Airbone optical detection system for monitoring the methane gas leak is developed. The measuring principle, concentration equation, measurement device, airborne clamping apparatus and tracking pointing control system is studied.Firstly, the absorbing and measuring mechanism is designed based on the principle of near-infrared tunable diode laser absorption spectrum, using a second derivative wavelength modulation technique, the gas concentration is deduced. Then, the detailed description of measurement device, airborne clamping apparatus and control system is given. The device is mounted on low-moving platform, simulation of the gas line leak experiment shows that this method has high accuracy to tracking and pointing the detected gas line, the detection sensitivity of the system for the buried pipeline leakage is about 50 ppm*m (in the case of methane) and the tracking and pointing accuracy of the detection laser for the pipeline is 1°. This system can be applied into the airborne remote sensing detection for the long-distance natural gas leak.

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As the mileage of subway is increasing rapidly, there is an urgent need for automatic subway tunnel inspection equipment to ensure the efficiency and frequency of daily tunnel inspection. The subway tunnel environment is complex, it cannot receive GPS and other satellite signals, a variety of positioning sensors cannot be used. Besides, there are random interference, wheel and rail idling and creep. All the above results in poor performance of conventional speed tracking and positioning methods. In this paper, a multi-sensor motion control system is proposed for the subway tunnel inspection robot. At the same time, a trapezoidal speed planning and a speed tracking algorithm based on MPC (Model Predictive Control) are proposed, which simplify longitudinal dynamics model to overcome the complex and variable nonlinear problems in the operation of the maintenance robot. The optimal function of speed, acceleration and jerk constraint is designed to make the tunnel inspection robot achieve efficient and stable speed control in the subway tunnel environment. In this paper, the "INS (inertial navigation system) + Odometer" positioning method is proposed. The difference between the displacement measured by the inertial navigation system and the displacement calculated by the odometer is taken as the measurement value, which reduces the dimension of the conventional algorithm. The closed-loop Kalman filter is used to establish the combined positioning model, and the system error can be corrected in real time with higher accuracy. The algorithms were verified on the test line. The displacement target was set to be 1 km and the limit speed was 60 km/h. The overshooting error of the speed tracking algorithm based on trapezoidal velocity planning and MPC was 0.89%, and the stability error was 0.32%. It improved the accuracy and stability of the speed following, and was much better than the PID speed tracking algorithm. At the speed of 40 km/h, the maximum positioning error of the robot within 2 km is 0.15%, and the average error is 0.08%. It is verified that the multi-sensor fusion positioning algorithm has significantly improved the accuracy compared with the single-odometer positioning algorithm, and can effectively make up for the position error caused by wheel-rail creep and sensor error.

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