An Adaptive Robot Search Algorithm for Balancing Exploitation and Exploration in Indoor Intermittent Source Seeking

The accidental release of chemicals poses significant risks to human life and property, requiring rapid and accurate source seeking. Chemicals released from an intermittent indoor source form narrow, dynamic, and discrete concentration patches, leaving robots with intermittent cues and posing challenges for source seeking. This study proposed an adaptive robot search algorithm that balances exploitation of historical data and exploration of unknown areas. The proposed exploitation strategy utilizes historical data to probabilistically estimate the source location, aiming to guide the robot upstream for faster source localization. The proposed exploration strategy directs the robot to unknown areas, aiming to gather more information by prioritizing points with the highest information gain based on frontier evaluation. A novel time-dependent factor reduces repeated visits to early regions while mitigating exploration bias in later search stages, enabling a dynamic balance between the two strategies when selecting navigation goals. The algorithm was tested in simulated environments with varying airflow speeds, source release cycles, source release duty cycles, and different scenarios. The results demonstrated reliable and excellent performance. The effectiveness of the proposed algorithm was further validated in real-world robot experiments.