Chung‐Yen Lin

About

Chung-Yen Lin is a robotics and optimization researcher whose work sits at the intersection of motion planning, robot control, and machine learning. He is perhaps best known for developing the Convex Feasible Set (CFS) Algorithm, a groundbreaking approach to real-time motion planning that reformulates highly nonconvex trajectory optimization problems into tractable convex subproblems. This work, published in 2017–2018, has accumulated over 130 citations and represents a significant advance in enabling robots to navigate cluttered environments at computational speeds suitable for real-world deployment. Beyond motion planning, Lin has made notable contributions to vision-guided robotics, particularly in compensating for the slow sampling rates and latency inherent in industrial vision hardware. Through statistical learning and probabilistic frameworks—including Expectation-Maximization and pose estimation techniques—his research has helped bridge the gap between sensor limitations and the demands of real-time visual servoing. He has also explored trajectory planning for robot service life optimization and model learning, demonstrating a commitment to practically impactful, industrially relevant research. With a body of work spanning trajectory smoothing, robot dynamics identification, and learning-based control, Lin has established himself as a versatile contributor to modern robotics, accumulating nearly 270 citations across his published work.

Research Focus

Key Achievements

Top Papers

1. 1
   The Convex Feasible Set Algorithm for Real Time Optimization in Motion Planning

   133 citations · 2018
2. 2
   Convex feasible set algorithm for constrained trajectory smoothing

   43 citations · 2017
3. 3
   Statistical Learning Algorithms to Compensate Slow Visual Feedback for Industrial Robots

   32 citations · 2014
4. 4
   Fast planning of well conditioned trajectories for model learning

   13 citations · 2014
5. 5
   Visual Servoing Considering Sensing Dynamics and Robot Dynamics

   11 citations · 2013
6. 6
   Pose estimation in industrial machine vision systems under sensing dynamics: A statistical learning approach

   9 citations · 2014
7. 7
   Path-constrained trajectory planning for robot service life optimization

   8 citations · 2016
8. 8
   Visual tracking with sensing dynamics compensation using the Expectation-Maximization algorithm

   7 citations · 2013
9. 9
   The Convex Feasible Set Algorithm for Real Time Optimization in Motion Planning

   7 citations · 2017
10. 10
    Probabilistic Approach to Modeling and Parameter Learning of Indirect Drive Robots From Incomplete Data

    6 citations · 2014

Key Collaborators

Contact & Links