Smoothing Algorithm Using Censor Distance on KRSRI ITN Malang Robot with Moving Average Method
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Abstract
Distance sensors play a crucial role in robotic navigation systems, particularly in the Indonesian Search and Rescue Robot Contest (KRSRI). However, sensor readings often suffer from instability due to external disturbances such as robot leg movements and environmental interference. This research aims to address these issues by implementing a smoothing algorithm based on the Simple Moving Average (SMA) method to refine distance sensor readings from the GP2Y Infrared Proximity sensor, enabling the robot to detect obstacles more accurately. Experiments were conducted under various conditions, both with and without the smoothing algorithm, showing that the use of SMA significantly improves the stability and accuracy of sensor data. The results indicate that sensor readings with SMA exhibited minimal fluctuations, maintaining higher consistency around the actual measured distance, while readings without SMA showed significant variability and inaccuracies. The implementation of SMA successfully reduced measurement errors, with an average error reduction of 97.68% compared to the raw sensor data. This improvement ensures more reliable obstacle detection and navigation performance, thereby enhancing the robot’s effectiveness in competitions as well as search and rescue applications in real-world environments.
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