OROCA seminar 2017 (Leon-Ryuwoon-Jung / Kihoon-Kim / Jinchul-Baek)

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The slides presented in OROCA seminar 2017. This presentation talks about how the AuTURBO, a team that researches and develops an Autonomous Driving Platform, was banded, and describes how the team won the prize in AutoRace 2017 - RBiz Challenge Seoul. contact: Leon Ryuwoon Jung : jung.ryuwoon@gmail.com Kihoon Kim : skfk3606@naver.com Jinchul Baek : gunstar089@gmail.com

Indústria automotiva

정 려운
김 기훈
백 진철
jung.ryuwoon@gmail.com
거북이의
첫 도로주행
OROCA 자율주행 프로젝트
AuTURBO

도로 주행 시험을 위해
6
OROCA – AuTURBO 팀
비전 처리 파트 : 2명
AI 파트 : 2명
센서링 파트 : 2명
SI 파트 : 2명
제어 파트 : 2명

도로 주행 시험을 위해
7
OROCA – AuTURBO 팀
ROS
Vision
AI
공부방

추후 일정
11
1. OROCA-AuTURBO 2년차 추가 멤버 모집 (3월 예상)
OROCA.org AuTURBO 게시판에 추후 선발 공지
2. 2018년도 대회 출전 준비
소문으로는 스페인에서 한다고…
3. 4륜형 자율주행 로봇 제작

관련 자료
12
http://emanual.robotis.com/docs/en/platform/turtlebot3/autonomous_driving/
#turtlebot3-auto
TurtleBot3 AutoRace 대회 및 자율주행 소스 관련
http://cafe.naver.com/ArticleList.nhn?search.clubid=25572101&search.menuid=149
&search.boardtype=L
OROCA.org - AuTURBO

OROCA - AuTURBO 소개
13
양재호 님 허 준 님
조동운 님 백진철 님
최종호 님 김기훈 님
이현옥 님 김도현 님
고명준 님 정려운

Loading 팀의
터틀봇3 오토레이스 후기
발표자 김기훈
skfk3606@naver.com

터틀봇3 오토레이스 진행과정
• 백 진철
gunstar089@gmail.com

차선인식, 신호등 인식
• 영상인식을 위해서 필요한 기능
– 영상인식을 위한 인공지능을 위해 tensorflow
를 사용한다.
– 하드웨어 지원이 되는 추론엔진을 사용한다.
– 내부 플래시 메모리를 사용하여 IO 속도를 최
대화 한다.
– 메인 메모리를 가능한한 많이 확보한다.
29

• 인공지능을 추론을 위해서는 GPU가 필요하다.
– 일반 PC를 사용하면 전력문제와 크기 문제가 있다. 판다와 같은 작은
임베디드 보드는 GPU가 없다.
– 임베디드 GPU를 사용할 수있는 DrivePX2를 사용할 수 있으나, 역시 크
기와 가격이 ...
– jetson TX 는 GPU core를 지원한며, 크기도 작아서 사용이 가능하다.
– tensorflow및 caffe를 지원한다.
– 1테라플롭스에 달하는 연산성능을 지원한다.
• Jetson TK는 300기가플롭스
• 인텔이 인수한 모비디우스는 100기가플롭스
Jetson TX를 선택한 이유
30

터틀봇3와 jetson TX2
터틀봇3와 Jetson TX2 크기 비교
터틀봇3와 AUVIDEA J140보드
33

문제발생
• J140 전원코드가 따로 없어 Jetson TX2 전원
사용하다 보드를 날림.
• 추가 구매에 딜리버리 2달 날림.
• Jetson TX2의 기본 IO디바이스와 달라 커널
레벨 컴파일이 필요.
• tensorflow를 이용한 영상인식 알고리즘
(fasterRCNN)을 이용시 성능부족: 360p를 초
당 3~5프레임 인식)
35

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OROCA seminar 2017 (Leon-Ryuwoon-Jung / Kihoon-Kim / Jinchul-Baek)

1. 정 려운 김 기훈 백 진철 jung.ryuwoon@gmail.com 거북이의 첫 도로주행 OROCA 자율주행 프로젝트 AuTURBO

6. 도로 주행 시험을 위해 6 OROCA – AuTURBO 팀 비전 처리 파트 : 2명 AI 파트 : 2명 센서링 파트 : 2명 SI 파트 : 2명 제어 파트 : 2명

7. 도로 주행 시험을 위해 7 OROCA – AuTURBO 팀 ROS Vision AI 공부방

8. 도로 주행 시험 8

9. v 9 TurtleBot3 규정 + Sensors, etc.

10. 도로 주행 시험 주행 결과 10

11. 추후 일정 11 1. OROCA-AuTURBO 2년차 추가 멤버 모집 (3월 예상) OROCA.org AuTURBO 게시판에 추후 선발 공지 2. 2018년도 대회 출전 준비 소문으로는 스페인에서 한다고… 3. 4륜형 자율주행 로봇 제작

12. 관련 자료 12 http://emanual.robotis.com/docs/en/platform/turtlebot3/autonomous_driving/ #turtlebot3-auto TurtleBot3 AutoRace 대회 및 자율주행 소스 관련 http://cafe.naver.com/ArticleList.nhn?search.clubid=25572101&search.menuid=149 &search.boardtype=L OROCA.org - AuTURBO

13. OROCA - AuTURBO 소개 13 양재호 님 허 준 님 조동운 님 백진철 님 최종호 님 김기훈 님 이현옥 님 김도현 님 고명준 님 정려운

14. Loading 팀의 터틀봇3 오토레이스 후기 발표자 김기훈 skfk3606@naver.com

15. 15

16. PID Control error error 16

17. Homography transformation 17

18. 18

19. 신호등 표지판 19

20. 20

21. 21

22. 22

23. 차단바 23

24. 주차장 24

25. 25

26. 26

27. 27

28. 터틀봇3 오토레이스 진행과정 • 백 진철 gunstar089@gmail.com

29. 차선인식, 신호등 인식 • 영상인식을 위해서 필요한 기능 – 영상인식을 위한 인공지능을 위해 tensorflow 를 사용한다. – 하드웨어 지원이 되는 추론엔진을 사용한다. – 내부 플래시 메모리를 사용하여 IO 속도를 최 대화 한다. – 메인 메모리를 가능한한 많이 확보한다. 29

30. • 인공지능을 추론을 위해서는 GPU가 필요하다. – 일반 PC를 사용하면 전력문제와 크기 문제가 있다. 판다와 같은 작은 임베디드 보드는 GPU가 없다. – 임베디드 GPU를 사용할 수있는 DrivePX2를 사용할 수 있으나, 역시 크 기와 가격이 ... – jetson TX 는 GPU core를 지원한며, 크기도 작아서 사용이 가능하다. – tensorflow및 caffe를 지원한다. – 1테라플롭스에 달하는 연산성능을 지원한다. • Jetson TK는 300기가플롭스 • 인텔이 인수한 모비디우스는 100기가플롭스 Jetson TX를 선택한 이유 30

31. Jetson TX 31

32. Jetson TX 32

33. 터틀봇3와 jetson TX2 터틀봇3와 Jetson TX2 크기 비교 터틀봇3와 AUVIDEA J140보드 33

34. 터틀봇3에 J140보드 실장 34

35. 문제발생 • J140 전원코드가 따로 없어 Jetson TX2 전원 사용하다 보드를 날림. • 추가 구매에 딜리버리 2달 날림. • Jetson TX2의 기본 IO디바이스와 달라 커널 레벨 컴파일이 필요. • tensorflow를 이용한 영상인식 알고리즘 (fasterRCNN)을 이용시 성능부족: 360p를 초 당 3~5프레임 인식) 35

36. Thank you

OROCA seminar 2017 (Leon-Ryuwoon-Jung / Kihoon-Kim / Jinchul-Baek)

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