Driving Behavior Decision-Making for Unmanned Monorail Cranes Based on Multisource Layering Perception Information

Monorail cranes (MCs) are the emerging transportation in the downhole and often work in dangerous deep confined spaces. To improve the safety production efficiency, a driving behavior decision-making method is proposed for unmanned MC. First, a multisource perception data layering method is presented. Based on the results of sensor performance testing in the downhole, an onboard perceptual platform is designed, and the sensor information are layered and integrated in a structured manner, considering the significance of different sensor information on safe driving. Second, a driving behavior decision-making framework for unmanned MCs is proposed. The decision-making objective and constraints are derived from the coal mine safety regulations, the MC driving manual, and the downhole testing results. This framework logic is novel in unmanned MC, which can achieve downhole driving by combining rule-based driving behavior decision-making and reactive one. The experiments in the heading area and the upper yard show that the proposed method increases the perception distance by $3.62\times $ compared with that of remote driving and reduces the decision-making response time by 96%. MC has enough distance and time to slow down first until it has to brake. In contrast, this MC driving behavior is more stable and safe in the downhole.