Periodic inspections are required for the safe operation of large pressure vessels such as spherical tanks. Inspection robots have been applied in large pressure vessels due to their low cost and high efficiency. This paper presents a robotic system for the inspection of spherical tanks, which can identify and track weld lines on the shortest running route. Two-dimensional (2D) weld maps were prepared for robot path planning on the basis of the actual distribution of weld lines. In 2D weld maps, indispensable repetitive lines were added to form an Eulerian circuit that traversed all weld lines. In addition, an improved Fleury algorithm was proposed to solve Eulerian circuit and plan an optimal running route for robot inspection. To accurately identify weld lines, deep learning networks were constructed and trained with weld line data sets, which were captured by the camera mounted in the front of the robot.The laboratory experiments indicated that the inspection robot could identify weld lines within 0.2-0.25 s and track weld lines with a maximum offset of ±20 mm. The experiment results demonstrated that the robot could plan the shortest path to traverse all weld lines on the experimental platform. In the field tests, the virtual simulation of weld path planning on spherical tanks was explored in detail. The field tests of a spherical tank (3000 m 3 ) verified that the robotic system could improve the efficiency and stability of inspection operations and replace manual inspection with automated weld line recognition and weld path planning.

Since there are many interferences in the indoor environment, it is difficult to achieve the precise positioning of the mobile robot using a single sensor. This paper presents a position estimation and positioning error correction method of mobile robots based on multisensor data. The robot’s positioning sensor includes ultra-wideband (UWB) components, inertial measurement unit (IMU), and encoders. UWB multipath interference causes more ranging errors, which can be reduced by the correction equation after data fitting. The real-time coordinates of the UWB robot tag can be calculated based on multiple UWB anchor data and the least squares method. The coordinate data x c , y c are acquired by UWB positioning subsystem, and the velocity data x ̇ c , y ̇ c are collected by IMU together with encoders. The multisensor data continuously update Kalman filter and estimate robot position. In the positioning process, the positioning data of different sensors can be mutually corrected and supplemented. The results of UWB ranging correction experiments indicate that data fitting can improve the UWB positioning accuracy. In the multisensor positioning experiments, compared with a single sensor, the positioning method based on data fusion of UWB, IMU, and encoders has higher accuracy and adaptability. When UWB signals are interfered or invalid, other sensors can still work normally and complete the robot positioning process. The multisensor positioning method not only improves the robot positioning accuracy but also has stronger environmental adaptability.

Wall-climbing robots have been well-developed for storage tank inspection. This work presents a backstepping sliding-mode control (BSMC) strategy for the spatial trajectory tracking control of a wall-climbing robot, which is specially designed to inspect inside and outside of cylindrical storage tanks. The inspection robot is designed with four magnetic wheels, which are driven by two DC motors. In order to achieve an accurate spatial position of the robot, a multisensor-data-fusion positioning method is developed. The new control method is proposed with kinematics based on a cylindrical coordinate system as the robot is moving on a cylindrical surface. The main purpose is to promote a smooth and stable tracking performance during inspection tasks, under the consideration of the robot's kinematic constraints and the magnetic restrictions of the adhesion system. The simulation results indicate that the proposed sliding mode controller can quickly correct the errors and global asymptotic stability is achieved. The prototype experimental results further validate the advancement of the proposed method; the wall-climbing robot can track both longitudinal and horizontal spatial trajectories stably with high precision.

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