Max Robotics — Global Service Robot Directory + US Certification

About

Tengda Gu is a robotics researcher whose work focuses on advancing path planning, workspace analysis, and precision control for both service and industrial robots. His key research areas include motion planning algorithms, robotic 3D printing, and workspace visualization. Gu’s most cited paper, “An Improved RRT* Path Planning Algorithm for Service Robot” (2020, 18 citations), addresses the limitations of the traditional RRT* algorithm in complex environments by reducing memory usage and accelerating convergence. He further contributes to additive manufacturing with “A Postprocessing and Path Optimization Based on Nonlinear Error for Multijoint Industrial Robot-Based 3D Printing” (2020, 12 citations), where he tackles the critical issue of nonlinear error from rotational joints to enable precise fabrication of freeform surfaces. Additionally, his work “A Workspace Visualization Method for a Multijoint Industrial Robot Based on the 3D-Printing Layering Concept” (2020, 3 citations) introduces an innovative approach to detailed workspace mapping, providing essential constraints for reliable robot control. Through these contributions, Gu demonstrates a clear impact on improving robotic efficiency and accuracy, with his research laying groundwork for more intelligent and adaptable robotic systems in manufacturing and service applications.