Papers
1
Total Citations
24
H-Index
1
About
Pu Wu is a leading researcher in robotics and intelligent optimization, with a primary focus on the dynamic performance and trajectory planning of parallel manipulators. His most influential work, "Optimal Time–Jerk Trajectory Planning for Delta Parallel Robot Based on Improved Butterfly Optimization Algorithm" (2022, 24 citations), introduces a novel multi-objective integrated trajectory planning method that significantly enhances the high-speed pick-and-place capabilities of Delta parallel robots. By developing an improved butterfly optimization algorithm (IBOA), Wu addresses the critical trade-off between motion time and jerk, directly improving robot smoothness, precision, and operational lifespan. This contribution is pivotal for industries relying on rapid, repetitive automation, such as packaging and electronics assembly. Wu’s research stands out for its practical engineering impact, offering a computationally efficient solution to a longstanding challenge in robotics. His work not only advances the theoretical foundations of trajectory optimization but also provides a scalable framework for real-world robotic systems, marking him as an innovator at the intersection of bio-inspired algorithms and mechanical design.
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