Three-Dimensional Metal Pipe Detection for Autonomous Excavators Using Inexpensive Magnetometer Sensors

Excavators are one of the several machines that play a vital role in the construction sector. The excavators' main task is to dig the appropriate shapes in the Earth, which may cause severe damage to subsurface infrastructure. There are numerous existing technologies to avoid this. However, they are either too expensive or too time-consuming to use. In this research, two affordable magnetometer sensors mounted to the bucket of an autonomous excavator were used to scan the digging area and find metallic pipelines and electricity-carrying cables underground. For this purpose, some theoretical methodologies, as well as AI-based ones, were applied, and their performances were compared. In this study, the researchers used a combination of derived data, mathematical formulas, and the neural network method to acquire information about underground pipes. The results obtained from this approach demonstrate a close resemblance to actual pipe size and orientation. The implications of this research are significant for the excavation industry, as it provides a higher level of certainty when dealing with underground facilities. These findings can help excavation operations become more cost-effective and time-saving, thereby improving overall efficiency.