Nantiwat Pholdee
Papers
5
Total Citations
239
H-Index
5
About
Nantiwat Pholdee is a leading researcher in mechanical engineering optimization, with a primary focus on meta-heuristic algorithms and their application to robotics and structural design. His work centers on developing novel hybrid optimization techniques that combine physics-based and bio-inspired methods to solve complex, real-world engineering problems. Pholdee’s most impactful contribution is the robust design of a robot gripper mechanism using a new hybrid grasshopper optimization algorithm, which has garnered 124 citations and demonstrates his ability to enhance industrial robot control and design. He further advanced the field with a hybrid flow direction optimizer-dynamic oppositional based learning algorithm (HFDO-DOBL), cited 74 times, effectively tackling constrained mechanical design challenges like planetary gear trains and hydrostatic systems. His research also extends to multiobjective trajectory planning for 6D robots, where he employs self-adaptive and meta-heuristic approaches to optimize motion paths and minimize travel time. With over 200 total citations across his top papers, Pholdee’s work is recognized for bridging theoretical algorithm development with practical engineering solutions, making significant strides in robotics and mechanical design optimization.
Research Focus
Key Achievements
Top Papers
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- 3Self-adaptive MRPBIL-DE for 6D robot multiobjective trajectory planning27 citations · 2019
- 4
- 5Trajectory Planning of a 6D Robot based on Meta Heuristic Algorithms7 citations · 2018