Error recovery in intelligent robotic workcells

This study describes the application of the develop ed model to the scheduling of the Robotic Flexible Assembly Cells (RFACs). The present study is a continuation of the paper titled “Development of a Fuzz ySimulation Model of Scheduling Robotic Flexible Assembly Cells”. This new model for multi-objective scheduling problems in RFACs was based on combining a fuzzy-based mathematical model with simulation software tools. In this study, a hypothetical case study of RFACs is presented to demonstrate the effectiveness of the developed model and then compare the results that are obtained by the new methodology with the common scheduling rules. The simulation results show that the performance of the proposed methodology outperforms the most popular scheduling rules from previous research.

Multi-objective optimisation of dynamic scheduling in robotic flexible assembly cells via fuzzy-based Taguchi approach

Companies are increasingly being forced by market demands to increase the competitiveness of their products in regard to price, quality and delivery time. Development, both of product and production, is the key word in achieving this goal. This paper focuses on the following related subject: cost-effective design, with applications to robotic assembly cells. Against this background, a total productivity model is introduced, which allows an opinion to be formed about the performance of robotic assembly cells during the conceptual stage of the design process.

A simulation model for use in designing robotic assembly cells

Traditionally, discrete parts or batch‐based manufacturing and process control have been quite distinct areas of design engineering. A robotic assembly cell and a glass manufacturing line has been seen as requiring differing design philosophies‐even down to instrumentation, sensors and control strategies that they may employ. This may change if an ambitious pan‐European project to develop sensor‐based systems produces hoped‐for results. The systems will be designed for incorporation into (almost) any industrial regime with minimal modification.

This paper presents ongoing research on assistive service robotics for health-care and home applications. The whole control system is based on a behavior-based architecture that integrates reactive behavior into a task driven structure. Efficient and natural robot communications are promoted by employing the user's perceived object color names. This information is then integrated with simple object shape features for object location and recognition. An error recovery strategy is embedded in each behavior by incorporating reasoning and an error recovery template into the behavior structure. Error recovery experience is also stored in the template and used to adjust the error recovery action generation process to improve the system's performance during its lifetime.

This book focuses on the design of Robotic Flexible Assembly Cell (RFAC) with multi-robots. Its main contribution consists of a new effective strategy for scheduling RFAC in a multi-product assembly environment, in which dynamic status and multi-objective optimization problems occur. The developed strategy, which is based on a combination of advanced solution approaches such as simulation, fuzzy logic, system modeling and the Taguchi optimization method, fills an important knowledge gap in the current literature and paves the way for future research towards the goal of employing flexible assembly systems as effectively as possible despite the complexity of their scheduling.

This study describes the application of the developed approach to the scheduling of the Robotic Flexible Assembly Cells (RFACs). The present study is a continuation of the paper titled "development of fuzzy AHP and TOPSIS approach for multi-objective scheduling problems in robotic flexible assembly cells". In this paper, a realistic case study of RFACs is presented to examined and verified the developed approach via implement three phases: problem description; application of FAHP-FTOPSIS; and analysis of the results. The analysis showed the stability of the results for FAHP-FTOPSIS and also verified the robustness and the appropriateness of the proposed approach. This study also demonstrated that the proposed approach can be successfully used in the multi-objective optimisation of dynamic scheduling in RFAC.

The concept of robotic flexible assembly cell (RFAC) has the potential to introduce significant improvements in system performance, as highlighted in Chap. 1. To achieve these improvements, the problems of RFAC at scheduling level need to be critically addressed. To date, researchers have paid little attention to handling the scheduling problems relevant to RFAC.

Flexible Assembly Systems - Hardware Components and Features: Basic components of a FAS.- Classification of flexible assembly systems.- Examples of industrial installations.- Issues in Design, Planning and Scheduling of Flexible Assembly: FAS design issues.- Network design for material flow systems.- Design for assembly.- Assembly planning.- Planning and scheduling.- Loading and Routing Decisions in Flexible Assembly Systems: Description of a flexible assembly system.- Optimization of station workloads and product movements.- Design and balancing of flexible assembly lines.- Numerical examples.- Simultaneous loading and routing.- Sequential loading and routing.- Loading and Routing Decisions with Assembly Plan Selection: Simultaneous loading, routing and assembly plan selection in a general flexible assembly system.- Simultaneous loading, routing and assembly plan selection in a flexible assembly line.- Loading and Scheduling in Flexible Assembly Cells: Loading and scheduling in a robot assembly cell.- Loading and sequencing in printed circuit board assembly.- Production Scheduling in Flexible Assembly Lines: Flexible assembly line with limited intermediate buffers.- Flexible assembly line with no in-process buffers.- Global lower bounds for flexible flow lines with unlimited buffers.- Just-in-time scheduling of flexible assembly lines.- Multilevel scheduling of flexible assembly lines with limited intermediate buffers.- Machine and Vehicle Scheduling in Flexible Assembly Systems: Dispatching scheduling.- Machine and vehicle scheduling - a multi-level approach.- Machine and vehicle scheduling - a single-level approach.

In this chapter, we described the design and integration of a robotic welding cell using a 3D simulation environment. The design was useful for building the CORBA-based distributed robotized welding cell in this research project. Issues such as layout definition, communication design, welding part design, robot and welding station commissioning were considered. The design also included a voice-command driven environment using the Microsoft Speech Application Interface V5.0. Definition of Context Free Grammars were used so that it was possible to start a typical robot task using a human operator's voice using verbal commands such as "weld perimeter" or "weld trajectory". The design and simulation previous to the implementation of an automated welding cell is useful, because possible errors can be prevented such as problems of area distribution, security, dimensions, etc. In addition to its great utility to save costs and avoid unnecessary damage to machinery and equipment. The design of complex robot systems using multisensorial inputs, high-level machine interfaces and distributed networked systems will be elements is of primary importance for advance robot manipulators in the near future so that the work reported in this chapter intents to demonstrate alternative guidelines to design such complex systems.

Existing methods for predicting robotic snap joint assembly cannot predict failures before their occurrence. To address this limitation, this paper proposes a method for predicting error states before the occurence of error, thereby enabling timely recovery. Robotic snap joint assembly requires precise positioning; therefore, even a slight offset between parts can lead to assembly failure. To correctly predict error states, we apply functional principal component analysis (fPCA) to 6Dforce/torque profiles that are terminated before the occurence of an error. The error state is identified by applying a feature vector to a decision tree, wherein the support vector machine (SVM) is employed at each node. If the estimation accuracy is low, we perform additional probing to more correctly identify the error state. Finally, after identifying the error state, a robot performs the error recovery motion based on the identified error state. Through the experimental results of assembling plastic parts with four snap joints, we show that the error states can be correctly estimated and a robot can recover from the identified error state.

Human or Industrial Robots

Multi-step manipulation tasks where robots interact with their environment and must apply process forces based on the perceived situation remain challenging to learn and prone to execution errors. Accurately simulating these tasks is also difficult. Hence, it is crucial for robust task performance to learn how to coordinate end-effector pose and applied force, monitor execution, and react to deviations. To address these challenges, we propose a learning approach that directly infers both low- and high-level task representations from user demonstrations on the real system. We developed an unsupervised task segmentation algorithm that combines intention recognition and feature clustering to infer the skills of a task. We leverage the inferred characteristic features of each skill in a novel unsupervised anomaly detection approach to identify deviations from the intended task execution. Together, these components form a comprehensive framework capable of incrementally learning task decisions and new behaviors as new situations arise. Compared to state-of-the-art learning techniques, our approach significantly reduces the required amount of training data and computational complexity while efficiently learning complex in-contact behaviors and recovery strategies. Our proposed task segmentation and anomaly detection approaches outperform state-of-the-art methods on force-based tasks evaluated on two different robotic systems.

Current developments in the field of automated assembly systems show an increasing interest in systems that are flexible in both CAD based product design and CAD based assembly. For the application addressed in this paper, coupling the vision system and a CAD database is of prime importance in order to achieve the required automatic reconfiguration of the assembly cell when new parts are defined. This paper presents a 3D CAD-based vision system for obtaining 3D data about the scene. After the images are acquired, edge detection is preformed and the detected edges are stored as chaincodes. Following that, a stereo vision algorithm is applied for finding the recognition features. The output are lists of features that are combined into a 3D wireframe representing the scene. The recognition algorithm takes the observed wireframe outputs from the stereo vision system, and compares them with a set of model wireframes derived from previous models, in order to select the 'best match', where the previous models used for recognition are derived from a product data model (PDM). The PDM is an interface between the CAD database and the recognition system, which allows the automatic generation of new models when new parts are introduced into the system. The vision system described in this paper is part of an intelligent robotic assembly cell, where the aim is to build a flexible intelligent robotic assembly cell, such that robots would be able to automatically assemble a random variety of small- batch products.