Optimization of Cartesian Tasks with Configuration Selection

TL;DR

This paper introduces a novel optimization algorithm that simultaneously determines the optimal workpiece placement and robot configuration for industrial tasks, improving efficiency over traditional iterative methods.

Contribution

It presents a new higher-order optimization method that extends robot capabilities with a virtual axis to optimize Cartesian task placement and robot configuration simultaneously.

Findings

Successfully applied to a commercial industrial robot example.

Improves efficiency by reducing iterative trial-and-error.

Demonstrates practical feasibility of the approach.

Abstract

A basic task in the design of an industrial robot application is the relative placement of robot and workpiece. Process points are defined in Cartesian coordinates relative to the workpiece coordinate system, and the workpiece has to be located such that the robot can reach all points. Finding such a location is still an iterative procedure based on the developers' intuition. One difficulty is the choice of one of the several solutions of the backward transform of a typical 6R robot. % combined with the limited range of the axes. We present a novel algorithm that simultaneously optimizes the workpiece location and the robot configuration at all process points using higher order optimization algorithms. A key ingredient is the extension of the robot with a virtual prismatic axis. The practical feasibility of the approach is shown with an example using a commercial industrial robot.