Currently, the oil and gas pipeline network is a key link in the coordinated development of oil and gas upstream and downstream cohesion. To ensure the reliability and safety of oil and gas pipeline network operation, it is necessary to inspect the pipeline periodically to minimize the risk of leakage, spill and theft, as well as documenting actual incidents and the effects on the environment. Traditional manpower inspection is extremely labor-intensive and inefficient. Through the use of UAV (unmanned aerial vehicle) inspection, it is possible to greatly increase efficiencies by reducing the amount of manpower and resources required by traditional inspection methods.

The integrated optimization for UAV inspection path of oil and gas pipeline networks, including physical feasibility, performance of mission, cooperation, real-time implementation, three-dimensional (3-D) space, et al, is a strategic problem due to its large-scale and complexity. Aimed at improving inspection efficiency and maximizing economic benefits, this paper proposes a novel mix-integer linear programming model which could be used for inspection path planning. Minimizing the total inspection time is the objective function of this model. The constraints of the mission scenario and the safety performance of UAV are taken into account. By using evolutionary genetic algorithm, each candidate route can be measured through the evaluation function that takes into account the cost of the route, the mission scenario as well as the cooperative and coordinative requirements among the unmanned aerial vehicles constraints.

Finally, the proposed approach is applied to a virtual oil and gas pipeline network. Compared with the traditional inspection approach, the proposed method is 66.48% less in inspection cost and 22.07% shorter in total inspection time, verifying the rationality and superiority of the model.

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