Sequencing of Parts in Robotic Cells

This paper considers scheduling problems in robotic cells that produce a set of part types on several machines served by one robot. We study the problem of sequencing parts of different types in a cell to minimize the production cycle time when the sequence of the robot moves is given. This problem is NP-hard for most of the one-unit robot move cycles in a robotic cell with more than two machines and producing more than two part types. We first give a mathematical formulation to the problem, and then propose a branch-and-bound algorithm to solve it. The bounding scheme of this algorithm is based on relaxing, for all of the machines except two, the constraints that a machine should be occupied by a part for a period at least as long as the processing time of the part. The lower bound obtained in this way is tight. This relaxation allows us to overcome the complexity of the problem. The lower bound can be computed using the algorithm of Gilmore and Gomory. Computational experiments on part sequencing problems in three-machine robotic cells are given.     

Schedule efficiency in a robotic production cell

In this paper, we analyze the efficiency of a given robot movement schedule for the case of a flow shop robotic production cell withm different machines, one input conveyor, and one output conveyor. We begin with the case of one-robot cells and extend our results to multirobot cells. The paper studies the efficiency of a movement schedule for identical parts by defining a movement network associated with this schedule. This network models any cell layout and applies to multirobot cells. Using the movement network, we propose two cycle time evaluation methods, the first using linear programming and the second based on finding a longest path. The latter method generates a procedure to obtain an analytical formula for the cycle time. We extend the proposed methods to study the efficiency of a given input sequence (schedule) for different parts, that is, to determine the sequence processing time. The results obtained here allow us to quickly evaluate the efficiency of any given feasible movement schedule, for identical or different parts.     

Scheduling algorithms for a two-machine flexible manufacturing system

The flexible manufacturing system (FMS) considered in this paper is composed of two CNC machines working in series—a punching machine and a bending machine connected through rollers acting as a buffer system of finite capacity. The main difference between the present problem and the standard two-machine flow shop problem with finite intermediate capacity is precisely the buffer system, which in our problem consists of two stacks of parts supported by rollers: the first stack contains the output of the punching machine, while the second stack contains the input for the bending machine. When the second stack is empty, the first stack may be moved over. Furthermore, the capacity of each stack depends on the particular part type being processed. The FMS can manufacture a wide range of parts of different types. Processing times on the two machines are usually different so that an unbalance results in their total workload. Furthermore, whenever there is a change of the part type in production, the machines must be properly reset—that is, some tools need to be changed or repositioned. A second important difference between the present problem and the usual two-machine flow shop problem is the objective. Given a list ofp part types to be produced in known quantities, the problem considered here is how to sequence or alternate the production of the required part types so as to achieve various hierarchical targets: minimize the makespan (the total time needed to complete production) and, for instance, compress the idle periods of the machine with less workload into a few long enough intervals that could be utilized for maintenance or other reasons. Although Johnson's rule is optimal in some particular cases, the problem addressed in the paper isNP-hard in general: heuristic procedures are therefore provided.     

Virtual Reality Based Support System for Layout Planning and Programming of an Industrial Robotic Work Cell

Traditional robotic work cell design and programming are considered inefficient and outdated in current industrial and market demands. In this research, virtual reality (VR) technology is used to improve human-robot interface, whereby complicated commands or programming knowledge is not required. The proposed solution, known as VR-based Programming of a Robotic Work Cell (VR-Rocell), consists of two sub-programmes, which are VR-Robotic Work Cell Layout (VR-RoWL) and VR-based Robot Teaching System (VR-RoT). VR-RoWL is developed to assign the layout design for an industrial robotic work cell, whereby VR-RoT is developed to overcome safety issues and lack of trained personnel in robot programming. Simple and user-friendly interfaces are designed for inexperienced users to generate robot commands without damaging the robot or interrupting the production line. The user is able to attempt numerous times to attain an optimum solution. A case study is conducted in the Robotics Laboratory to assemble an electronics casing and it is found that the output models are compatible with commercial software without loss of information. Furthermore, the generated KUKA commands are workable when loaded into a commercial simulator. The operation of the actual robotic work cell shows that the errors may be due to the dynamics of the KUKA robot rather than the accuracy of the generated programme. Therefore, it is concluded that the virtual reality based solution approach can be implemented in an industrial robotic work cell.     

A Look-Ahead Routing Procedure for Machine Selection in a Highly Informative Manufacturing System

Many dispatching rules have been developed for the on-line control of product flow in a job shop. The introduction of a highly informative manufacturing system (HIMS) has added a new requirement to a classical job-shop control problem: the selection of machines by parts of different types. An HIMS can keep a great deal of information on the status of the system, such as information on what is scheduled in the near future with great accuracy, which can be used for shop floor control. For example, the knowledge of the time when the next parts arrive at the machines can be used for better routing. This article tests the effect of the use of this knowledge for part routing on the part's flow time and tardiness under a look-ahead routing procedure (LARP). LARP assigns a new part to a machine so that the assignment minimizes the flow time or tardiness of the current part and the next N parts arriving after the current part. A test shows that the reduction of part flow time is up to 11% and the reduction of tardiness is up to 21% for the cases with this procedure.     

A Genetic Algorithm Approach to the Scheduling of FMSs with Multiple Routes

Usually, most of the typical job shop scheduling approaches deal with the processing sequence of parts in a fixed routing condition. In this paper, we suggest a genetic algorithm (GA) to solve the job-sequencing problem for a production shop that is characterized by flexible routing and flexible machines. This means that all parts, of all part types, can be processed through alternative routings. Also, there can be several machines for each machine type. To solve these general scheduling problems, a genetic algorithm approach is proposed and the concepts of virtual and real operations are introduced. Chromosome coding and genetic operators of GAs are defined during the problem solving. A minimum weighted tardiness objective function is used to define code fitness, which is used for selecting species and producing a new generation of codes. Finally, several experimental results are given.     

Scheduling parallel machines with major and minor setup times

This article discusses the problem of scheduling a large set of parts on an FMS so as to minimize the total completion time. Here, the FMS consists of a set of parallel identical machines. Setup time is incurred whenever a machine switches from one type of part to another. The setup time may be large or small depending on whether or not the two part types belong to the same family. This article describes a fast heuristic for this scheduling problem and derives a lower bound on the optimal solution. In computational tests using random data and data from an IBM card test line, the heuristic archieves nearly optimal schedules.     

A knowledge-based system for the strategic control level of robots in flexible manufacturing cells

The strategic control level synthesis for robots is related to a hierarchical robot control problem. The main control problem at the strategic control level is to select the model and algorithm to be used by the lower control level to execute the given robot task. Usually there are several lower control level models and algorithms that can be used by the robot control system for every robot task. Strategic control level synthesis depends on the particular robot system application. In a typical application, when the robot system is used in a flexible manufacturing system for manipulating various part types, the robot tasks executed by the robot system depend on the manufacturing processes in the system. If the robot system is applied in another flexible manufacturing system, dedicated to other manufacturing processes, another set of robot tasks might be needed to perform the necessary operations. Therefore, the quantity and the kind of knowledge required in the system for the strategic control level differ from one application to another. Such a fact creates the appropriate conditions for employing some artificial intelligence techniques. This article describes a knowledge-based system approach to the strategic control level synthesis problem.     

Workload balance and part-transfer minimization in flexible manufacturing systems

Problems related to the flow management of a flexible manufacturing system (FMS) are here formulated in terms of combinatorial optimization. We consider a system consisting of several multitool automated machines, each one equipped with a possibly different tool set and linked to each other by a transportation system for part moving. The system operates with a given production mix. The focused flow-management problem is that of finding the part routings allowing for an optimal machine workload balancing. The problem is formulated in terms of a particular capacity assignment problem. With the proposed approach, a balanced solution can be achieved by routing parts on a limited number of different paths. Such a balancing routing can be found in polynomial time. We also give polynomial-time and-space algorithms for choosing, among all workload-balancing routings, the ones that minimize the global amount of part transfer among all machines.     

Optimization of expression conditions for soluble protein by using a robotic system of multi-culture vessels

We have developed a robotic system for an automated parallel cell cultivation process that enables screening of induction parameters for the soluble expression of recombinant protein. The system is designed for parallelized and simultaneous cultivation of up to 24 different types of cells or a single type of cell at 24 different conditions. Twenty-four culture vessels of about 200 ml are arranged in four columns x six rows. The system is equipped with four independent thermostated waterbaths, each of which accommodates six culture vessels. A two-channel liquid handler is attached in order to distribute medium from the reservoir to the culture vessels, to transfer seed or other reagents, and to take an aliquot from the growing cells. Cells in each vessel are agitated and aerated by sparging filtered air. We tested the system by growing Escherichia coli BL21(DE3) cells harboring a plasmid for a model protein, and used it in optimizing protein expression conditions by varying the induction temperature and the inducer concentration. The results revealed the usefulness of our custom-made cell cultivation robot in screening optimal conditions for the expression of soluble proteins.     

Putting it together: the computational complexity of designing robot controllers and environments for distributed construction

Creating target structures through the coordinated efforts of teams of autonomous robots (possibly aided by specific features in their environments) is a very important problem in distributed robotics. Many specific instances of distributed robotic construction teams have been developed manually. An important issue is whether automated controller design algorithms can both quickly produce robot controllers and guarantee that teams using these controllers will build arbitrary requested target structures correctly; this task may also involve specifying features in the environment that can aid the construction process. In this paper, we give the first computational and parameterized complexity analyses of several problems associated with the design of robot controllers and environments for creating target structures. These problems use a simple finite-state robot controller model that moves in a non-continuous deterministic manner in a grid-based environment. Our goal is to establish whether algorithms exist that are both fast and correct for all inputs and if not, under which restrictions such algorithms are possible. We prove that none of these problems are efficiently solvable in general and remain so under a number of plausible restrictions on controllers, environments, and target structures. We also give the first restrictions relative to which these problems are efficiently solvable and discuss what theoretical solvability and unsolvability results derived relative to the problems examined here mean for real-world construction using robot teams.     

An inexact algorithm for part input sequencing and scheduling with side constraints in FMS

Consider a flexible manufacturing system that produces parts of several types. The FMS consists of several groups of pooled, identical machines, a materials handling system, and a set of nonconsumable resources. Each type of part has its own unique sequence in the execution of the operations. An inexact algorithm is presented that sequences and schedules the input of the parts in the FMS by considering constraints such as machine availabilities and other resources in the machine groups, the availability of the transport units, and so on. Primarily, the goal of the algorithm is to minimize makespan and, secondarily, to minimize turnaround time. Several strategies are discussed, and the results are reported. A real-life problem is described.     

Fly-like visuomotor responses of a robot using aVLSI motion-sensitive chips

 We explore the use of continuous-time analog very-large-scale-integrated (aVLSI) neuromorphic visual preprocessors together with a robotic platform in generating bio-inspired behaviors. Both the aVLSI motion sensors and the robot behaviors described in this work are inspired by the motion computation in the fly visual system and two different fly behaviors. In most robotic systems, the visual information comes from serially scanned imagers. This restricts the form of computation of the visual image and slows down the input rate to the controller system of the robot, hence increasing the reaction time of the robot. These aVLSI neuromorphic sensors reduce the computational load and power consumption of the robot, thus making it possible to explore continuous-time visuomotor control systems that react in real-time to the environment. The motion sensor provides two outputs: one for the preferred direction and the other for the null direction. These motion outputs are created from the aggregation of six elementary motion detectors that implement a variant of Reichardt's correlation algorithm. The four analog continuous-time outputs from the motion chips go to the control system on the robot which generates a mixture of two behaviors – course stabilization and fixation – from the outputs of these sensors. Since there are only four outputs, the amount of information transmitted to the controller is reduced (as compared to using a CCD sensor), and the reaction time of the robot is greatly decreased. In this work, the robot samples the motion sensors every 3.3 ms during the behavioral experiments. Received: 4 October 1999 / Accepted in revised form: 26 April 2001     

Machine Sharing in Manufacturing Systems: Total Flexibility versus Chaining

In this paper, we compare the operational performance of two machine-sharing configurations: total flexibility and chaining. We show that chaining captures most of the benefits of total flexibility while limiting the number of part types processed on any individual machine to only two. We examine the relative desirability of the two configurations under varying buffer sizes, loading conditions, number of machines, and setup times, as well as for different control policies. For nonzero setups times, we show that chained configurations can outperform fully flexible ones. This particularly is the case when either the number of machines or length of setup times is high. We also find that the effect of the system size on performance diminishes with the number of machines. This means that multiple smaller chains can perform almost as well as a single long one. Our results are consistent with the recent findings of Jordan and Graves (1995), who examined the economic benefits of chaining relative to full flexibility.     

Planning the routing mix in FASs to minimize total transportation time

The routing mix problem in flexible assembly systems is considered. The problem consists of assigning the operations for each part to the machines, with the two objectives of balancing the machine workloads and minimizing the burden of the transportation system. These two objectives are sometimes conflicting, since the latter tends to support assigning operations to the same machine(s) as much as possible, and this may be bad for workload balancing. A linear programming problem is presented that, given a constraint on the workload of each machine, finds one solution that minimizes the overall time spent moving the parts from one machine to another. Since such a linear program may have an exponential number of variables, an efficient column generation technique to solve the problem is devised. The efficiency of the method is validated by experiments on a large number of random problems.     

Information-driven self-organization: the dynamical system approach to autonomous robot behavior

In recent years, information theory has come into the focus of researchers interested in the sensorimotor dynamics of both robots and living beings. One root for these approaches is the idea that living beings are information processing systems and that the optimization of these processes should be an evolutionary advantage. Apart from these more fundamental questions, there is much interest recently in the question how a robot can be equipped with an internal drive for innovation or curiosity that may serve as a drive for an open-ended, self-determined development of the robot. The success of these approaches depends essentially on the choice of a convenient measure for the information. This article studies in some detail the use of the predictive information (PI), also called excess entropy or effective measure complexity, of the sensorimotor process. The PI of a process quantifies the total information of past experience that can be used for predicting future events. However, the application of information theoretic measures in robotics mostly is restricted to the case of a finite, discrete state-action space. This article aims at applying the PI in the dynamical systems approach to robot control. We study linear systems as a first step and derive exact results for the PI together with explicit learning rules for the parameters of the controller. Interestingly, these learning rules are of Hebbian nature and local in the sense that the synaptic update is given by the product of activities available directly at the pertinent synaptic ports. The general findings are exemplified by a number of case studies. In particular, in a two-dimensional system, designed at mimicking embodied systems with latent oscillatory locomotion patterns, it is shown that maximizing the PI means to recognize and amplify the latent modes of the robotic system. This and many other examples show that the learning rules derived from the maximum PI principle are a versatile tool for the self-organization of behavior in complex robotic systems.     

Production Capacity Modeling of Alternative, Nonidentical, Flexible Machines

Analyzing the production capacity of a flexible manufacturing system consisting of a number of alternative, nonidentical, flexible machines, where each machine is capable of producing several different part types simultaneously (by flexibly allocating its production capacity among these part types), is not a trivial task. The production capacity set of such a system is naturally expressed in terms of the machine-specific production rates of all part types. In this paper we also express it in terms of the total production rates of all part types over all machines. More specifically, we express the capacity set as the convex hull of a set of points corresponding to all possible assignments of machines to part types, where in each assignment each machine allocates all its capacity to only one part type. First, we show that within each subset of assignments having a given number of machines assigned to each part type, there is a unique assignment that corresponds to an extreme point of the capacity set. Then, we propose a procedure for generating all the extreme points and facets of the capacity set. Numerical experience shows that when the number of part types is less than four, the size of the capacity set (measured in terms of the number of variables times the number of constraints) is smaller, if the capacity set is expressed in terms of the total production rates of all part types over all machines than if it is expressed in terms of the machine-specific production rates of all part types. When the number of part types is four or more, however, the opposite is true.     

The robot and the satellite for tele-operating echographic examination in Earth isolated sites, or onboard ISS

The objective was to design and validate a method for tele-operating (from an expert site) an echographic examination in an isolated site. METHOD: The isolated places, defined as areas with reduced medical facilities, could be secondary hospitals 20 to 50 km from the university hospital, or dispensaries in Africa or Amazonia, or a moving structure like a rescue vehicle or the International Space Station (ISS). At the expert center, the ultrasound medical expert moves a fictive probe, connected to a computer (n degrees 1) which sends, the coordinate changes of this probe via an ISDN or satellite line to a second computer (n degrees 2), located at the isolated site, which applies them to the robotic arm holding the real echographic probe. RESULTS: The system was tested at Tours Hospital on 105 patients. A complete investigation (visualization) of all the organs requested for different clinical cases was obtained in 76% of the cases with the robot, and 87% at the reference echography: In 11% of the cases, at least one of the organ visualized at reference echo could not be investigated by the robot, thus the diagnostic was not done. The number of repositioning was higher for the robot (6.5 +/- 2) than for the reference echo (5.1 +/- 2 = or > 24% more with robot). The duration of the examination was higher with the robot (16 +/- 10 min) than for the reference echography (11 +/- 4 min = or > +43% with the robot compare to reference echography. The system was also tested successfully using satellite links in a limited number of cases (approx 30).     

Single cell deposition and patterning with a robotic system

Integrating single-cell manipulation techniques in traditional and emerging biological culture systems is challenging. Microfabricated devices for single cell studies in particular often require cells to be spatially positioned at specific culture sites on the device surface. This paper presents a robotic micromanipulation system for pick-and-place positioning of single cells. By integrating computer vision and motion control algorithms, the system visually tracks a cell in real time and controls multiple positioning devices simultaneously to accurately pick up a single cell, transfer it to a desired substrate, and deposit it at a specified location. A traditional glass micropipette is used, and whole- and partial-cell aspiration techniques are investigated to manipulate single cells. Partially aspirating cells resulted in an operation speed of 15 seconds per cell and a 95% success rate. In contrast, the whole-cell aspiration method required 30 seconds per cell and achieved a success rate of 80%. The broad applicability of this robotic manipulation technique is demonstrated using multiple cell types on traditional substrates and on open-top microfabricated devices, without requiring modifications to device designs. Furthermore, we used this serial deposition process in conjunction with an established parallel cell manipulation technique to improve the efficiency of single cell capture from ～80% to 100%. Using a robotic micromanipulation system to position single cells on a substrate is demonstrated as an effective stand-alone or bolstering technology for single-cell studies, eliminating some of the drawbacks associated with standard single-cell handling and manipulation techniques.     

RALPH application for surface mount assembly

Automated assembly in the electronics industry offers many advantages over traditional assembly methods. As product lives become shorter and batch numbers become smaller, new programming methods will be needed to shorten the lead time for these automated lines. Providing a method for humans to interact with the robot in a natural language mode will lead to some of these advantages. Task-level programming deals with the interactions of objects rather than robot motions and therefore requires little robotic expertise but allows for easy and fast programming. One of the long-term projects aimed at alleviating the above problem is the development of a Robotic Assembly Language Planning Hierarchy (RALPH), a task-level automatic robot programming language with natural language commands. In surface mount assembly, the PC board must be populated with chips with high tolerances—a few thousandths of an inch. The part features are so small that human assemblers constantly make mistakes. The redeeming aspect of the components with regard to assembly lies in the geometry of the features. In this article we capitalize on the above geometric attributes of chips and chip pads on PCBs in order to apply the principles of RALPH in the development of a system for automatic assembly.