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 strong source and may fall short in comprehensively identifying all potential sources. DIAS addresses this gap by introducing a hybrid controller that identifies the presence of sources and then alternates between exploration for data gathering and exploitation for guiding robots to identified sources. It further enhances search efficiency by dividing the environment into Voronoi cells and approximating source density functions based on Gaussian process regression. Additionally, DIAS can be integrated with existing source seeking algorithms. We compare DIAS with existing algorithms, including DOSS and GMES in simulated gas leakage scenarios where the number of sources outnumbers or is equal to the number of robots. The numerical results show that DIAS outperforms the baseline methods in both the efficiency of source identification by the robots and the accuracy of the estimated environmental density function.

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 investigating the sample collection process via multirobot informative path planning. We present an approach for incorporating multi-robot informative path planning into a spatiotemporal adaptive sampling framework while considering path length constraints for sampling location selection. We also incorporate informative path planning to determine the best path to collect samples along while en route to collecting the desired sample. We achieve this in a decentralized manner by decoupling the process into two stages: the first stage uses our spatiotemporal mixture of Gaussian Processes (STMGP) model to determine the most informative sampling location via a mutual information lower bound heuristic and the second stage plans an informative path to collect the desired sample and other additional informative samples via submodular function optimization. Moreover, we effectively leverage peer-to-peer communication to enable coordination. Simulation results on real-world spatiotemporal data are provided to validate the effectiveness of our proposed approach.