Task planning for mobile painting manipulators based on manipulating space

Abstract

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.

Task Space Model Predictive Control for Vineyard Spraying with a Mobile Manipulator

“…Similar work has been done with mobile manipulators for paint spraying [24][25][26]. Here, a lawnmower path is often used as a reference for the position of the tool, an approach that was adopted in the currently presented work.…”

A deep learning based automatic surface segmentation algorithm for painting large-size aircraft with 6-DOF robot

“…Therefore, there are eight parts left to be solved. Corresponding dexterous workspace can be calculated with one posture given (Wang et al , 2018). In that article, we calculate dexterous workspace only corresponding to one posture for finding suitable station of paint robot.…”

“…Where r , θ and ψ are three spherical coordinates as shown inFigure 5(a); l 2 and l 3 are the lengths of Link 2 (l2 inFigure 4, from Axis 2 to Axis 3) and Link 3 (l3 inFigure 4, from PA to Axis 3), whose value are 1,300 and 1,508.5 mm, respectively; α′ 0 is the generalized maximum wrist angle and α′ is one direction angle (Wang et al , 2018).…”

“…Therefore automatic robotic painting operations are more and more popular in manufacturing, such as mobile phone manufacturing, automotive manufacturing and aircraft manufacturing. To achieve completely automated painting, many researches (Yu et al , 2015), (Wang et al , 2018) have been conducted on robotic paint operations. However, there are still many issues impeding complete automation, such as surface segmentation (Atkar et al , 2008), (Xia et al , 2009) and trajectory planning (Wang et al , 2015), (Zhou et al , 2015), (Sun et al , 2011), (Seriani et al , 2015), (Andulkar et al , 2015).…”

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