Mohak Sukhwani

Indian Institute of Technology Hyderabad, ABB (India), ABB (Switzerland)

Papers

Total Citations

H-Index

About

Mohak Sukhwani’s research bridges precision agriculture and intelligent robotics, with a focus on making autonomous systems both productive and safe. His most influential work, “Plantation monitoring and yield estimation using autonomous quadcopter for precision agriculture” (2016, 52 citations), introduced a supervised learning framework that enables quadcopters to autonomously monitor plantations and estimate crop yields—a foundational contribution to modern precision agriculture. Building on this, Sukhwani has advanced collaborative robotics by proposing safety-aware controllers for human-robot shared workspaces (2019) and developing dynamic knowledge graphs as semantic memory models for industrial robots (2021). These latter works allow machines to accumulate and reason over experiential data, enhancing their proficiency over time. His research uniquely combines practical deployment (autonomous navigation, sensor integration) with cognitive architectures (knowledge graphs, semantic memory), addressing both immediate agricultural challenges and long-term robot autonomy. With a citation trajectory that underscores growing interest in his integrated approach, Sukhwani’s work is shaping how robots perceive, learn, and safely interact in complex, real-world environments—from farms to factories.