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Journal : Journal of Robotics and Control (JRC)

Distance-based Indoor Localization using Empirical Path Loss Model and RSSI in Wireless Sensor Networks Dwi Joko Suroso; Muhammad Arifin; Panarat Cherntanomwong
Journal of Robotics and Control (JRC) Vol 1, No 6 (2020): November
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.1638


Wireless sensor networks (WSNs) have a vital role in indoor localization development. As today, there are more demands in location-based service (LBS), mainly indoor environments, which put the researches on indoor localization massive attention. As the global-positioning-system (GPS) is unreliable indoor, some methods in WSNs-based indoor localization have been developed. Path loss model-based can be useful for providing the power-distance relationship the distance-based indoor localization. Received signal strength indicator (RSSI) has been commonly utilized and proven to be a reliable yet straightforward metric in the distance-based method. We face issues related to the complexity of indoor localization to be deployed in a real situation. Hence, it motivates us to propose a simple yet having acceptable accuracy results. In this research, we applied the standard distance-based methods, which are is trilateration and min-max or bounding box algorithm. We used the RSSI values as the localization parameter from the ZigBee standard. We utilized the general path loss model to estimate the traveling distance between the transmitter (TX) and receiver (RX) based on the RSSI values. We conducted measurements in a simple indoor lobby environment to validate the performance of our proposed localization system. The results show that the min-max algorithm performs better accuracy compared to the trilateration, which yields an error distance of up to 3m.  By these results, we conclude that the distance-based method using ZigBee standard working on 2.4 GHz center frequency can be reliable in the range of 1-3m. This small range is affected by the existence of interference objects (IOs) lead to signal multipath, causing the unreliability of RSSI values. These results can be the first step for building the indoor localization system, which low-cost, low-complexity, and can be applied in many fields, especially indoor robots and small devices in internet-of-things (IoT) world’s today.
A Simulation-Based Study of Maze-Solving-Robot Navigation for Educational Purposes Ismu Rijal Fahmi; Dwi Joko Suroso
Journal of Robotics and Control (JRC) Vol 3, No 1 (2022): January
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v3i1.12241


The point of education in the early stage of studying robotics is understanding its basic principles joyfully. Therefore, this paper creates a simulation program of indoor navigations using an open-source code in Python to make navigation and control algorithms easier and more attractive to understand and develop. We propose the maze-solving-robot simulation as a teaching medium in class to help students imagine and connect the robot theory to its actual movement. The simulation code is built for free to learn, improve, and extend in robotics courses or assignments. A maze-solving robot study case is then done as an example of implementing navigation algorithms. Five algorithms are compared, such as Random Mouse, Wall Follower, Pledge, Tremaux, and Dead-End Filling. Each algorithm is simulated a hundred times in every type of the proposed mazes, namely mazes with dead ends, loops only, and both dead ends and loops. The observed indicators of the algorithms are the success rate of the robots reaching the finish lines and the number of steps taken. The simulation results show that each algorithm has different characteristics that should be considered before being chosen. The recommendation of when-to-use the algorithms is discussed in this paper as an example of the output simulation analysis for studying robotics.