AUTONOMOUS ROBOT NAVIGATION SYSTEM WITH COMBINATION OF SLAM, GDM AND ANEMOTAXIS FOR GAS SOURCE LOCALIZATION

• Main Author: Soegiarto, Duddy
• Format: Dissertations
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/69903
• Institution: Institut Teknologi Bandung

Disaster management is hazardous for humans, especially when measuring and sampling disaster data. Remote data measurement and sampling are needed to reduce risks and hazards. One solution that can be done is the use of moving robots to replace the human role in these activities. Research on mobile robots that detect hazardous chemicals (olfactory sensing capabilities), and hazardous gases, continues to grow. The field of research on robots engaged in this field is called Mobile Robot Olfaction (MRO). The robot can provide real-time data on gas concentrations and contaminated environmental conditions. Localization of gas sources or Gas source localization (GSL) is one of the studies in MRO. Localization of gas sources means finding the position of sources around the environment through the distribution of gas carried by the wind. Research on the GSL problem in an unknown environment is still very open. This dissertation research aims to find a single gas source using a single robot and multiple robots in an unknown environment. A robotic autonomous navigation system for GSL is proposed by combining simultaneous localization and mapping (SLAM), gas distribution mapping (GDM) and Anemotaxis. The research developed an evaluation algorithm to determine the exploratory destination points: Frontier-multi-criteria decision-making (MCDM) and Anemotaxis-GDM. Both algorithms aim to move the robot effectively towards the gas source. The robot uses Frontier-MCDM to select candidate destinations to find gas-contaminated areas. Anemotaxis-GDM is used for tracking gas sources when robotic gas sensors detect gas concentrations. Research on tracing gas sources by combining the anemotaxis and SLAM-GDM (Anemotaxis-GDM) methods has yet to be carried out, so it is an opportunity to contribute to this dissertation research. Anemotaxis-GDM ensures that the robot moves in areas with high gas concentrations to estimate the location of gas sources better. The task allocation method was developed to support multi-robot collaboration in GSL applications. A combination of the Frontier-MCDM algorithm and PROMETHEE II-based task allocation is used to delegate exploration tasks to each robot. This method aims to select target candidates by involving data from each robot, dividing exploration tasks and carrying out the coordination process. Several simulations and tests in the iv natural environment were conducted to test and validate all algorithms. The test results validate the effectiveness of the Frontier-MCDM, Anemotaxis- GDM and PROMETHEE II-based task allocation algorithms.