The implementation of the K-nearest neighbor algorithm to detect the KRSRI robot obstacles

Abstract

The Indonesian SAR robot contest (KRSRI) is a development of the fire extinguisher robot contest (KRPAI); initially, the robot at KRPAI only put out fires. Still, at KRSRI, the robot was asked to prioritize the SAR function. The robot had to overcome obstacles in this contest to complete it. Based on this, an obstacle detection system for the robot was designed using machine learning with the K-nearest neighbor algorithm and gray level co-occurrence matrix feature extraction. Later, the robot is expected to be able to carry out accurate obstacle detection to prioritize efficiency so that no more time is consumed due to the robot incorrectly detecting an obstacle. The results of the tests that have been carried out show that the detection accuracy based on the test dataset is 80% for rising barriers, 100% for debris obstacles, and 90% for step obstacles, and an error value of 20% for increasing obstacles is obtained, 0% for debris obstacles, and 10% for stair obstacles.