Tamara Petrovic

Resource Allocation in Free-Choice Multiple Reentrant Manufacturing Systems Based on Machine-Job Incidence Matrix

In this paper, we introduce machine-job incidence (MJI) matrix that can be obtained from Steward sequencing matrix and Kusiak machine-part incidence matrix. Methods for determination of structural properties of free-choice multiple reentrant systems (FMRF) are proposed and an explanation on how the content (number of active jobs) of those structures can be controlled. This paper gives a new method on how to determine if allocation of resources, in a form of repeatable sequences, gives stable system behavior. Although efficiency of the proposed methods have been demonstrated on examples involving manufacturing workcells, the method can be used for other discrete-event systems as well, as long as the system under study belongs to FMRF class.

Modified Banker's algorithm for scheduling in multi-AGV systems

In todays highly complex multi-AGV systems key research objective is finding a scheduling and routing policy that avoids deadlock while assuring that vehicle utilization is as high as possible. It is well known that finding such an optimal policy is a NP-hard task in general case. Therefore, big part of the research is oriented towards finding various suboptimal policies that can be applied to real world plants. In this paper we propose modified Bankers algorithm for scheduling in multi-AGV systems. A predetermined missions path is executed in a way that some non-safe states are allowed in order to achieve better utilization of vehicles. A graph-based method of polynomial complexity for verification of these states is given. Algorithm is tested on a layout of a real plant for packing and warehousing palettes. Results shown at the end of the paper demonstrate advantages of the proposed method compared with other methods based on Bankers algorithm.

Decentralized control of free ranging AGVs in warehouse environments

In this paper we propose an algorithm for decentralized control of Automated Guided Vehicles (AGVs) operating in automated warehouse environments. The motion planning part of the algorithm provides vehicles with capabilities for autonomous motion planning considering nonholonomic vehicle constraints and collision-free path execution. The decision making part of the algorithm ensures safe vehicle motions and reliable conflict situation resolution. The proposed control algorithm also prevents occurrence of deadlock and livelock situations. Stability of the algorithm has been proven by its analysis based on automata theory, while its performance has been validated by simulation on a system comprising twenty vehicles as well as experimentally on a laboratory setup comprising five Pioneer 3DX vehicles.

Aerial-ground robotic system for autonomous delivery tasks

In this paper we present a study of a robotic system that consists of an unmanned aerial vehicle equipped with a pair of manipulator arms (MMUAV), and unmanned ground vehicles (UGVs). The envisioned application scenario includes autonomous packet transportation, where MMUAV is used for picking/placing packets, while both MMUAV and UGV can be used for packet transportation, with different energy consumption profiles. We propose a reactive method for decentralized task planning and coordination of robots using hierarchical task decomposition based on TÆMS framework. Our approach takes into account low-level motion-planning aspects of the system as well as high-level mission specification, making this a multi-layered system. For low-level planning we use sampling-based planner combined with obstacle-free trajectory generation. Methods are verified in simulations and on an experimental testbed, using 3D Robotics quadcopter and Pioneer 3DX mobile robots with the results showing stability and robustness of the presented methods.

Can UAV and UGV Be Best Buddies

This paper presents the results of our efforts to build a heterogeneous robotic system capable of executing complex disaster response and recovery tasks. We aim to explore high level task scheduling and mission planning algorithms that enable various types of robots to cooperate together, utilizing each others strengths to yield a symbiotic robotic system. In the proposed scenario, a ground vehicle and an aerial robot work together to close a valve in a disaster stricken industrial environment. To that end we use TÆMS framework in order to specify interrelationships between mission subtasks and develop an effective scheduling and coordination mechanism, inspired by Generalized Partial Global Planning. We present simulation results with two different outcomes that show cooperative capabilities of the system.

Matrix-based sequencing in multiple re-entrant flowlines

In this article, we address issues related to modelling of a discrete event system in a way that would effectively provide an easy implementation of the scheduling policy on the actual controller and at the same time would provide an answer on how profound the utilization of the system resources is. Herein we propose a method that relies on usage of a matrix model and a max-plus algebra model. The idea is to use the advantages of each of the given models. A system, initially given in matrix form, can be transferred into a max-plus model under the given sequence. In the case of structural changes, the system matrix model can be easily modified and hence supervisory matrix controller redesign is straightforward. On the other hand, the max-plus model is suitable for performance analysis, which in some cases could be difficult if a matrix model is used. Moreover, max-plus algebra represents the system on the higher level of abstraction, which is suitable for investigation of particular properties of discrete event systems. In addition, using both models, we propose a method for determining a scheduling sequence that is not well formed.

Matrix-based approach to sequence analysis in multiple reentrant flowlines

This paper presents a method for extension of matrix model of manufacturing system in order to provide an efficient tool for analysis of systems with various dispatching sequences of shared resources. Proposed method is used for transformation of system matrices in linear max-plus model. Once the linear model is determined sequence feasibility can be checked. Furthermore, the method provides a straightforward procedure for production cycle calculation and resource utilisation. Efficiency of presented technique is demonstrated on a manufacturing system example at the end of the paper.

A modular control system for warehouse automation - algorithms and simulations in USARSim

In this paper, we present a control system for a fully autonomous material handling facility. The scenario we are considering is motivated by the 2011 IEEE Virtual Manufacturing Automation Challenge (VMAC). It consists of multiple autonomously guided vehicles (AGVs), transporting pallets of various goods between several input and output locations, through an unstructured warehouse environment. Only a map of the warehouse and a pallet delivery list are provided a priori. Pallets must be delivered to the output locations in the shortest time possible, while respecting the ordering of different pallet types specified by the delivery list. The presented control system handles all aspects of warehouse operation, from individual vehicle control to high-level mission planning and coordination. Delivery mission assignments are optimized using dynamic programming and simulated annealing techniques. Mission executions are coordinated using graph search methods and a modified version of the Bankers algorithm, to ensure safe, collision and deadlock-free system operation. System performance is evaluated on a virtual warehouse model, using the high fidelity USARSim simulator.

Machine-job incidence matrix based analysis of manufacturing systems in time domain

This paper presents an extension of previously introduced method for manufacturing systems analysis and design based on so called Machine-Job Incidence matrix. Static model of a manufacturing system is augmented by introduction of timed machine-job incidence matrix that is comprised of operational times required for jobs in the system to be completed as well as times required for setting-up of resources upon completion of requested tasks. The proposed dynamic model has been validated by simulation of free-choice multiple reentrant flow line. Results presented in the paper demonstrate efficiency of the model.

Robot workstation failure recovery based on a layout optimization

This article focuses on the robot workstation layout problem and briefly discusses a recovery control strategy. Since present industrial workstations utilize a flexible manufacturing cell served by a robot, researchers in this field try to find the best method determining the physical organization of resources in available space. As solving the facility layout problem (FLP) might reduce material handling expenses, the most common objective in these approaches is to minimize the material handling costs. Our work introduces a new approach in obtaining the optimal positions of resources in a robot workstation where considerable contribution to the final layout design comes from the failure recovery data. The optimization criteria include material flow and transportation cost as the standard FLP objectives. In our approach we also consider the resource rate of failure and treatment quality as a part of the failure recovery. The optimization problems were solved with the state of the art optimization algorithm for the nonlinear optimization problems. The computational results of the study are discussed and analysed on the basis of a real industrial application. The commonly used objective function is compared to the proposed objective function extended with the failure recovery. As an important part of the failure recovery strategy, making the proper recovery decision in the workstation control design is also discussed.