Arpita Khuntia

Papers

Total Citations

H-Index

About

Arpita Khuntia’s research lies at the intersection of robotics, neural networks, and soft computing, with a focus on solving complex motion planning and coordination problems. Her most cited work, “A neural network based inverse kinematic problem” (2011, 21 citations), addresses the challenge of computing joint configurations for robotic manipulators—a problem that becomes increasingly complex as the number of joints grows. By proposing a neural network-based solution, she offered a more efficient alternative to traditional analytical methods, enabling smoother and more adaptable robot control. In parallel, Khuntia has made significant contributions to multi-robot task allocation. Her 2011 paper (10 citations) introduces a genetic algorithm (GA) approach for assigning tasks to non-homogeneous robots, accounting for each robot’s unique capabilities and the spatial-temporal demands of tasks. She extended this work in 2012 (5 citations) by integrating soft computing techniques to further optimize task distribution in heterogeneous multi-robot systems. Together, these contributions demonstrate Khuntia’s ability to blend biologically inspired computation with practical robotics challenges, laying groundwork for more autonomous and efficient robotic teams in industrial and research settings.