Srujan Deolasee — accepted AI-conference papers

2025

Distributed Multi-Robot Source Seeking in Unknown Environments with Unknown Number of Sources

Lingpeng Chen, Siva Kailas, Srujan Deolasee, Wenhao Luo, Katia Sycara, Woojun Kim

ICRA 2025

We introduce a novel distributed source seeking framework, DIAS, designed for multi-robot systems in scenarios where the number of sources is unknown and potentially exceeds the number of robots. Traditional robotic source seeking methods typically focused on directing each robot to a specific stron

Cited by 1 Source ↗Scholar ↗

2025

Integrating Multi-Robot Adaptive Sampling and Informative Path Planning for Spatiotemporal Natural Environment Prediction

Siva Kailas, Srujan Deolasee, Wenhao Luo, Woojun Kim, Katia P. Sycara

ICRA 2025

Learning to predict spatiotemporal (ST) environmental processes from a sparse set of samples collected autonomously is a difficult task from both a sampling perspective (collecting the best sparse samples) and from a learning perspective (predicting the next timestep). In this work, we focus on inve

Cited by 3 Source ↗Scholar ↗