Transportation to solve intralogistics is becoming as complex as managing transportation in road logistics. Luckily, it takes place in structured environments where automation is easier. This work is about solving the task assignment problem for a group of automated guided vehicles (AGVs) serving the internal transport of a warehouse or factory. Instead of having a centralized task planner, we use an agent-based approach where agents represent all the stakeholders in the transport system. Namely, the clients are the transport orders and the taxis, the AGVs. We have modeled client and taxi behaviors by using extended finite-state stack machines (EFS2Ms) because they enable both modeling belief-desire-intention (BDI) agents and lower-level controllers. As a result, agent software is produced in a systematic way and, what is more, analyses of different working conditions can be done to fine-tune parameters of the models to achieve an efficient transportation. Results on one realistic study-case show that average service times can be shortened with respect to fixed planning and that this system can operate in legacy systems.

Industrial plants are distributed systems whose automation requires complex controllers. Networks of state machines model these controller systems but their syntheses remain a problem. In this paper, we approach it by combining extended finite state machines (EFSM) with state stacks. While EFSM match most design templates, extended finite state stack machines (EFS2M) accommodates to procedural agent architectures for modules at higher-abstraction levels of the controllers' networks. Results include a code generator from state machine diagrams and a simulator of a mobile robot for internal transportation whose controller stack includes EFSM and EFS2M.

Efficient control of systems based on automated guided vehicles requires accurate estimation of cost parameters such as speed, energy, task completion performance, et cetera. These parameters change along time, particularly in battery-operated robots. This work addresses the problem of on-line cost parameter identification and estimation which will be beneficial for proper control decisions of the individual mobile robots and for the system as a whole. Several filtering and estimation methods have been investigated with respect to travelling times, as they are considered key cost parameters. Results show that these parameters depend on the robot, the route and the moment, so they are linked to a particular robot, a region of the floor and a time period (or to a battery level). In fact, differences with static, pre-runtime travelling time computations, either heuristically or by characterization of real robots, are large enough to affect system's performance and overall productivity and efficiency.

Mobile robots are used in manufacturing workshops and storage plants to automate internal logistics. A model of a manufacturing plant which is representative of the forthcoming flexible systems is presented in this paper. It has been implemented in physical simulator V-Rep, with the KUKA youBot robot as the unit to perform material transportation and handling. This implementation has been used to create a virtual laboratory for students to learn about embedded robot controllers. Results show that the model is robust and that part of the system complexity must be transparent to users to focus on system design issues. Furthermore, the virtual laboratory has been used to explore some approaches to develop navigation control with minimal infrastructure support and other embedded software, which proves its validity as a research tool, too.

Automated guided vehicles (AGVs) are increasingly being used in industrial facilities to improve efficiency of internal transportation processes. In this work, we have analyzed what are the requirements for AGV systems and made a correspondence to our controller architecture, which relies on multi-agent systems. We show that, by using our agent-based model, it is possible to rapidly deploy AGV system controllers and attain higher degrees of robustness and flexibility than by using conventional approaches.

Conveyors are cheaper, simpler and faster than automated guided vehicles (AGVs), but the latter have become an affordable option with pluses like flexibility and adaptability. In this work, we have explored how AGVs can replace a conveyor system in an automated laboratory for clinical analyses. Often, these facilities feature a conveyor system to transport samples from one machine to another at the highest rates possible. We have simulated a plant where tube racks with samples are transported by AGVs to see if the overall throughput achieves the maximum capacity of analyzers. Simulation results show that it is possible to achieve the same production yield with AGVs whereas the robustness and adaptability are higher.

Thousands of sample measurements for clinical analyses in large hospitals are made every day, which requires laboratories with modular analyzer systems that can operate with reliability and adaptability. In this paper we propose to transform a conventional, centrally controlled laboratory facility to a multi-agent system in order to distribute the control and make it more adaptable to priority samples and other unexpected events. The conversion requires replacing passive sample transport systems of laboratories by colonies of robots, which become the transportation agents. These multi-agent systems are completed by agents for the analyzers and the ones related to management software of laboratories. Result systems are compatible with the conventional ones so laboratories could be transformed in a straightforward way.

Learning activities' guides tell students which tasks and when they should be carried out to attain the educational goals of corresponding courses. In this paper, an easy to use framework for developing and using these guides is presented. Results of its use show that students are satisfied with them but that there is room for improvement in augmenting their capabilities.

The ever growing complexity of multi-robot systems and of robot themselves result in an increasing degree of uncertainty and unpredictability. In this work, the multi-agent approach to design such systems is adapted so behaviors of agents are described in terms of abstract state machines. By constraining agent behaviors to deterministic machines, verification of resulting systems is expected to be more effective and, as a by-product, implementation of physical agents is more straightforward. In this paper, the design framework is described as well as some preliminary tests to prove the viability of this approach.

The introduction of the European Credit Transfer System (ECTS) has fostered a number of changes in the way courses are given in the university. In this paper we propose to present subjectpsilas syllabuses as learning activitiespsila agendas to students. Hence, they not only grasp course programs easier but are more aware of their own work, which is essential to learning evaluation within ECTS. Learning agendas have been used in a Computer Fundamentals course within Computer Science degree since year 2006/07. Results show that students consult them regularly and that overall academic yield have been progressively increasing.

ECL is a system design language suitable for heterogeneous, reactive systems, but there are no tools to directly compile the data-flow parts into hardware. In this paper, we propose several approaches to synthesizing these parts into hardware without extending the language. As a consequence, the resulting environment enables a true HW/SW codesign for small and medium-size systems.

This paper presents a method to automatically recognize and model singleand multi-output logic gates out of a switch-level network, even for irregular transistor structures. Resulting subcircuit models are directly used in a symbolic simulator for circuit analysis purposes. Other applications of derived netlists cover switch-level simulation acceleration and test generation tool enhancement.

Incremental methods are successfully applied to deal with successive verifications of slightly modified switch-level networks. That is, only those parts affected by the changes are symbolically traversed for verification. In this paper, we present an incremental technique for symbolic simulators which is inspired in both existing incremental techniques for non-symbolic simulators and a token-passing mechanisms in Petri nets.

  This paper presents a symbolic method to detect short and open circuit faults in switch-level networks.Detection and fault sensitization vector determination are possible since the behavior of each node is described by a set of two functions: the on-set and the off-set functions.Their analyses provide designers with an efficient tool for circuit verification and test pattern generation.

In CMOS logic circuits, stuck-on and bridging faults are of special importance. This paper presents the theoretical foundations of a symbolic method to detect their presence and, implicitly, offer a test vector to sensitize the fault being therefore measurable through IDDQ techniques.

Designing an optimal clocking scheme for a sequential circuit requires accurate knowledge of the delay of its longest sensitizable and of its shortest destabilizing path. However, there is a large imbalance in the research effort spent on both types of paths. This paper tries to compensate this imbalance, by presenting a new destabilizing criterion, for which it will be proved that it leads to correct optimal clocking schemes. Furthermore, this paper results in clocking schemes which are tighter in comparison with those presented in literature.>