Research Interests
- Artificial Intelligence
- Machine Learning
- Deep Reinforcement Learning
- Multi-Agent Systems
- Evolutionary Algorithms
- Swarm Robotics
- Swarm Intelligence
Current Research / Ongoing Projects
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Decentralized Coordination in Multi-Agent Systems using Deep Reinforcement Learning
My doctoral research focuses on developing scalable, adaptive, and decentralized decision making for coordinating multiple autonomous agents in complex environments. The primary application domains include UAV-based Search and Rescue (SAR) missions, defense automation, and resource-aware path planning. -
Flocking-Based Experiment for Decentralized Training
Inspired by natural swarms, this research explores how agents can learn coordinated behaviors like flocking using decentralized training paradigms. It emphasizes independent learning without centralized control, aiming to understand the scalability and robustness of such approaches in large-agent systems. -
LLM-Integrated Maze Problem Solving
This project investigates how Large Language Models can be integrated into a reinforcement learning framework to assist agents in solving maze environments. The objective is to combine spatial reasoning and visual pathfinding to improve generalization across unseen maze topologies.
Past Research / Mini-Projects
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Couzin's Model for Flocking Behavior
Implemented and analyzed the Couzin model to simulate emergent flocking behavior in autonomous agents. The project studied how local interaction rules among agents give rise to collective motion patterns in dynamic environments. -
Collective Decision-Making in Swarms
Explored decentralized decision-making mechanisms in swarm systems using models like the majority rule and voter model. The study evaluated how consensus is achieved in noisy and uncertain environments through distributed interactions. -
Multi-Robot Path Planning for Area Coverage in Complex Environments
Proposed an improved approach for multi-robot coverage path planning (mCPP) using A* and spanning tree coverage algorithms. The method efficiently partitions the workspace among robots and plans optimal single-robot paths to ensure complete coverage, even in environments with obstacles. -
Comparative Study on Grid-Based and Non-Grid-Based Path Planning Algorithms
Conducted a comparative analysis of sampling-based path planning techniques including A*, PRM and RRT, with a focus on the effectiveness of grid-based sampling in reducing computational complexity while maintaining exploration efficiency. -
Modeling and Prediction of COVID-19 in India Using Machine Learning
Developed a forecasting framework using non-linear regression, recurrent neural networks, and artificial neural networks to predict COVID-19 spread in India. The proposed model demonstrated improved long-term prediction accuracy and offered insights into the impact of public health interventions.
Collaborations & Mentoring
I actively collaborate with researchers in AI, robotics, and optimization. I also mentor undergraduate and graduate students on research projects in swarm intelligence and reinforcement learning.