AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

An annotated bibligraphy of multisensor integration

technical reportIn this paper we give an annotated bibliography of the multisensor integration literature

Plethora : a framework for the intelligent control of robotic assembly systems

Plethora : a framework for the intelligent control of robotic assembly system

Design of a Generic Manufacturing Cell Control System.

The feasibility of design and demonstration of a cell control system to function in the fully integrated manufacturing environment independent of the parts produced or the manufacturing processes involved was investigated. A hierarchical control structure was used. Free standing implementations of a cell controller, a workstation controller, and programmable device interfaces were designed. The system is data driven, and was designed to use the manufacturing databases that exist in the computer integrated manufacturing environment. Operation of the cell controller and its interaction with the rest of the system was demonstrated in real-time by simulating the computer integrated manufacturing environment on microcomputers connected to each other via communication links

Collaborative and Cooperative Robotics Applications using Visual Perception

The objective of this Thesis is to develop novel integrated strategies for collaborative and cooperative robotic applications. Commonly, industrial robots operate in structured environments and in work-cell separated from human operators. Nowadays, collaborative robots have the capacity of sharing the workspace and collaborate with humans or other robots to perform complex tasks. These robots often operate in an unstructured environment, whereby they need sensors and algorithms to get information about environment changes. Advanced vision and control techniques have been analyzed to evaluate their performance and their applicability to industrial tasks. Then, some selected techniques have been applied for the first time to an industrial context. A Peg-in-Hole task has been chosen as first case study, since it has been extensively studied but still remains challenging: it requires accuracy both in the determination of the hole poses and in the robot positioning. Two solutions have been developed and tested. Experimental results have been discussed to highlight the advantages and disadvantages of each technique. Grasping partially known objects in unstructured environments is one of the most challenging issues in robotics. It is a complex task and requires to address multiple subproblems, in order to be accomplished, including object localization and grasp pose detection. Also for this class of issues some vision techniques have been analyzed. One of these has been adapted to be used in industrial scenarios. Moreover, as a second case study, a robot-to-robot object handover task in a partially structured environment and in the absence of explicit communication between the robots has been developed and validated. Finally, the two case studies have been integrated in two real industrial setups to demonstrate the applicability of the strategies to solving industrial problems

Industrial Robotics

This book covers a wide range of topics relating to advanced industrial robotics, sensors and automation technologies. Although being highly technical and complex in nature, the papers presented in this book represent some of the latest cutting edge technologies and advancements in industrial robotics technology. This book covers topics such as networking, properties of manipulators, forward and inverse robot arm kinematics, motion path-planning, machine vision and many other practical topics too numerous to list here. The authors and editor of this book wish to inspire people, especially young ones, to get involved with robotic and mechatronic engineering technology and to develop new and exciting practical applications, perhaps using the ideas and concepts presented herein

New concepts in automation and robotic technology for surface engineering

Nowadays, the use of robots for the automation of process is very common. This is due to the advantages provided: cost reduction, quality increase, high reproducibility, etc. Nevertheless the robots have the disadvantage, that a high initial investment is necessary. Thermal spraying processes use industrial robots for many reasons, some of them are: high control of the process, quality increase, dangerous work environment, etc. The industrial robot can control many parameters during the process; like the trajectory and the velocity of the torch, which have a significant influence on the heat and mass transfer to the piece and coating. Properties such as coating thickness, porosity, micro hardness and thermal stress distribution are therefore significantly influenced by the spraying distance, velocity and trajectory. It is thus necessary to implement new tools, which support robot programming and fulfill the requirements of torch handling for thermal spraying and lacquered operation. Optimized robot programming is necessary for high quality products regarding coating properties and functionality. To optimize the robot programming, different off-line programming tools are used. The off-line programming has the advantages: increase of work safety and efficiency, low time to program, continuous production, etc.Escuela Técnica Superior de Ingeniería IndustrialUniversidad Politécnica de CartagenaInstitute for Manufacturing Technologies of Ceramic Components and Composites (IMTCCC; University of Stuttgart

Data and Process Mining Applications on a Multi-Cell Factory Automation Testbed

This paper presents applications of both data mining and process mining in a factory automation testbed. It mainly concentrates on the Manufacturing Execution System (MES) level of production hierarchy. Unexpected failures might lead to vast losses on investment or irrecoverable damages. Predictive maintenance techniques, active/passive, have shown high potential of preventing such detriments. Condition monitoring of target pieces of equipment beside defined thresholds forms basis of the prediction. However, monitored parameters must be independent of environment changes, e.g. vibration of transportation equipments such as conveyor systems is variable to workload. This work aims to propose and demonstrate an approach to identify incipient faults of the transportation systems in discrete manufacturing settings. The method correlates energy consumption of the described devices with the workloads. At runtime, machine learning is used to classify the input energy data into two pattern descriptions. Consecutive mismatches between the output of the classifier and the workloads observed in real time indicate possibility of incipient failure at device level. Currently, as a result of high interaction between information systems and operational processes, and due to increase in the number of embedded heterogeneous resources, information systems generate unstructured and massive amount of events. Organizations have shown difficulties to deal with such an unstructured and huge amount of data. Process mining as a new research area has shown strong capabilities to overcome such problems. It applies both process modelling and data mining techniques to extract knowledge from data by discovering models from the event logs. Although process mining is recognised mostly as a business-oriented technique and recognised as a complementary of Business Process Management (BPM) systems, in this paper, capabilities of process mining are exploited on a factory automation testbed. Multiple perspectives of process mining is employed on the event logs produced by deploying Service Oriented Architecture through Web Services in a real multi-robot factory automation industrial testbed, originally used for assembly of mobile phones

Industrial Robot Collision Handling in Harsh Environments

The focus in this thesis is on robot collision handling systems, mainly collision detection and collision avoidance for industrial robots operating in harsh environments (e.g. potentially explosive atmospheres found in the oil and gas sector). Collision detection should prevent the robot from colliding and therefore avoid a potential accident. Collision avoidance builds on the concept of collision detection and aims at enabling the robot to find a collision free path circumventing the obstacle and leading to the goal position. The work has been done in collaboration with ABB Process Automation Division with focus on applications in oil and gas. One of the challenges in this work has been to contribute to safer use of industrial robots in potentially explosive environments. One of the main ideas is that a robot should be able to work together with a human as a robotic co-worker on for instance an oil rig. The robot should then perform heavy lifting and precision tasks, while the operator controls the steps of the operation through typically a hand-held interface. In such situations, when the human works alongside with the robot in potentially explosive environments, it is important that the robot has a way of handling collisions. The work in this thesis presents solutions for collision detection in paper A, B and C, thereafter solutions for collision avoidance are presented in paper D and E. Paper A approaches the problem of collision avoidance comparing an expert system and a hidden markov model (HMM) approach. An industrial robot equipped with a laser scanner is used to gather environment data on arbitrary set of points in the work cell. The two methods are used to detect obstacles within the work cell and shows a different set of strengths. The expert system shows an advantage in algorithm performance and the HMM method shows its strength in its ease of learning models of the environment. Paper B builds upon Paper A by incorporating a CAD model of the environment. The CAD model allows for a very fast setup of the expert system where no manual map creation is needed. The HMM can be trained based on the CAD model, which addresses the previous dependency on real sensor data for training purposes. Paper C compares two different world-model representation techniques, namely octrees and point clouds using both a graphics processing unit (GPU) and a central processing unit (CPU). The GPU showed its strength for uncompressed point clouds and high resolution point cloud models. However, if the resolution gets low enough, the CPU starts to outperform the GPU. This shows that parallel problems containing large data sets are suitable for GPU processing, but smaller parallel problems are still handled better by the CPU. In paper D, real-time collision avoidance is studied for a lightweight industrial robot using a development platform controller. A Microsoft Kinect sensor is used for capturing 3D depth data of the environment. The environment data is used together with an artificial potential fields method for generating virtual forces used for obstacle avoidance. The forces are projected onto the end-effector, preventing collision with the environment while moving towards the goal. Forces are also projected on to the elbow of the 7-Degree of freedom robot, which allows for nullspace movement. The algorithms for manipulating the sensor data and calculating virtual forces were developed for the GPU, this resulted in fast algorithms and is the enabling factor for real-time collision avoidance. Finally, paper E builds on the work in paper D by providing a framework for using the algorithms on a standard industrial controller and robot with minimal modifications. Further, algorithms were specifically developed for the robot controller to handle reactive movement. In addition, a full collision avoidance system for an end-user application which is very simple to implement is presented. The work described in this thesis presents solutions for collision detection and collision avoidance for safer use of robots. The work is also a step towards making businesses more competitive by enabling easy integration of collision handling for industrial robots

Fourth Conference on Artificial Intelligence for Space Applications

Proceedings of a conference held in Huntsville, Alabama, on November 15-16, 1988. The Fourth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: space applications of expert systems in fault diagnostics, in telemetry monitoring and data collection, in design and systems integration; and in planning and scheduling; knowledge representation, capture, verification, and management; robotics and vision; adaptive learning; and automatic programming