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This paper proposes an efficient base position (BP) optimization method for mobile painting robot manipulators (MPRMs). An approximate decoupled model is first established to overcome the coupling problem of painting robots. And the manipulating characteristics are summarized as three constraints: positioning, orientation and singularity avoidance constraints. Then, joint-level performance criteria of one manipulating point and a painting path, which reflect the manipulability and dexterity, were constructed successively. Considering multiple constraints, the BP optimization problem is translated into a standard inequality constrained optimization problem of the path criterion. Two algorithms are designed to solve this problem: one is based on the internal penalty function method used to obtain an initial BP; the other is based on the generalized Lagrange multiplier method used to get the near-optimal BP. This method was applied to a real MPRM system painting three typical surfaces: flat, cylindrical and truncated conical surfaces. Application results demonstrate the effectiveness as well as the availability of the approximate decoupled model. Simultaneously, compared with previous methods, the efficiency is improved by hundreds of times.

Purpose To paint large workpieces automatically, painting manipulators with hollow wrists must be transported by mobile platforms to different positions because of their limited workspaces. This paper aims to provide a visualization method for finding appropriate base positions (BPs) and maximum painting areas for manipulators. Design/methodology/approach This paper begins by analyzing the motion characteristics of manipulators possessing a spherical wrist and summarizing them into three constraints – positioning, orientation and singularity avoidance. The hollow wrist is simplified and considered as spherical by introducing the concepts of an inner wrist center and an outer wrist center. Taking the three constraints into consideration, the boundaries of the manipulating space are formulated analytically. Finally, to verify the method, the space obtained is applied to determine the maximum painting areas for flat, cylindrical and conical surfaces. Experiments of robotic painting were used to confirm the results. Findings Compared with previous studies, the maximum areas obtained using the proposed method increased by 17-131 per cent with an algorithm of lower complexity, and the process remained visually intuitive, thereby demonstrating that the method of manipulating space is more effective. Originality/value Such a method allows individuals to visualize the entire painting area at the current BP, thereby maximizing painting areas or optimizing BPs. It opens a black box that is the relationship between BPs and blocks. The method can also be used to choose the best configuration for painting manipulators, select the end-effector structure parameters, split surfaces into blocks, etc.

In order to paint large workpieces, heavy painting manipulators are always transported by mobile platforms due to their limited workspaces. To reduce the vibration of the end-effector, the base velocity of the manipulator is constant when painting. In this operation mode, the Starting Base Position (SBP) of the manipulator is important to attain the maximum manipulability and dexterity. This paper aims to optimize the SBP for painting a specified surface efficiently. An approximated decouplable model of painting manipulators is first built to get the analytically expressed inverse kinematic solutions. Then joint-level criteria for one manipulating point reflecting the manipulability and dexterity are proposed, followed by the combined criterion for a painting task about SBP with consideration of the platform velocity. Afterwards, the SBP optimization problem is translated into a least squares problem. To solve such a problem, an initial SBP value is first calculated through an algorithm based on internal penalty function method. Then a modified Levenberg-Marquardt method is employed to get the optimal SBP. Results of applications on painting a straight and an arc path indicate that the method proposed is effective and efficient.

The industrial manufacturers are pleased to set up robotic manipulators instead of human workers to execute painting tasks which greatly reduces the risk of workers’ being poisoned and is more conductive to maintain stable quality because of the manipulator’s high repetitive accuracy. When the industrial manipulator is employed to paint on big parts, the operating mode is also changed. The manipulator is mounted on a mobile platform, which is usually a NC-machine, to extend its workspace for painting on big parts. And the manipulator starts operation just as the mobile platform arrives at a proper position and stops. However it is difficult for a human worker to figure out a proper stop position in which the manipulator can reach all goal points with a regular process velocity due to the special mechanical structure of serial revolute joints and the compound free-form surfaces, which is the most significant barrier to develop robotic big-parts painting. In this paper, we put forward a novel idea based on analysis of the base’s workable location space (BWLS) of the manipulator to solve the stop position optimizing problem in order to get a bigger continuous operating area for the manipulator once stops and improve painting quality. Compared to previous study on the reachability of the end-effector fixing the base of the manipulator, we explore the BWLS while the center wrist point of the manipulator is determined. Through formulating an analytical expression, we can describe the BWLS more precisely and the intersection curves between these workable spaces can be also obtained when the wrist moves to the target points. With considering these boundary surfaces derived from the physical limits of the manipulator, the planning results will be more efficient and reliable. The optimization results are verified by computer simulation. A 3D model of the manipulator with a mobile platform has been constructed. The painting trajectory for a target plane surface is firstly performed and the corresponding optimized stop position is then provided. Thus the method is proved to be effective and less time cost which could significantly reduce the planning time in comparison with manual operation.

This paper analyses how the width of overlapping decides the coating thickness according to the robotic spray painting model. As it is difficult to obtain stable width of the overlapping between adjacent paths for the commonly used spraying path generation based on parallel planes, which causes problems for controlling the coating thickness in the overlap area, we come up with a spraying path generation algorithm for painting free-form surfaces. The new algorithm is able to guarantee that the overlapping widths are always the same by accurately controlling the distance between corresponding points in adjacent paths. The algorithm is tested on an extrude surface and the result represents that it dramatically reduces the deviations of coating overlapping width between two adjacent paths, which does much good for increasing the coating thickness uniformity.