Pengju Yang
Papers
2
Total Citations
9
H-Index
2
About
Pengju Yang is a robotics researcher focused on advancing the motion planning and control of robotic manipulators in complex, obstacle-rich environments. His primary research areas include inverse kinematics, collision detection, and obstacle-avoidance algorithms for industrial and service robots. Yang’s major contributions lie in developing computationally efficient methods that enable manipulators to navigate cluttered spaces, such as overhead multi-line environments, where traditional approaches struggle. His 2024 paper on an obstacle-avoidance inverse kinematics method has garnered 5 citations, while his work on fast collision detection using point clouds and stretched primitives has earned 4 citations, reflecting early but growing impact in the field. These studies address critical gaps by balancing accuracy and speed when dealing with non-convex obstacles and complex link geometries. Yang’s achievements include proposing novel frameworks that integrate point cloud data with simplified geometric primitives, significantly reducing computational overhead without sacrificing safety. His research is particularly relevant for autonomous systems in manufacturing, logistics, and inspection, where real-time obstacle avoidance is essential. As a rising scholar, Pengju Yang is contributing practical solutions to one of robotics’ most persistent challenges: enabling dexterous manipulation in unpredictable environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2