A novel shared control algorithm for industrial robots

Marco Ramacciotti,Stefano Roccella,Cesare Stefanini,Fabio Leoni,Mario Milazzo

doi:10.1177/1729881416682701

Abstract

Human management of robots in many specific industrial activities has long been imperative, due to the elevated levels of complexity involved, which can only be overcome through long and wasteful preprogrammed activities. The shared control approach is one of the most emergent procedures that can compensate and optimally couple human smartness with the high precision and productivity characteristic to mechatronic systems. To explore and to exploit this approach in the industrial field, an innovative shared control algorithm was elaborated, designed and validated in a specific case study.

Highlights

Collaboration between robots and humans is one of the major topics faced by scientists in recent years: the main issue being to obtain specific goals aimed at improving the sustainability of many processes, especially in industrial contexts
The innovative shared control algorithm for industrial robotics processes (SCAIRPs) shown in this article become a new paradigm for the development of anthropomorphic industrial robotic processes, using the advantages of automation with ‘active supervision’ on the part of users
It is possible to highlight the first of the track made with a constant speed guaranteed by the robot control (Figure 8(a0)) and, in Figure 8(b0), the outcome arising from the combination of the robotic speed control with the controlled weaving movement given by the human operator

Summary

Introduction

Collaboration between robots and humans is one of the major topics faced by scientists in recent years: the main issue being to obtain specific goals aimed at improving the sustainability of many processes, especially in industrial contexts. The innovative shared control algorithm for industrial robotics processes (SCAIRPs) shown in this article become a new paradigm for the development of anthropomorphic industrial robotic processes, using the advantages of automation with ‘active supervision’ on the part of users With this strategy, a mechatronic system can execute its task with the human operator interacting and modifying the robot’s preprogrammed tasks while the process is running. The algorithm is composed of two cycles as follows: Main cycle (MC): In this cycle, the robot’s main task (motion path) is calculated and conveniently managed to be processed into the ‘subcycle NOC (SCN)’ In this phase, NOC and Ás are defined (Equations (11) and (12)). T1 was conducted using the software-in-the-loop approach with the following simulation setup: constant v 1⁄4 500 mmmin; cycle time Át 1⁄4 7 ms; linear path A start point ð0; 0Þ, end point (50,0); linear path B start point (0,0), end point (5,0); simulation of haptic HRI inputs using a random function

Experiments

Conclusions