Papers
16
Total Citations
270
H-Index
9
About
Chinmaya Sahu is a robotics researcher whose work centers on autonomous navigation, path planning, and motion control of humanoid robots. His most significant contributions lie in developing intelligent navigational controllers that harness bio-inspired and computational optimization techniques to guide humanoid robots through complex, cluttered environments. Sahu has pioneered hybrid approaches that combine methods such as adaptive ant colony optimization, adaptive particle swarm optimization, regression analysis, and fuzzy logic-embedded neural networks, consistently demonstrating improved navigational efficiency over conventional algorithms. His 2018 paper on a hybridized regression-adaptive ant colony optimization approach stands as his most influential work, amassing 65 citations, while a companion study on adaptive ant colony optimization for humanoid path planning attracted an additional 37 citations. Together, these works established Sahu as a key voice in bio-inspired robotics navigation. His 2022 contributions extended this trajectory into fuzzy-neural architectures and PID-based stabilization, reflecting a broadening research vision. With a portfolio spanning static and dynamic path planning, multi-robot coordination, and self-fabricated biped systems, Sahu's cumulative impact — over 240 citations — underscores his sustained relevance to the robotics research community.
Research Focus
Key Achievements
Top Papers
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- 7A hybridized RA-APSO approach for humanoid navigation18 citations · 2017
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