Reinforcement Learning-based Dynamic Field Exploration and Reconstruction Using Multi-Robot Systems for Environmental Monitoring

Thinh Lu Divyam Sobti Deepak Talwar Wencen Wu ^*

• San Jose State University, San Jose, United States

In the realm of real-time environmental monitoring and hazard detection, multi-robot systems present a promising solution for mapping and exploring dynamic fields, particularly where human intervention poses safety risks. This research introduces a strategy for path planning and control of a group of mobile sensing robots to efficiently explore and reconstruct a dynamic field consisting of multiple non-overlapping diffusion sources. Our approach integrates a reinforcement learningbased path planning algorithm to guide the multi-robot formation in identifying diffusion sources, with a clustering-based method for destination selection for the multi-robot formation once a new source is detected, to enhance coverage and accelerate exploration in unknown environments.Simulation results and real-world laboratory tests demonstrate the effectiveness of our approach in mapping and reconstructing the dynamic field. This study advances the field of multi-robot systems in environmental monitoring and has practical implications for rescue missions and field explorations.