The Human Centered Robotics & Automation

Zheng Dongliang   

Office: 2-231, SEIEE Building

Research: Mobile Robot, Multi-robot Systems, Visual Servoing

  I am a second year master student at Shanghai Jiao Tong University (SJTU), China. I am working with my advisor Prof. Hesheng Wang. I was born in Chongqing, China, in 1993. I received my B.E degree in Automation from Northeastern University, Shenyang, China, in 2015.

  My current research interests include: Robot Control, Visual Servoing, Multi-Robot Systems, and Quadrotor Unmanned Aerial Vehicles.



[1] D. Zheng, H. Wang, J. Wang, S. Chen, W. Chen, X. Liang, "Image-Based Visual Servoing of a Quadrotor Using Virtual Camera Approach," IEEE/ASME Trans. Mechatronics, vol. 22, no. 2, pp. 972-982, 2017. 

[2] D. Zheng, H. Wang, W. Chen, "Planning and Tracking in Image Space for Image-Based Visual Servoing of a Quadrotor", IEEE Trans. Ind. Electron., Accepted

[3] D. Zheng, H. Wang, W. Chen, "Ego-Motion Estimation of a Quadrotor Based on Nonlinear Observer", IEEE/ASME Trans. MechatronicsMajor Revision

[4] D. Zheng, H. Wang, W. Chen, "Image-Based Visual Tracking of a Moving Target for a Quadrotor", ASCC 2017, Accepted.

[5] D. Zheng, H. Wang, Z. Xie, W. Chen, X. Kong, "Autonomous Navigation of a Quadrotor in Unknown Environments", ROBIO 2017, Accepted.

Research Projects:

  1. Image-based visual control of a quadrotor UAV

    Quadrotor image based visual servoing uses visual information from the onboard camera to control quadrotor motion in real-time.

    In IBVS, control inputs are directly derived from Image errors. When the image errors are driven to zero, the quadrotor is regulated at the corresponding desired position. IBVS is known to be robust compared to PBVS, but it is more complex because of the underactuation of the image feature dynamics. Relevant Paper [1]



  2. Trajectory Planning and Tracking in Image Space

    Aim to directly control and tracking in image space, jointly addressing the perception, planning and control problem for a quadrotor. Instead of planning the trajectory of the camera, a method by directly planning the trajectories of image features in image space is introduced. Then, a tracking controller is proposed to track the feature's trajectory in image space. Relevant Paper [2]



  3. Ego-motion estimation

    Design observers to estimate the translational velocity of the quadrotor and the depth of the features simultaneously by using onboard monocular camera and onboard computation. Relevant Paper [3]


  4. Quadrotor navigation and collision avoidance

    Aim to design coupled planning and control methods to navigate the quadrotor through unknown cluttered environment. Design safety-critical control methods to improve safety.

Honors and Awards:

  • Outstanding Graduates, NEU

  • Excellent Student, NEU, 2012&2013


  B.E, Northeastern University, Shenyang, China, 2011-2015;

  M.S, Shanghai Jiao Tong University, Shanghai, China, 2015-2018.