR display," Sensors, vol. 19, no. 22, Nov., 2019, DOI: 10.3390/s19225036. 
  8. H. Miura, A. Watanabe, M. Okugawa, T. Miura, and T. Koganeya, "Plant inspection by using a ground vehicle and an aerial robot: lessons learned from plant disaster prevention challenge in world robot summit 2018," Advanced Robotics, vol. 34, no. 2, pp. 104-118, 2020, DOI: 10.1080/01691864.2019.1690575. 
  9. Y. Kobayashi, S. Kanai, C. Kikumoto, and K. Sakoda, "Design and fabricate of reconnaissance robots for nuclear power plants that underwent accidents," Journal of Robotics and Mechatronics, vol. 34, no. 3, pp. 523-526, Jun., 2022, DOI: 10.20965/jrm.2022.p0523. 
  10. D. Lee, G. Kang, T. Kim, D. H. Shim, H. Jung, and E. Kim, "Route Planning and Elevator Boarding Algorithms for Last Mile Delivery Service in Multi-floor Environments," The Journal of Korea Robotics Society, vol. 18, no.1, pp. 10-17, Feb., 2023, DOI: 10.7746/jkros.2023.18.1.010. 
  11. Z. Zhao, J. Zhao, and Y. Lou, "Autonomous operation of elevator buttons for multi-floor navigation," Proceedings of 2020 Chinese Intelligent Systems Conference: Volume II, Springer Singapore, 2021, pp. 161-170, DOI: 10.1007/978-981-15-8458-9_18. 
  12. S. Shin, J. Lee, J. Noh, and S. Choi, "Robust Detection for Autonomous Elevator Boarding Using a Mobile Manipulator," Asian Conference on Pattern Recognition, Kitakyushu, Japan, pp. 15-28, 2023, DOI: 10.1007/978-3-031-47634-1_2. 
  13. B. Kim, C. Jung, D. H. Shim, and A. Agha-mohammadi, "Adaptive Keyframe Generation based LiDAR Inertial Odometry for Complex Underground Environments," 2023 IEEE International Conference on Robotics and Automation (ICRA), London, United Kingdom, pp. 3332-3338, 2023, DOI: 10.1109/ICRA48891.2023.10161207. 
  14. B. Kim, S. Lee, J. Park, and D. H. Shim, "3D Costmap Generation and Path Planning for Reliable Autonomous Flight in Complex Indoor Environments," The Journal of Korea Robotics Society, vol. 18, no. 3, pp. 337-345, Aug., 2023, DOI: 10.7746/jkros.2023.18.3.337. 
  15. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: Unified, real-time object detection," arXiv:1506. 02640, 2016, DOI: 10.48550/arXiv.1506.02640. 
  16. A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, "Pointpillars: Fast encoders for object detection from point clouds," arXiv:1812.05784, 2018, DOI: 10.48550/arXiv.1812.05784. 
  17. S. Liu, Z. Zeng, T. Ren, F. Li, H. Zhang, J. Yang, C. Li, J. Yang, H. Su, J. Zhu, and L. Zhang, "Grounding dino: Marrying dino with grounded pre-training for open-set object detection," arXiv:2303.05499, 2023, DOI: 10.48550/arXiv.2303.05499. 
  18. I. de L. Paez-Ubieta, E. Velasco-Sanchez, S. T. Puente, and F. A. Candelas, "Detection and depth estimation for domestic waste in outdoor environments by sensors fusion," IFAC-PapersOnLine, vol. 56, no. 2, pp. 9276-9281, 2023, DOI: 10.1016/j.ifacol.2023.10.211. 
  19. C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, "Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors," arXiv:2207.02696, 2022, DOI: 10.48550/arXiv.2207.02696. 
  20. South Korea, Ministry of Land, Infrastructure and Transport, "Regulations on Housing Construction Standards", Article 16(Stairs), Paragraph 1, 2024. 
  21. South Korea, Ministry of health and welfare, "Enforcement Rule of the Act on Guarantee of Promotion of Convenience of Persons with Disabilities, the Elderly, and Pregnant Women", Annex 1, 2023. 
  22. J. Morales, J. L. Martinez, M. A. Martinez, and A. Mandow, "Pure-pursuit reactive path tracking for nonholonomic mobile robots with a 2D laser scanner," EURASIP Journal on Advances in Signal Processing, May, 2009, DOI: 10.1155/2009/935237. 
  23. Z. Y. Chen, G. Z. Chen, L. Z. Qi, F. Yin, D. D. Li, and M. J. Shi, "Steering dynamic performance analysis of the tracked mobile robot," IOP Conference Series: Materials Science and Engineering, vol. 392, no. 6, 2018, DOI: 10.1088/1757-899X/392/6/062038. 
  24. A. I. Mourikis, N. Trawny, S. I. Roumeliotis, D. M. Helmick, and L. Matthies, "Autonomous stair climbing for tracked vehicles," The International Journal of Robotics Research, vol. 26, no. 7, pp. 737-758, Jul., 2007, DOI: 10.1177/0278364907080423. 
  25. Reliable Autonomous Reconnaissance System for a Tracked Robot, [Online], https://youtu.be/PvutVRnyGuY?si=TOpW0Y_56Cmj3P-G, Accessed: May 23, 2024. 
  26. Tracked Robot's Autonomous Exploration and Object Detection in Complex Multi-Floor Environments, [Online], https://youtu.be/ua-bf6es4ac?si=J2Jqw-md5zouD9Vl, Accessed: May 23, 2024.