Faculty Research Profile
Jeong hwan Jeon
학력
2015.09: Ph.D. in Aeronautics and Astronautics, MIT
2009.09: S.M. in Aeronautics and Astronautics, MIT
2007.02: B.S. in Mechanical and Aerospace Engineering, Seoul National University
주요 경력
2019.08 ~ Current: Assistant Professor, UNIST
2018.11 ~ 2019.08: Principal Research Scientist, nuTonomy (an Aptiv company)
2015.09 ~ 2018.10: Senior Research Scientist, nuTonomy (an Aptiv company, acquired in 2017)
수상/학회/외부활동
Current: Member of the IEEE
2021 ~ 2022: Council member for the IEIE (대한전자공학회)
2021.06: Outstanding Young Researcher Award from the ICROS (제어로봇시스템학회)
로보틱스 및 모빌리티 연구실
Robotics and Mobility Lab.
"로보틱스 및 모빌리티 연구실"의 전문 분야는 로보틱스, 제어 이론, 모션 계획, 최적화 방법, 자율 시스템, 학습 기반 알고리즘, 그리고 자율주행차와 물류로봇을 포함한 미래 모빌리티 분야입니다. MIT와 nuTonomy (2017년 Delphi / Aptiv가 인수한 자율주행 기술 스타트업, 2020년 Aptiv와 현대자동차의 합작벤처 Motional로 이어짐) 에서의 연구 개발 경험을 학문적으로 승화시키고 관련 기술을 발전시켜 모든 사람의 일상 생활을 보다 안전하고 효율적으로 만들고자 합니다.
현재 연구 초점은
1) 로봇 및 자율 시스템을 위한 알고리즘
2) 제어 이론 및 활용
3) 안전성과 효율성이 극대화된 미래 모빌리티 시스템 및 구성 요소의 설계, 개발, 배포를 통한 교통사고 사망자 제로의 달성
에 있습니다. 로보틱스와 제어 이론의 연구 결과는 미래 모빌리티 시스템을 개선하는 데 도움이 되는 경우가 많습니다.
Dr. Jeon’s areas of expertise lie in the fields of control theory, motion planning, optimization methods, robotics, autonomous systems, and future mobility including self-driving cars. After years of research at MIT and four years of work at nuTonomy (a self-driving technology startup acquired by Delphi/Aptiv in 2017, later led to Motional, a joint venture by Aptiv and Hyundai Motor Company), he hopes to make everyone’s daily living safer and efficient by technological advances. His current focus of research is on 1) algorithms for robots and autonomous systems, 2) control theory and applications, 3) the design, development, and deployment of future mobility system and many of its components with maximized safety and efficiency, ultimately aiming for zero traffic fatality. Very often, research outcomes in robotics and control theory lead to the improved future mobility system.
Dr. Jeon’s areas of expertise lie in the fields of robotics, control theory, motion planning, optimization methods, autonomous systems, learning-based algorithms, and future mobility including self-driving cars and logistics robots. After years of research at MIT and four years of work at nuTonomy (a self-driving technology startup acquired by Delphi/Aptiv in 2017, later led to Motional, a joint venture by Aptiv and Hyundai Motor Company), he hopes to make everyone’s daily living safer and efficient by technological advances. His current focus of research is on 1) algorithms for robots and autonomous systems, 2) control theory and applications, 3) the design, development, and deployment of future mobility system and many of its components with maximized safety and efficiency, ultimately aiming for zero traffic fatality. Very often, research outcomes in robotics and control theory lead to the improved future mobility system.
연구분야
제어, 모션 플래닝, 최적화, 학습 기반 알고리즘, 로보틱스, 자율 시스템, 미래 모빌리티, 자율주행차, 물류로봇
control, motion planning, optimization, learning-based algorithms, robotics, autonomous systems, future mobility, self-driving cars, logistics robots
연구 희망분야
제어, 모션 플래닝, 최적화, 학습 기반 알고리즘, 로보틱스, 자율 시스템, 미래 모빌리티, 자율주행차, 물류로봇
control, motion planning, optimization, learning-based algorithms, robotics, autonomous systems, future mobility, self-driving cars, logistics robots
연구주제
- 제어, 모션 플래닝, 최적화, 학습 기반 알고리즘, 로보틱스, 자율 시스템, 미래 모빌리티, 자율주행차, 물류로봇, 수요응답형 모빌리티
- 안전한 학습 기반 알고리즘, 행위/의도 예측, 악의적 공격에 대한 안전, 로봇의 고성능 기동, 자율주행차를 위한 교통 제어, 에너지 효율적인 운전
- control theory, motion planning, optimization methods, learning-based algorithms, robotics, autonomous systems, future mobility, self-driving cars, logistics robots, on-demand mobility
- safe learning-based algorithms, behavior/intent prediction, safety against malicious attacks, high-performance robotic maneuvers, traffic control for self-driving cars, energy-efficient driving
- control theory, motion planning, optimization methods, learning-based algorithms, robotics, autonomous systems, future mobility, self-driving cars, logistics robots, on-demand mobility
- safe learning-based algorithms, behavior/intent prediction, safety against malicious attacks, high-performance robotic maneuvers, traffic control for self-driving cars, energy-efficient driving
국가연구개발사업 기술 분류체계
국가과학기술표준분류
ED. 전기/전자 > ED11. 무기센서 및 제어 > ED1114. 무인자동화
논문
- IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), Anytime computation of time-optimal off-road vehicle maneuvers using the RRT*, J. Jeon, S. Karaman, E. Frazzoli (2011)
- American Control Conference (ACC), Optimal motion planning with the half-car dynamical model for autonomous high-speed driving, J. Jeon, R. V. Cowlagi, S. C. Peters, S. Karaman, E. Frazzoli, P. Tsiotras, K. Iagnemma (2013)
- IEEE International Conference on Robotics and Automation (ICRA), Optimal sampling-based feedback motion trees among obstacles for controllable linear systems with linear constraints, J. Jeon, S. Karaman, E. Frazzoli (2015)
특허
- AI 4족 보행 로봇을 활용한 공유 플랫폼 및 그 방법, 2023.07. 출원
- 조도가 급변하는 환경에서 3D 객체를 탐지하는 방법 및 장치, 2023.05. 출원