KNOWLEDGE-BASED ROBOT VISION SYSTEM FOR AUTOMATED PART HANDLING

South African Journal of Industrial Engineering, May 2008 by Wang, J, van Niekerk, T I, Hattingh, D G, Hua, T

ABSTRACT

This paper discusses an algorithm incorporating a knowledge-based vision system into an industrial robot system for handling parts intelligently. A continuous fuzzy controller was employed to extract boundary information in a computationally efficient way. The developed algorithm for on-line part recognition using fuzzy logic is shown to be an effective solution to extract the geometric features of objects. The proposed edge vector representation method provides enough geometric information and facilitates the object geometric reconstruction for gripping planning. Furthermore, a part-handling model was created by extracting the grasp features from the geometric features.

OPSOMMING

Hierdie artikel beskryf 'n kennis-gebaseerde visiesisteemalgoritme wat in 'n industri?le robotsisteem ingesluit word om sodoende intelligente komponenthantering te bewerkstellig. 'n Kontinue wasige beheerder is gebruik om allerlei objekinligting deur middel van 'n effektiewe berekeningsmetode te bepaal. Die ontwikkelde algoritme vir aan-lyn komponentherkenning maak gebruik van wasige logika en word bewys as 'n effektiewe metode om geometriese inligting van objekte te bepaal. Die voorgestelde grensvektormetode verskaf voldoende inligting en maak geometriese rekonstruksie van die objek moontlik om greepbeplanning te kan doen. Voorts is 'n komponenthanteringsmodel ontwikkel deur die grypkenmerke af te lei uit die geometriese eienskappe.

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1. INTRODUCTION

PC-based intelligent gripping systems are characterized by sensor-based perception and fusion, the incorporation of intelligent algorithms into the control systems, as well as the integration of individual subsystem modules. A robot that can 'see' and 'feel' is easier to train in the performance of complex tasks while, at the same time, it requires a less stringent control mechanism than pre-programmed machines do. A sensory, trainable system is also adaptable to a much larger variety of tasks - thus achieving a degree of universality that ultimately translates into lower production and maintenance costs.

Robot vision is a multidisciplinary field of study, and it plays a critical role in robot intelligence. Vision capabilities endow a robot with a sophisticated sensing mechanism that allows it to respond to its environment in an 'intelligent' and flexible manner. Part recognition is a pivotal process in the multi-sensor robot system. The function of recognition algorithms is to identify each segmented object in a scene and to assign a label to that object. Recognition approaches in use today can be divided into two principal categories: decision-theoretic and structural. Decision-theoretic methods are based on quantitative descriptions (e.g. statistical texture) while structural methods rely on symbolic descriptions and their relationships (e.g. sequences of directions in a chain-coded boundary).

For an intelligent gripping system, the object position and orientation, as well as the object's profile, must be identified and represented accurately in real time. An intelligent gripping process is usually accomplished with the involvement of sensors, such as vision sensor, force sensor, and tactile sensors, so that the grasping status is kept in a stable condition. An automatic gripping planning system must be capable of reasoning out the shape of objects within the workspace [1, 2]. All possible faces must be reasoned out, based on the boundary model extracted from the partrecognition module. A gripping model is accordingly constructed in terms of those faces. Heuristic approaches are effective and efficient to determine the optimal faces to be grasped.

This project is focused on the extraction, description, and interpretation of those features that are significant to the gripping-oriented application and capable of facilitating gripping planning. To represent the objects efficiently and effectively, an edge vector expression method was developed such that computational efficiency is increased dramatically. To extract edge vectors, a fuzzy vector tracing method was developed, based on a continuous fuzzy controller and heuristic vector tracing method. Profile traversing and feature extracting were conducted with respect to the closed vector chain generated. The classification algorithm was based on structural technique - also known as syntactic pattern recognition - which considers relationships between features or edges of an object, and deduces the geometry ID in terms of the topology of edge vectors.

2. ROBOT VISION SYSTEM

Figure 1 illustrates the generic intelligent gripper system, which consists of a robot vision module, a robot control module, and an intelligent gripping module based on industrial robots. The core of this type of system features machine vision and gripping intelligence.

The architecture of a robot vision system may be regarded as hierarchical. The robot vision functionalities may be partitioned into, first, low-level vision, which includes those processes that are primitive and require no intelligence such as image acquisition and image preprocessing; and, second, high-level vision, which includes extracting, modeling, recognition, description, and cognition. In this project, the objectives of the vision system are to produce data that represent the grippingoriented profile, and to identify the geometry. The architecture of the robot vision system implemented in this project is illustrated in Figure 2.