Path Optimization of UAV Inspection Suspended Track Based on Dynamic Adaptive Ant Colony Optimization

The permanent magnet maglev technology rail transit system, also known as "Redrail," is a new type of suspended aerial rail system. The traditional manual method has long inspection cycle, high cost, many blind spots, low security, and other disadvantages when it used for rail inspection because the rail is suspended in the air. A method of using an unmanned aerial vehicle (UAV) to automatically inspect the "Redrail" track is proposed to address the above problems. A mathematical model of the inspection path problem is constructed, and a dynamic adaptive ant colony optimization (DAACO) is proposed to solve the shortest inspection path. Seventeen instances are selected as monitoring point data, and the simulation test is conducted. Simulation results show that the solution quality of the shortest inspection path solved by the proposed algorithm is better, and the dispersion degree is smaller. Finally, the proposed algorithm is used to plan the inspection path, and the ground station is used to control the UAV for actual flight test. Test results show that the UAV successfully completes the inspection task in accordance with the inspection path planned by the proposed algorithm, which greatly improves its inspection efficiency compared with the manual inspection method and verifies its effectiveness.