Brief Bio

Caiwu Ding is a Research Professional at Siemens, Princeton, NJ. He received PhD of Mechanical Engineering at the New Jersey Institute of Technology. His research lies at the intersection of artificial intelligence, controls and vehicle design focusing on the development of robotic systems with applications in autonomous vehicle and manufacturing automation. He worked at Siemens Technology in Berkeley, CA where he built the first automation cell integrating Siemens PLC TIA Portal with industrial robots. He has 7 top-tier journal and conference publications in a diverse range of topics from control theory to aerial vehicle design and automation systems. All are first authored by Caiwu. For his research he developed the first omnidirectional aerial drilling/screwing system. He also serves as a reviewer for multiple top-tier journals and conferences in control, automation, and robotics.   

Industrial Experience

Advanced robotics R&D internship at Siemens, Berkeley, CA.   (Jun 2021 – Dec 2021)

At Siemens, Caiwu worked on R&D of robotic grasping and packing automation by investigating machine vision, deep learning and optimal control methods based on various robotic systems including ROS on Linux PC, MotoCom SDK on Siemens industrial PC and Motologix with TIA portal on Siemens SIMATIC s7 PLC. 

Research Professional at Siemens, Princeton, NJ.   (March 2022 – Current)

Research Interest

Aerial Robot Design   Design and control of Unmanned Aerial Vehicles (UAVs) for different applications

Aerial Manipulation    Control of Unmanned Aerial Vehicles (UAVs) for interaction with the environments

UAVs Motion Planning   Optimal control for UAV motion planning and multiple UAVs cooperative control

motion planning in dynamic environments and in the presence of occlusions; robot learning through user interaction; human-robot teaming.

Publication

Journal Articles:

1. C. Ding and L. Lu, "A Tilting-Rotor Unmanned Aerial Vehicle for Enhanced Aerial Locomotion and Manipulation Capabilities: Design, Control, and Applications," in IEEE/ASME Transactions on Mechatronics, doi: 10.1109/TMECH.2020.3036346. (TMECH with AIM 2021 option)[Video] 

2. C. Ding, L. Lu, C. Wang and C. Ding, "Design, Sensing, and Control of a Novel UAV Platform for Aerial Drilling and Screwing," in IEEE Robotics and Automation Letters, doi: 10.1109/LRA.2021.3062305.(RA-L with ICRA 2021 option) [Video] 

3. C. Ding, H. Peng, L. Lu and C. Ding, "Aerial Manipulation Using a Novel Unmanned Aerial Vehicle Cyber-Physical," in Newsletter of IEEE Technical Committee on Cyber-Physical Systems, Volume 05, Issue 01 (Mar. 2021).

Conference Papers:

1. Ding, C., Lu, L., Wang, C., & Ouyang, B. (2018). Modeling and control of fully actuated vector thrust unmanned aerial vehicles. In Proceedings of the International Symposium on Flexible Automation 2018 International Symposium on Flexible Automation (pp. 451-458). The Institute of Systems, Control and Information Engineers.

2. C. Ding, L. Lu, C. Wang and J. Li, "6-DOF Automated Flight Testing Using a Humanoid Robot Arm," 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), Munich, Germany, 2018, pp. 217-222, doi: 10.1109/COASE.2018.8560549.[Video] 

3. Ding, C., Lu, L., & Wang, C. (2018, September). Energy-Efficient Adaptive Robust Control of Vector Thrust UAVs With Unknown Inertia Parameters. In Dynamic Systems and Control Conference (Vol. 51913, p. V003T36A005). American Society of Mechanical Engineers.

4. S. Zhou, B. Li, C. Ding, L. Lu and C. Ding, "An Efficient Deep Reinforcement Learning Framework for UAVs," 2020 21st International Symposium on Quality Electronic Design (ISQED), Santa Clara, CA, USA, 2020, pp. 323-328, doi: 10.1109/ISQED48828.2020.9136980.

Presentation

Design, Sensing, and Control of a Novel UAV Platform for Aerial Drilling and Screwing (ICRA 2021)

Energy-Efficient Adaptive Robust Control of Vector Thrust UAVs With Unknown Inertia Parameters (DSCC 2018)

Software Skills

SolidWorks, NX, Arduino, MATLAB, C++, ROS, Python, AutoCAD, ANSYS, CNC programming, PLC, Minitab.