Fudan Robotics & Autonomous Systems Group (Fudan-RAS), College of Future Information Technology, Fudan University

The group is oriented towards full-stack research and development of robotics and autonomous systems, with the goal of building fully autonomous intelligent robotic systems. We carry out the corresponding fundamental research and core technology development, and publish high-impact papers.

Our research focuses on intelligent robotics and autonomous unmanned systems, covering embodied intelligence, visual perception, precise manipulation, agricultural robots, autonomous navigation, SLAM, cloud-robotic systems, robotic picking and dexterous manipulation, industrial visual inspection and analysis, deep reinforcement learning, intelligent industrial robots, and special-purpose robots. We have led or participated in more than 30 national, ministerial, and industry-academia projects, including one sub-project of the MOST National Key R&D Programme, one project of the National Natural Science Foundation of China (NSFC), one project of the Shanghai Natural Science Foundation, one PhD-programme grant of the Ministry of Education, two Shanghai Agricultural Committee science-and-technology projects, one enterprise joint research centre, and one key R&D project of the Qinghai Science and Technology Department. We have published more than 50 papers in domestic and international core journals, and filed more than 20 invention patents. The lab has made notable progress in intelligent perception and control, agricultural and facility robotics, 3D object detection, 6D pose estimation, and mobile-robot navigation and SLAM, with results published or accepted in top-tier international journals and conferences such as IEEE Transactions on Industrial Informatics, Robotics and Autonomous Systems, Computers and Electronics in Agriculture, ICRA, IROS, and ROBIO.

The lab is equipped with multiple autonomous-robot R&D platforms, sensors, computing workstations, 3D printers, and test systems, providing strong support for the corresponding research.

1. Main research directions

Robot Design — covers the mechanical structure, electrical systems, and integrated platforms of robots of various forms (ground mobile, manipulators, integrated mobile manipulation, etc.), with emphasis on performance, modularity, and real-world deployability.

Autonomous Navigation — focuses on perception, mapping, localisation, and path planning in complex dynamic environments, including visual SLAM, multi-sensor fusion, global and local path planning, and multi-robot cooperative navigation.

Manipulation & Mobile Manipulation — studies autonomous grasping, manipulation, and task execution in unknown or semi-structured environments, combining perception, decision making, and control for dexterous manipulation, with particular attention to intelligent behaviour generation for Mobile Manipulation Systems.

Our research revolves around robotics and autonomous unmanned systems, embodied intelligence, intelligent industrial robots, special-purpose robots, robot vision, etc. Major topics include:

3D visual perception

Robotic autonomous navigation, SLAM, autonomous driving

Robotic grasping and dexterous manipulation

Industrial visual inspection and analysis

Deep learning, imitation learning, semi-supervised learning

Robot modelling and control

Robot design and development (special-purpose robots, agricultural robots, bio-inspired robots)

Intelligent systems

Human-robot interaction

Action recognition

Embedded systems

2. Recruitment

The group recruits students with interest, perseverance, and ability in robotics and AI.

We have trained nearly 40 students and engineers. Graduates have gone on to top-50 universities overseas for further study, successful entrepreneurship, and employment at major enterprises and important institutions. The group adopts a dual-track "engineering + academic" model and a "T-shaped" talent-development system that gives equal weight to engineering practice and academic depth. Students master mathematical and algorithmic foundations while being deeply embedded in front-line scenarios, receiving end-to-end training from low-level mechanical design and embedded development to high-level algorithm optimisation. Thanks to this integrated practical training, graduates are highly competitive and well matched to industry roles, with major destinations including leading enterprises such as Huawei, Tencent, DJI, Li Auto, and BYD. Industry feedback indicates that our graduates rapidly bridge the gap between academia and engineering and quickly take on core R&D tasks; some have grown into technical backbones at top firms within a short time, and the most outstanding have been promoted to head of R&D within a few years.

Recruitment programmes

Electronic Information (Intelligent Systems Engineering; Artificial Intelligence; Automation; Computer Science; Electronic Circuits; Mechanical Engineering)

Full-stack hardware-software co-design and development for robotics and AI.

Undergraduate students aspiring to pursue research in robotics are welcome to join the group early for internships and to participate in research work.

3. Lab life

4. News coverage

https://news.fudan.edu.cn/2025/0412/c31a144875/page.htm

https://www.sohu.com/a/882659942_121924584

https://www.sohu.com/a/885134958_99905845

https://tv.cctv.com/2025/07/08/VIDE6e0HCq3bUTQibpdIXoZ6250708.shtml

Executive Director, Shanghai Society of Agricultural Engineering

Executive Committee Member, CCF Intelligent Robotics Association

Has led or participated in more than 30 national, ministerial, and industry-academia projects, including sub-projects of MOST National Key R&D Programmes, NSFC grants, Shanghai Natural Science Foundation grants, PhD-programme grants of the Ministry of Education, and Shanghai Agricultural Science and Technology Committee grants. Director of the University-Enterprise Joint Laboratory for Integrated Sensing and Artificial-Intelligence Education. More than 50 papers published in domestic and international core journals; more than 30 patents filed and over 20 granted. Skilled in the overall design and implementation of innovative electronic-information systems and in robotic system design and development, with extensive experience in industry-university-research collaboration and the translation of results, which have been covered repeatedly by major media outlets including CCTV and People.cn.

July 1999: B.Eng., Department of Light Sources and Illuminating Engineering, Fudan University

July 2002: M.Eng., Department of Electronic Engineering, Fudan University

July 2005: Ph.D., Department of Electronic Engineering, Fudan University

July 2002 - Present: Lecturer, Associate Professor, Department of Electronic Engineering, Fudan University

Undergraduate Courses:

High-Frequency Electronic Circuits Laboratory

Fundamentals of Analog Electronics

Introduction to Embodied Intelligence

Graduate Courses:

Robotics & Autonomous System

Intelligent Unmanned Systems

Huiliang Shang, Xueyi Chi, Ruijiao Li, Xuan Zhao, Huosheng Hu. Obstacle-Aware and High-Reach Path Planning for Robotic Manipulators in Complex Factory Farming Environments, IEEE Transactions on Industrial Informatics.(2025)

Jiawei Wei, Yuzhen Pan, Liping Sun, Huiliang Shang, and Xiong Chen. "A novel redundant cooperative control strategy for rob otic pollination", Computers and Electronics in Agriculture 220.C (2024)

Anzheng Zhang, Yuzhen Pan, Chenyun Zhang, Jinhua Wang, Guangrong Chen, Huiliang Shang*. Design and Implementation of a Novel Agricultural Robot with Multi-Modal Kinematics. 16th International Conference on Intelligent Robotics and Applications (ICIRA), 2023.

Yuzhen Pan,, Jiawei Wei, Huiliang Shang*. Mechanism Design of a Multi-linkage Parallel Hopping Robot with Kinematics Analysis. 2022 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2022.

Pan Yuzhen, Wei Jiawei，KHAN Rezwan Al Islam，Chen Xiong, Wang Hongbo, Shang Huiliang. Design analysis and redundant cooperative control of a novel modular agricultural robot.

Journal of Mechanical Engineering，2024，60(11): 1-14

Zhenxiao Zhao, Lei Zhang, Huiliang Shang*. A Lightweight Subgraph-Based Deep Learning Approach for Fall Recognition. Sensors, 2022, 22(15), 5482. (SCI IF=3.9)

Han Wu a, Huiliang Shang;. Potential game for dynamic task allocation in multi-agent system. ISA Transactions. 2020.

Huiliang Shang , Yudong Tao, Yuan Gao and Chen Zhang, Xiaoling Wang*. An Improved Invariant for Matching Molecular Graphs based on VF2 Algorithm. IEEE Transactions on Systems, Man and Cybernetics: Systems ， 45(1) (2015), 122 - 128 . (SCI ) . WOS:000346733600010

Huiliang Shang , Yuan Gao , Jiajun Zhu, Feng Li, An Optmized Circuit Simulation Method for the Identification of Isomorphic Disconnected Graphs. Circuits, Systems, and Signal Processing ( SCI ), 32(5 ), (2013), 2469-2473. WOS:000323656500027

Huiliang Shang , Feng Li, XianDing Tang, Peng-Yung Woo. A New Algorithm for Isomorphism Determination of Undirected Graphs-Circuit Simulation Method. Circuits, Systems, and Signal Processing ( SCI ), 30(5 ) (2011), 1115-1130. WOS:000293185600015

Wang, Miao; Zhang, Qi; Zhu, Jiajun; Tao, Yudong; Kong, Qingsheng; Shang, Huiliang *. A New Computerized Tongue Diagnosis Method with Optimized Outline Extraction Algorithm Using HSV Color Model. Journal of Computational and Theoretical Nanoscience ( SCI ), 11( 6) (2014), 1556-1562(7). WOS:000336084800023

Feng Li, Huiliang Shang , Peng-Yung Woo. Determination of Isomorphism and Its Applications for Arbitrary Graphs Based on Circuit Simulation. Circuits, Systems, and Signal Processing ( SCI ), 27(5) (2008),749-761. WOS:000259859900011

Huiliang Shang*. ，F Kang，C Xu，G Chen，S Zhang. The SVE Method for Regular Graph Isomorphism Identification.Circuits Systems & Signal Processing, 2015, 34:1-10 ( SCI )

Li Chen, Dongyi Wang, Yiqin Liu, Xiaohang Gao, Huiliang Shang*. (2015, November). A novel automatic tongue image segmentation algorithm: Color enhancement method based on L* a* b* color space. In Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on (pp. 990-993). IEEE. ( EI )

Huiliang Shang, Zhang, Qi; Jin, Mimin; Wang, Wenxin; Zhu, Jiajun; Kong, Qingsheng. A portable pulse signal acquiring and monitoring system based on Android platform. In Bioinformatics and Biomedicine (BIBM), 2013 IEEE International Conference on (pp. 226-227). IEEE.[EI: 20141017414437].

Shang Hui-Liang, Xu, Ren-Mei, Yuan Jun-Kang. A Smart Home System Based On Zigbee and IOS Software.Proceedings of the International Conference on Parallel and Distributed Systems – ICPADS 2012, p 940-944, 2012 [EI: 20130916048030].

Huiliang Shang, Xiong Chen, Jichuan Li, Wenjie Dong, Peng-Yung Woo. Isomorphism Detection of Kinematic Chains Based on the Improved Circuit Simulation Method, International Journal of Computer Applications in Technology, 46(3)(2013),236-243 [EI: 20131416180718].