التفاصيل البيبلوغرافية
| العنوان: |
Combining SLAM, GDM, and Anemotaxis for Gas Source Localization in Unknown and GPS-denied Environments. |
| المؤلفون: |
Soegiarto, Duddy, Trilaksono, Bambang Riyanto, Adiprawita, Widyawardana, Idris Hidayat, Egi Muhammad, Prabowo, Yaqub Aris |
| المصدر: |
International Journal on Electrical Engineering & Informatics; Sep2022, Vol. 14 Issue 3, p514-551, 38p |
| مصطلحات موضوعية: |
GAS distribution, AUTONOMOUS robots, GAS detectors, MOBILE robots, TURBULENT flow, GOAL programming |
| مستخلص: |
This paper discusses an autonomous robot navigation system for the gas source localization problem in an unknown and GPS-denied environment to find a single gas source transported in turbulent wind flow using mobile robot olfaction (MRO). A new autonomous navigation system method is proposed by combining simultaneous localization and mapping (SLAM), gas distribution mapping (GDM), and anemotaxis. We developed two destination evaluation algorithms for the navigation system, frontier-multi criteria decision making (MCDM) and Anemotaxis-GDM, to drive the robot towards a gas source. The frontier-MCDM uses to select destination candidates for gas contaminant discovery. The Anemotaxis-GDM is used for gas source tracking when the robot's gas sensor detects gas contamination. The GSL process ends after the declaration of the gas source. The simulation results for three different environments with one gas source validate the effectiveness of the frontier-MCDM and anemotaxis-GDM algorithms. The simulation results show that the frontier-MCDM algorithm has a 60% success rate in finding gas contamination compared to the FFD-IG_CL strategy (13.3%) and frontier-IG_CL strategy (20%). The evaluation of anemotaxis-GDM algorithm simulation results based on the average accuracy index indicates that the robot's final pose is close to the gas source. The average simulation success ratio is 71.1%. [ABSTRACT FROM AUTHOR] |
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