Papers
2
Total Citations
8
H-Index
2
About
S. Manikandan’s research sits at the intersection of autonomous robotics and intelligent data analysis, with a focus on making real-world systems more adaptive and responsive. In their most cited work, “Tour Planning Design for Mobile Robots Using Pruned Adaptive Resonance Theory Networks” (2021, 6 citations), Manikandan tackles a critical bottleneck in autonomous navigation: the inability of conventional algorithms to anticipate unexpected obstacles during tour path planning. By integrating pruned adaptive resonance theory networks, they developed a more robust framework for real-time mobile robot control, enhancing both safety and efficiency in dynamic environments. This contribution is particularly relevant for applications in logistics, surveillance, and service robotics. Beyond robotics, Manikandan has also explored the domain of user feedback analysis in “Review of Feedback Analysis of Business Process Outsourcing” (2023, 2 citations), addressing the challenge of extracting meaningful requirements from large volumes of online user reviews—a task often hindered by manual analysis. While still early in their career, Manikandan’s work demonstrates a clear commitment to bridging theory and practice, with a growing citation footprint that signals increasing recognition in both the robotics and business process optimization communities.
Research Focus
Key Achievements
Top Papers
- 1
- 2Review of Feedback Analysis of Business Process Outsourcing2 citations · 2023