Manuka Suriyage
Papers
1
Total Citations
8
H-Index
1
About
Manuka Suriyage is a robotics and automation researcher whose work focuses on optimizing industrial robotic systems for enhanced efficiency and precision. His primary research areas include delta robot kinematics, path planning, and computer vision integration for material handling applications. Suriyage's most notable contribution is his development of a genetic algorithm-based approach for pick-and-place sequence optimization in color and size sorting delta robots, published in 2020. This work, which has garnered 8 citations, introduces a novel methodology that identifies the optimal path in task space for industry-emulated sorting scenarios, significantly improving operational throughput. By integrating OpenCV-Python programming for real-time visual recognition, his research bridges the gap between computer vision and robotic manipulation, enabling robots to dynamically adapt to varying object characteristics. This achievement demonstrates his ability to combine evolutionary computation with practical industrial challenges, offering scalable solutions for lightweight material handling. Suriyage's work holds particular relevance for manufacturing sectors seeking to automate complex sorting tasks, and his methodology serves as a foundation for further advancements in adaptive robotic systems.
Research Focus
Key Achievements
Top Papers
- 1