Chen Song

About

Chen Song is a leading researcher in the design and control of cable-driven parallel robots, with a particular focus on overcoming the fundamental challenges of actuation redundancy and unilateral cable constraints. His major contributions include the development of a Workspace-Based Model Predictive Control (W-MPC) scheme, which enables real-time, stable control of these complex nonlinear systems—a paper that has garnered 43 citations since 2022. Song has also pioneered hybrid actuation strategies, such as combining thrusters with cables for enhanced workspace feasibility (22 citations), and introduced innovative cable routing designs that minimize the number of actuators required for multi-link robots (13 citations). His work extends to practical applications, including the "Hybrid Pose Adjustment (HyPA) robot" for modular construction assembly (2024), and the development of the open-source CASPR-ROS software framework, which bridges simulation and hardware implementation for the cable robotics community. With a growing citation record and a focus on both theoretical rigor and hardware deployment, Song’s research is shaping the future of safe, lightweight, and high-performance robotic systems for manufacturing and construction.

Research Focus

Key Achievements

Top Papers

1. 1
   Workspace-Based Model Predictive Control for Cable-Driven Robots

   43 citations · 2022
2. 2
   Wrench-feasible workspace-based design of hybrid thruster and cable driven parallel robots

   22 citations · 2022
3. 3
   Cable Routing Design and Performance Evaluation for Multi-Link Cable-Driven Robots With Minimal Number of Actuating Cables

   13 citations · 2019
4. 4
   Hybrid pose adjustment (HyPA) robot design for prefabricated module control in modular construction assembly

   10 citations · 2024
5. 5
   CASPR-ROS: A Generalised Cable Robot Software in ROS for Hardware

   4 citations · 2017

Key Collaborators

Contact & Links