<![CDATA[Indonesia shares borders with Papua New Guinea, Malaysia, and Timor Leste, where border markers often face displacement or disputes due to challenging and inaccessible terrain. This research develops a waypoint navigation system on a quadcopter, integrating computer vision to enhance the detection and monitoring of border markers. The system leverages the Robot Operating System (ROS) as middleware for seamless integration and control, while a camera detects ArUco markers placed on boundary markers. Image processing, implemented using OpenCV integrated with ROS, facilitates efficient data conversion. The quadcopter autonomously navigates to target coordinates based on marker detection, with an average percentage error of 3.3% for the X-axis and 2.5% for the Y-axis. Tests showed the system could detect a 40x40 cm marker from a height of 5 meters up to a distance of 14 meters, with an average position error of 3.75%. The communication range was effective up to 150 meters before timing out. Despite the computational limitations of the Raspberry Pi hardware, the system demonstrated efficiency, scalability, and ease of deployment. Future research will focus on hardware enhancements, the exploration of advanced image processing methodologies, improved camera resolutions, and the extension of communication networks to support deployment in national boundary monitoring operations.]]>
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