Odor source localization (OSL) using mobile robots in indoor ventilated environments remains challenging due to turbulent dispersion, uneven concentration distribution, and weak robustness in conventional algorithms. This paper proposes an efficient OSL strategy for wheeled mobile robots by integrating time-varying smoke plume modeling, particle filtering (PF), and information entropy. A multi-sensor fusion perception system is developed, including an LDS-02 LiDAR, ultrasonic anemometer, and PMS5003 particle sensor. The proposed method employs a plume model to characterize odor particle propagation, uses particle filtering to estimate the posterior distribution of the source location, and introduces information entropy to quantify perceptual uncertainty and optimize robot path planning. Comparative simulations and real-world experiments are conducted in a 5 m × 3 m indoor ventilated environment against the traditional gradient–bionic hybrid algorithm. Results demonstrate that the proposed algorithm significantly reduces the average search time and improves the localization success rate. The long-distance localization success rate exceeds 90%, and the positioning error is controlled within 0.5 m. The proposed strategy provides a reliable and practical solution for OSL in indoor ventilation environments.
Ye et al. (Mon,) studied this question.