Manimuthu Arunmozhi
Papers
4
Total Citations
106
H-Index
4
About
Manimuthu Arunmozhi is a leading researcher in autonomous reconfigurable robotics, with a focus on developing intelligent, energy-efficient systems for surface coverage tasks such as cleaning, harvesting, and painting. His major contributions center on Tetris-inspired, self-reconfigurable robots that can adapt their morphology for optimal area coverage. Arunmozhi pioneered the use of genetic algorithms inspired by the Traveling Salesman Problem for complete path planning, enabling these robots to navigate complex environments efficiently. He also developed a novel energy consumption estimation model that allows autonomous tiling devices to remain constantly aware of their power expenditure during deployment. His work extends to computer vision, where he proposed a three-layer filtering framework for visual dirt detection and an adaptive tiling scheme for selective area coverage, significantly improving cleaning efficiency. Additionally, Arunmozhi applied multi-criteria decision-making techniques to optimize tiling path planning, balancing lower energy consumption with greater area coverage. With his most-cited paper garnering 49 citations, his research has laid critical groundwork for practical, autonomous surface-tiling robots that are both intelligent and energy-aware, advancing the field toward truly self-sufficient domestic and industrial automation.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4