A General Approach for Constrained Robotic Coverage Path Planning on 3D Freeform Surfaces

Abstract

There are many industrial robotic applications which require a robot manipulator’s end-effector to fully cover a 3D surface region in a constrained motion. Constrained surface coverage in this context is focused on placing commonly used coverage patterns (such as raster, spiral, or dual-spiral) onto the surface for the manipulator to follow. The manipulator must continuously satisfy surface task constraints imposed on the end-effector while maintaining manipulator joint constraints. While there is substantial research for coverage on planar surfaces, methods for constrained coverage of 3D (spatial) surfaces are limited to certain (parametric or spline) surfaces and do not consider feasibility systematically given manipulator and task constraints. There is a lack of fundamental research to address the general problem: given a manipulator, a 3D freeform surface, and task constraints, whether there exists a feasible continuous motion plan to cover the surface, and if so, how to produce a uniform coverage path that best satisfies task constraints. In this paper, we introduce a general approach to address this fundamental but largely open coverage problem. We have applied our approach to example 3D freeform surface coverage tasks in simulation and real world environments with a 7-DOF robotic manipulator to demonstrate its effectiveness. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper was motivated by the constrained coverage path planning problem on 3D freeform surfaces for many industrial applications, such as painting, spray coating, abrasive blasting, polishing, shotcreting, etc. It provides a principled and general approach that includes an automatic robotic system to find feasible robotic end-effector paths for covering a 3D freeform surface with some interaction from a human worker who provides key parameters related to the specific task without being an expert in robotics. Therefore, the approach enables a human worker who only has the domain knowledge of a specific coverage task to operate the general and automatic robotic system effectively for completing the task.