Max Robotics — Global Service Robot Directory + US Certification

About

Kanak Kalita is a prominent researcher specializing in multi-criteria decision making (MCDM), robotics, and industrial automation, with a particular focus on developing systematic frameworks for optimal robot selection and performance optimization. His work addresses a critical challenge in modern manufacturing: helping industries navigate the complex landscape of robotic systems to make informed, data-driven decisions. Kalita's most impactful contributions include pioneering the application of advanced MCDM methodologies to spray painting robot selection, most notably his SWARA-CoCoSo approach (84 citations) and his MEREC-based framework (80 citations), which together represent foundational tools for evaluating and selecting industrial robots in hazardous manufacturing environments. His comprehensive comparative study of MCDM models for industrial robot selection (27 citations) further demonstrates his commitment to rigorous, systematic analysis across competing decision frameworks. Beyond selection methodologies, Kalita has contributed meaningfully to robot safety and performance, developing novel optimization approaches such as the NSWOA-MARCOS method for variable stiffness joints in manipulators, addressing the growing need for safe human-robot collaboration. With over 210 cumulative citations, his research has established him as a valuable voice in the intersection of intelligent decision systems and industrial robotics, offering practical tools that directly benefit engineers and manufacturing professionals worldwide.