New Applications in Industry 4.0

*Industrial Robotics - New Paradigms*

Journal on Interactive Design and Manufacturing (IJIDeM).

2019;**13**(4):1401-1422

(ISIE), Vancouver; 2019

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**58**

**Chapter 4**

**Abstract**

manufacturing, industrial 4.0.

**1. Introduction**

uncertainties on the fly.

**61**

high-speed sensing, coarse-to-fine strategy

Dynamic Compensation

Framework to Improve the

**Keywords:** industrial robot, autonomy, intelligence architecture,

Autonomy of Industrial Robots

*Shouren Huang, Yuji Yamakawa and Masatoshi Ishikawa*

It is challenging to realize the autonomy of industrial robots under external and internal uncertainties. A majority of industrial robots are supposed to be programmed by teaching-playback method, which is not able to handle with uncertain working conditions. Although many studies have been conducted to improve the autonomy of industrial robots by utilizing external sensors with model-based approaches as well as adaptive approaches, it is still difficult to obtain good performance. In this chapter, we present a dynamic compensation framework based on a coarse-to-fine strategy to improve the autonomy of industrial robots while at the same time keeping good accuracy under many uncertainties. The proposed framework for industrial robot is designed along with a general intelligence architecture that is aiming to address the big issues such as smart

Performance of industrial robots in realizing fast and accurate manipulation is very important for manufacturing process, as it directly relates to productivity and quality. On the other hand, with manufacturing shifting from an old era of mass production to a new era of high-mix low-volume production, autonomous capability of industrial robots becomes more and more important to the manufacturing industry. Autonomy represents the ability of a system in reacting to changes and

Currently, off-line teaching-playback using a teaching pendant, or physically positioning a robot with a teaching arm, is supposed to be the main method for the applications of industrial robots. The method features a user-friendly interface developed by commercial robot manufacturers and is usually motion optimized and reliable so long as task conditions do not change. As detailed in [1], negative effects of nonlinear dynamics during high-speed motion may be pre-compensated in order to achieve accurate path tracking during the playback phase. However, it is impossible for a teaching-playback robot to adapt to significant variations in the initial pose of a working target or unexpected fluctuations during manipulation. CAD model-based teaching methods neither enable a robot to adapt to changes on the fly.
