Ananya Gandhi

I have done my BS-MS in the Department of Electrical Engineering and Computer Science at the Indian Institute of Science Education and Research (IISER), Bhopal, graduating in 2025. I am the recipient of the Kanako Miura Travel Award and have presented my work at the IEEE Humanoids 2024 Conference held at Nancy, France.

My research focuses on Swarm Robotics, Multi Agent Reinforcement Learning, Decentralised Systems and Artificial Intelligence. I have presented my research on Moving Depot (MOD): An Efficient Depot Motion Strategy for Multi-Robot Foraging at the ANTS 2024 International Conference.

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I graduated as a Master's student in the BS-MS dual degree program at the Indian Institute of Science Education and Research (IISER) Bhopal, where I was a member of the Multi Robot Autonomy Lab (MOON Lab). I was advised by Prof. Sujit PB. My major was Electrical Engineering and Computer Science. And I hold a minor in Data Science Engineering. My academic interests include robotic swarm intelligence, multi-agent reinforcement learning, computational social choices, computer vision, and quantitative finance.

My current research is centered on the development of an advanced algorithm for drone swarming behavior, aimed at enhancing the collective dynamics and coordination of multiple drones operating in a shared environment. The research focuses on creating reward and penalty mechanisms to ensure optimal swarm movement and functionality.

I have presented research at international conferences and received awards like the Kanako Miura Travel Award (2024) and Special Mentions at INSEF (2017). Additionally, I have established a Quantitative Finance Club at IISERB. Prior to IISER Bhopal, I completed my junior college education in Pune, Maharashtra, where I was the College Gold-Medalist in Computer Science in the Maharashtra State Board for +2 Education.

To pursue my interests in swarm robotics, I have spent time as a research intern at:

This paper introduces a novel Moving Depot (MOD) strategy for multi-robot foraging, inspired by adaptive behaviors in insect colonies. Unlike traditional stationary depot strategies (SDS), MOD focuses on dynamic depot repositioning to maximize foraging efficiency in environments with obstacles and changing resource distributions. The results demonstrate enhanced adaptability and efficiency in dynamic conditions compared to existing methods.

Template credits: Rachneet Kaur