Inspection robots have been developed to support the maintenance of separator vessels. One challenge for such robots is to identify and navigate along the weld bead. This paper proposes a new solution to the weld bead recognition problem, including its tracking, which aims to automate weld bead inspection. A reliable weld bead detection method was developed through five steps that process the line profile sensor data. The weld bead was identified based on the estimation of its center. A non-linear controller paired with a module that compensates for uncertainties generated by gravity was designed to ensure the tracking of weld bead. Such a controller can reduce the impact of the variation in the robot's orientation on the weld bead tracking. An algorithm redirects the robot at the end of the weld bead. The experiments were conducted in a laboratory-scale setup, allowing movement along horizontal and diagonal surfaces. The results show that the robot tracked the weld line in all simulated conditions, with a maximum error of 2.86 mm.

This work discusses a novel human–robot interface for a climbing robot for inspecting weld beads in storage tanks in the petrochemical industry. The approach aims to adapt robot autonomy in terms of the operator’s experience, where a remote industrial joystick works in conjunction with an electromyographic armband as inputs. This armband is worn on the forearm and can detect gestures from the operator and rotation angles from the arm. Information from the industrial joystick and the armband are used to control the robot via a Fuzzy controller. The controller works with sliding autonomy (using as inputs data from the angular velocity of the industrial controller, electromyography reading, weld bead position in the storage tank, and rotation angles executed by the operator’s arm) to generate a system capable of recognition of the operator’s skill and correction of mistakes from the operator in operating time. The output from the Fuzzy controller is the level of autonomy to be used by the robot. The levels implemented are Manual (operator controls the angular and linear velocities of the robot); Shared (speeds are shared between the operator and the autonomous system); Supervisory (robot controls the angular velocity to stay in the weld bead, and the operator controls the linear velocity); Autonomous (the operator defines endpoint and the robot controls both linear and angular velocities). These autonomy levels, along with the proposed sliding autonomy, are then analyzed through robot experiments in a simulated environment, showing each of these modes’ purposes. The proposed approach is evaluated in virtual industrial scenarios through real distinct operators.

This article presents a novel strategy for an autonomous inspection robot for oil industry tanks. The goal is to identify and track the external weld beads of a tank by an inspection robot. The chosen robot is an autonomous climbing robot with differential magnetic wheels. The detection of the weld bead is made with a line profile sensor. An algorithm is proposed to identify the weld bead position from the robot. The control is designed with the Fuzzy technique. The approach is evaluated on a real plate and a cylindrical tank virtually designed with the same characteristics that real tank projects.

AIR is a climbing robot designed for nondestructive testing and inspection of weld seams in tanks and vessels of the oil and gas industry. In this paper a Fuzzy control system is proposed to a optimal selection of Levels of Autonomy (LoA) at each moment, mixing joystick inputs from an operator and sensor information from the environment to ensure that the movement of tracking weld seams is being achieved while allowing the operator to have full control of the robot's movements without having to control each maneuver manually. Experiments were executed in a simulator environment and are presented along with future ideas for research.