In large-scale shipbuilding, welding tasks represent a significant portion of all tasks, requiring automated robot operation. However, current welding robots are not automated for high and narrow spaces because they cannot pull heavy welding cables, causing deviations from the intended path and reducing the welding accuracy. This paper proposes a cable-towing stabilization method considering factors such as the self-weight of multiple vehicles, magnetic adhesion force, and force necessary to hold the cables. The proposed approach integrates welding robots, which perform welding tasks, with towing robots, which alleviate the load imposed by welding cables and wire feeders. Cable-towing on walls requires reducing excessive distances between vehicles, as well as their excessive acceleration, in addition to maintaining the mechanical stability of each vehicle. Therefore, the optimal positions and postures of the towing vehicles are sequentially calculated using an optimization problem. The proposed method was evaluated through simulations and real-world experiments, confirming stable cabletowing on a wall surface of approximately 3 × 1.5 m. The findings of this research enhance the safety and efficiency of managing deformable linear objects with robots, focusing on mechanical safety while expanding the operational range from single-vehicle wall-mounted operations to cooperative multi-vehicle wall-mounted tasks, thereby increasing applicability to various wall-towing scenarios.

This paper presents a wheeled wall-climbing robot with a shape-adaptive magnetic adhesion mechanism for large steel structures. To travel up and down various curved ferromagnetic surfaces, we developed a 2 DOF rotational magnetic adhesion mechanism installed on each wheel that can change the orientation of the magnets to keep the magnetic force direction always normal to the contact surface. These magnetic wheels have a spherical shape and can move relative to the main body by a non-elastic suspension mechanism so that the robot can climb up small obstacles on the ground and find contact points for each wheel on a wall with an arbitrary curved shape. Being geometrically stable is important for the robot because this robot is intended to be a mobile base for a welding manipulator. The detailed design of the mechanism and the results of climbing tests are presented.

An intuitive on-site robot programming method for small-lot robotic welding is presented. In current robotic welding, a human operator has to input numerous parameters, including feedrate, swing width, and frequency, by using a teach pendant or a control panel before executing the task. This traditional approach is suitable for mass production, but requires tedious, time-consuming programming, which does not fit low-volume manufacturing, such as shipbuilding. In this paper, a method is developed for acquiring those parameters directly from an on-site human demonstration and seamlessly transitioning from manual operation to automatic control. With this method, a welding worker can directly execute a welding task, and the motion of a welding torch is observed, from which key parameters are identified and the machine performs the rest of the task autonomously. No tedious parameter input is required, but the worker can jump-start the task. The motion of a welding torch is represented as a combination of sinusoidal and linear functions. Discrete Fourier Transform (DFT) and Recursive Least Squares (RLS) estimates are used for identifying the parametric model in real time. Furthermore, an algorithm is developed for determining whether an appropriate estimation result has been obtained and when to switch from manual operation to autonomous control. The method is implemented on a virtual teleoperation system and seamless control transition is demonstrated.