Combining particle filter algorithm with bio-inspired anemotaxis behavior: A smoke plume tracking method and its robotic experiment validation. (15th March 2020)

• Record Type: Journal Article
• Authors: Chen, Xinxing; Huang, Jian
• Is Part Of: Measurement. Volume 154(2020)
• Publication Date: 2020-03-15
• Subjects: Olfactory robot -- Particulate matter -- Firefly algorithm -- Particle filter -- Entropy; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2020.107482
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A smoke source localization robot was built. A particulate matter sensor was selected as the nose of the robot. An anemotaxis – particle filter based smoke plume tracking method was proposed. Abstract: The smoke source localization robot is a safe substitution of human and animal rescuers in many dangerous search and rescue mission. In this paper, we propose an anemotaxis – particle filter based smoke plume path tracking method and presented a smoke source localization robot. A modified firefly algorithm is applied in the resampling step of particle filter based smoke plume tracking method to mimic anemotaxis behaviors in the odor source searching process of living creatures. The performance of the proposed algorithm is qualitatively evaluated in a simulated wind tunnel environment in Webots, a high-fidelity robotic simulator. The average computation time including plume path estimation, resampling and target evaluation in every step is 35.9974 ms and the average smoke source localization error is 0.7769 m. We also verify the feasibility by running the proposed algorithm on the smoke source localization robot in real experiments.