P. Jagadeesh

About

P. Jagadeesh is a researcher whose work sits at the intersection of robotics, optimization algorithms, and intelligent scheduling systems. His most notable contributions focus on the development and application of metaheuristic optimization techniques — particularly the Electromagnetism-Like (EM) algorithm and Genetic Algorithms (GA) — to solve complex task scheduling problems in dual robot manipulator systems. His 2014 paper on optimizing task scheduling for dual manipulators stands as his most influential work, accumulating 13 citations, while his closely related 2013 study has garnered an additional 6 citations, reflecting consistent scholarly interest in his methodology. Jagadeesh's research addresses a critical challenge in industrial robotics: determining time-optimal task distributions between cooperating robotic arms. By hybridizing GA for near-optimal task scheduling with the EM algorithm as a refinement mechanism, he demonstrated a powerful combined approach that advances efficiency in multi-robot coordination. Though his citation profile is emerging, his specialized focus on bio-inspired and physics-based optimization for robotic applications provides a meaningful foundation for researchers exploring autonomous systems, manufacturing automation, and intelligent motion planning.

Research Focus

Key Achievements

Top Papers

1. 1
   Optimization of the Time of Task Scheduling for Dual Manipulators using a Modified Electromagnetism-Like Algorithm and Genetic Algorithm

   13 citations · 2014
2. 2
   Using Electromagnetism-like algorithm and genetic algorithm to optimize time of task scheduling for dual manipulators

   6 citations · 2013

Key Collaborators

Contact & Links