393 research outputs found
A Review Of Design And Control Of Automated Guided Vehicle Systems
This paper presents a review on design and control of automated guided vehicle systems. We address most key related issues including guide-path design, estimating the number of
vehicles, vehicle scheduling, idle-vehicle positioning, battery management, vehicle routing, and conflict resolution. We discuss and classify important models and results from key publications in literature on automated guided vehicle systems, including often-neglected areas, such as idle-vehicle positioning and battery management. In addition, we propose a decision framework for design and implementation of automated guided vehicle systems, and suggest some fruitful research directions
Control of free-ranging automated guided vehicles in container terminals
Container terminal automation has come to the fore during the last 20 years to improve their efficiency. Whereas a high level of automation has already been achieved in vertical handling operations (stacking cranes), horizontal container transport still has disincentives to the adoption of automated guided vehicles (AGVs) due to a high degree of operational complexity of vehicles. This feature has led to the employment of simple AGV control techniques while hindering the vehicles to utilise their maximum operational capability. In AGV dispatching, vehicles cannot amend ongoing delivery assignments although they have yet to receive the corresponding containers. Therefore, better AGV allocation plans would be discarded that can only be achieved by task reassignment. Also, because of the adoption of predetermined guide paths, AGVs are forced to deploy a highly limited range of their movement abilities while increasing required travel distances for handling container delivery jobs. To handle the two main issues, an AGV dispatching model and a fleet trajectory planning algorithm are proposed. The dispatcher achieves job assignment flexibility by allowing AGVs towards to container origins to abandon their current duty and receive new tasks. The trajectory planner advances Dubins curves to suggest diverse optional paths per origin-destination pair. It also amends vehicular acceleration rates for resolving conflicts between AGVs. In both of the models, the framework of simulated annealing was applied to resolve inherent time complexity. To test and evaluate the sophisticated AGV control models for vehicle dispatching and fleet trajectory planning, a bespoke simulation model is also proposed. A series of simulation tests were performed based on a real container terminal with several performance indicators, and it is identified that the presented dispatcher outperforms conventional vehicle dispatching heuristics in AGV arrival delay time and setup travel time, and the fleet trajectory planner can suggest shorter paths than the corresponding Manhattan distances, especially with fewer AGVs.Open Acces
Simulation in Automated Guided Vehicle System Design
The intense global competition that manufacturing companies face today results in an
increase of product variety and shorter product life cycles. One response to this threat is
agile manufacturing concepts. This requires materials handling systems that are agile
and capable of reconfiguration. As competition in the world marketplace becomes
increasingly customer-driven, manufacturing environments must be highly
reconfigurable and responsive to accommodate product and process changes, with rigid,
static automation systems giving way to more flexible types.
Automated Guided Vehicle Systems (AGVS) have such capabilities and AGV
functionality has been developed to improve flexibility and diminish the traditional
disadvantages of AGV-systems. The AGV-system design is however a multi-faceted
problem with a large number of design factors of which many are correlating and
interdependent. Available methods and techniques exhibit problems in supporting the
whole design process. A research review of the work reported on AGVS development in
combination with simulation revealed that of 39 papers only four were industrially
related. Most work was on the conceptual design phase, but little has been reported on
the detailed simulation of AGVS.
Semi-autonomous vehicles (SA V) are an innovative concept to overcome the problems
of inflexible -systems and to improve materials handling functionality. The SA V
concept introduces a higher degree of autonomy in industrial AGV -systems with the
man-in-the-Ioop. The introduction of autonomy in industrial applications is approached
by explicitly controlling the level of autonomy at different occasions. The SA V s are
easy to program and easily reconfigurable regarding navigation systems and material
handling equipment. Novel approaches to materials handling like the SA V -concept
place new requirements on the AGVS development and the use of simulation as a part
of the process. Traditional AGV -system simulation approaches do not fully meet these
requirements and the improved functionality of AGVs is not used to its full power.
There is a considerflble potential in shortening the AGV -system design-cycle, and thus
the manufacturing system design-cycle, and still achieve more accurate solutions well
suited for MRS tasks.
Recent developments in simulation tools for manufacturing have improved production
engineering development and the tools are being adopted more widely in industry. For
the development of AGV -systems this has not fully been exploited. Previous research
has focused on the conceptual part of the design process and many simulation
approaches to AGV -system design lack in validity. In this thesis a methodology is
proposed for the structured development of AGV -systems using simulation. Elements of
this methodology address the development of novel functionality.
The objective of the first research case of this research study was to identify factors for
industrial AGV -system simulation. The second research case focuses on simulation in
the design of Semi-autonomous vehicles, and the third case evaluates a simulation based
design framework. This research study has advanced development by offering a
framework for developing testing and evaluating AGV -systems, based on concurrent
development using a virtual environment. The ability to exploit unique or novel features
of AGVs based on a virtual environment improves the potential of AGV-systems
considerably.University of Skovde. European Commission for funding the INCO/COPERNICUS Projec
Traffic management and control of automated guided vehicles using artificial neural networks
An industrial traffic management and control system based on Automated Guided Vehicles faces
several combined problems. Decisions must be made concerning which vehicles will respond, or are
allocated to each of the transport orders. Once a vehicle is allocated a transport order, a route has to
be selected that allows it to reach its target location. In order for the vehicle to move efficiently along
the selected route it must be provided with the means to recognise and adapt to the changing
characteristics of the path it must follow. When several vehicles are involved these decisions are
interrelated and must take into account the coordination of the movements of the vehicles in order to
avoid collisions and maximise the performance of the transport system. This research concentrates on
the problem of routing the vehicles that have already been assigned destinations associated with
transport orders.
In nearly all existing AGV systems this problem is simplified by considering there to be a fixed route
between source and destination workstations. However if the system is to be used more efficiently,
and particularly if it must support the requirements of modern manufacturing strategies, such as Justin-
Time and Flexible Manufacturing Systems, of moving very small batches more frequently, then
there is a need for a system capable of dealing with the increased complexity of the routing problem.
The consideration of alternative paths between any two workstations together with the possibility of
other vehicles blocking routes while waiting at a particular location, increases enormously the number
of alternatives that must be considered in order to identify the routes for each vehicle leading to an
optimum solution. Current methods used to solve this type of problem do not provide satisfactory
solutions for all cases, which leaves scope for improvement. The approach proposed in this work
takes advantage of the use of Backpropagation Artificial Neural Networks to develop a solution for the
routing problem. A novel aspect of the approach implemented is the use of a solution derived for
routing a single vehicle in a physical layout when some pieces of track are set as unavailable, as the
basis for the solution when several vehicles are involved. Another original aspect is the method
developed to deal with the problem of selecting a route between two locations based on an analysis of
the conditions of the traffic system, when each movement decision has to be made. This lead to the
implementation of a step-by-step search of the available routes for each vehicle.
Two distinct phases can be identified in the approach proposed. First the design of a solution based on
an ANN to solve the single vehicle case, and subsequently the development and testing of a solution
for a multi-vehicle case. To test and implement these phases a specific layout was selected, and an
algorithm was implemented to generate the data required for the design of the ANN solution.
During the development of alternative solutions it was found that the addition of simple rules provided
a useful means to overcome some of the limitations of the ANN solution, and a "hybrid" solution was
originated. Numerous computer simulations were performed to test the solutions developed against
alternatives based on the best published heuristic rules. The results showed that while it was not
possible to generate a globally optimal solution, near optimal solutions could be obtained and the best
hybrid solution was marginally better than the best of the currently available heuristic rules
A Comprehensive Overview of Classical and Modern Route Planning Algorithms for Self-Driving Mobile Robots
Mobile robots are increasingly being applied in a variety of sectors, including agricultural, firefighting, and search and rescue operations. Robotics and autonomous technology research and development have played a major role in making this possible. Before a robot can reliably and effectively navigate a space without human aid, there are still several challenges to be addressed. When planning a path to its destination, the robot should be able to gather information from its surroundings and take the appropriate actions to avoid colliding with obstacles along the way. The following review analyses and compares 200 articles from two databases, Scopus and IEEE Xplore, and selects 60 articles as references from those articles. This evaluation focuses mostly on the accuracy of the different path-planning algorithms. Common collision-free path planning methodologies are examined in this paper, including classical or traditional and modern intelligence techniques, as well as both global and local approaches, in static and dynamic environments. Classical or traditional methods, such as Roadmaps (Visibility Graph and Voronoi Diagram), Potential Fields, and Cell Decomposition, and modern methodologies such as heuristic-based (Dijkstra Method, A* Algorithms, and D* Algorithms), metaheuristics algorithms (such as PSO, Bat Algorithm, ACO, and Genetic Algorithm), and neural systems such as fuzzy neural networks or fuzzy logic (FL) and Artificial Neural Networks (ANN) are described in this report. In this study, we outline the ideas, benefits, and downsides of modeling and path-searching technologies for a mobile robot
The investigation of the effect of scheduling rules on FMS performance
The application of Flexible Manufacturing Systems (FMSs) has an effect in competitiveness, not only
of individual companies but of those countries whose manufactured exports play a significant part in
their economy (Hartley, 1984). However, the increasing use of FM Ss to effectively provide customers
with diversified products has created a significant set of operational challenges for managers
(Mahmoodi et al., 1999). In more recent years therefore, there has been a concentration of effort on
FMS scheduling without which the benefits of an FMS cannot be realized.
The objective of the reported research is to investigate and extend the contribution which can be made
to the FMS scheduling problem through the implementation of computer-based experiments that
consider real-time situations. [Continues.
SLAM research for port AGV based on 2D LIDAR
With the increase in international trade, the transshipment of goods at international container ports is very busy. The AGV (Automated Guided Vehicle) has been used as a new generation of automated container horizontal transport equipment. The AGV is an automated unmanned vehicle that can work 24 hours a day, increasing productivity and reducing labor costs compared to using container trucks. The ability to obtain information about the surrounding environment is a prerequisite for the AGV to automatically complete tasks in the port area. At present, the method of AGV based on RFID tag positioning and navigation has a problem of excessive cost. This dissertation has carried out a research on applying light detection and ranging (LIDAR) simultaneous localization and mapping (SLAM) technology to port AGV. In this master's thesis, a mobile test platform based on a laser range finder is developed to scan 360-degree environmental information (distance and angle) centered on the LIDAR and upload the information to a real-time database to generate surrounding environmental maps, and the obstacle avoidance strategy was developed based on the acquired information. The effectiveness of the platform was verified by the experiments from multiple scenarios. Then based on the first platform, another experimental platform with encoder and IMU sensor was developed. In this platform, the functionality of SLAM is enabled by the GMapping algorithm and the installation of the encoder and IMU sensor. Based on the established environment SLAM map, the path planning and obstacle avoidance functions of the platform were realized.Com o aumento do comĂ©rcio internacional, o transbordo de mercadorias em portos internacionais de contentores Ă© muito movimentado. O AGV (âAutomated Guided Vehicleâ) foi usado como uma nova geração de equipamentos para transporte horizontal de contentores de forma automatizada. O AGV Ă© um veĂculo nĂŁo tripulado automatizado que pode funcionar 24 horas por dia, aumentando a produtividade e reduzindo os custos de mĂŁo-de-obra em comparação com o uso de camiĂ”es porta-contentores. A capacidade de obter informaçÔes sobre o ambiente circundante Ă© um prĂ©-requisito para o AGV concluir automaticamente tarefas na ĂĄrea portuĂĄria. Atualmente, o mĂ©todo de AGV baseado no posicionamento e navegação de etiquetas RFID apresenta um problema de custo excessivo. Nesta dissertação foi realizada uma pesquisa sobre a aplicação da tecnologia LIDAR de localização e mapeamento simultĂąneo (SLAM) num AGV. Uma plataforma de teste mĂłvel baseada num telĂ©metro a laser Ă© desenvolvida para examinar o ambiente em redor em 360 graus (distĂąncia e Ăąngulo), centrado no LIDAR, e fazer upload da informação para uma base de dados em tempo real para gerar um mapa do ambiente em redor. Uma estratĂ©gia de prevenção de obstĂĄculos foi tambĂ©m desenvolvida com base nas informaçÔes adquiridas. A eficĂĄcia da plataforma foi verificada atravĂ©s da realização de testes com vĂĄrios cenĂĄrios e obstĂĄculos. Por fim, com base na primeira plataforma, uma outra plataforma experimental com codificador e sensor IMU foi tambĂ©m desenvolvida. Nesta plataforma, a funcionalidade do SLAM Ă© ativada pelo algoritmo GMapping e pela instalação do codificador e do sensor IMU. Com base no estabelecimento do ambiente circundante SLAM, foram realizadas as funçÔes de planeamento de trajetĂłria e prevenção de obstĂĄculos pela plataforma
Robots learn to behave: improving human-robot collaboration in flexible manufacturing applications
L'abstract Ăš presente nell'allegato / the abstract is in the attachmen
Metaphor-based negotiation and its application in AGV movement planning
The theme of this thesis is "metaphor-based negotiation". By metaphor-based negotiation I mean a category of approaches for problem-solving in Distributed Artificial
Intelligence (DAI) that mimic some aspects of human negotiation behaviour. The
research in this dissertation is divided into two closely related parts. Cooperative interaction among agents in a multiagent system (MAS) is discussed in general, and
the discussion leads to a formal definition of metaphor-based negotiation. Then, as
a specific application, a "spring-based" computational model for metaphor-based negotiation is developed as an approach to solving movement planning, specifically the
AGV scheduling problem (AGVSP) â determing the timings of AGVs' activities, of
automated guided vehicles (AGVs) in a factory.By formally addressing the multi-agent cooperative interaction problem and assuming
that agents in a MAS are rational, benevolent and fully informed, an initial strategy
set of cooperative interaction can be reduced to a strategy set by eliminating strategies
that are irrational in a group sense. However, it is proved in this dissertation that, in
the remaining strategy set, no unique strategy can be found that is acceptable to all
agents according their individual preferences. More specifically, in this smaller strategy
set, if one agent moves from one strategy to another in an attempt to better its individual goal achievement, then there is at least one agent whose goal achievement will
be negatively affected by such a move. So, the cooperative interaction problem can
only be partially solved if no further knowledge is given to those agents. The idea of a
common sense principle is introduced in this dissertation to overcome the deficiencies
of the assumptions of rationality, benevolence and full-informedness.In reality, the assumption of full-informedness of agents may not be practical. Communication is needed for agents to (1) exchange their local problem solving information,
and (2) exchange proposals for global problem solving, when their views are in conflict.
Based on the discussion of cooperative interaction, a formal definition of metaphorbased
negotiation is proposed to formally indicate what is a proposal and what is the
condition for accepting a proposal from another agent. In this definition, the common
sense principle is one of the most important features, not found in definitions of negotiation available so far in the literature, which guides agents to find an agreement
when negotiation is running into difficulties.The AGVSP involves timing activities for each AGV in a AGV-based factory. The
AGVSP is naturally distributed: the whole problem can be easily divided into several
subproblems each of which involves timing of activities of one AGV. Therefore, it is
intuitively straightforward for us to seek DAI approaches to solving the AGVSP. In
spired by Kwa's Iterative Negotiation Model [Kwa 88b] [Kwa 88a] for the AGVSP, we
developed a spring-based (metaphor-based) negotiation model for the AGVSP to overcome some vital problems in Kwa's model. The idea of the spring-based negotiation
model is described below:The AGVSP can be regarded as a Distributed Constraint Satisfaction Problem (DCSP)
and solved in a MAS. Each agent in the MAS is designed to solve a subproblem â a
local scheduling problem which is a small Constraint Satisfaction Problem (CSP). Conflicts exist when intra-agent constraints or inter-agent constraints are violated. These
constraints can be classified into hard constraintsâ those that can not be relaxed at
the agent level unless the system designer permits (e.g., by providing an arbitrator),
and soft constraints â those that can be relaxed at the agent level when necessary.
When agents are in conflict, i.e, when some inter-agent constraints are violated (or
say, when one agent's timings of its activities overlap those of some other agents),
these agents involved will resolve the conflicts through a (metaphor-based) negotiation
procedure in which conflicts will be gradually resolved by each agent's relaxation of
its intra-agent constraints, i.e, by yielding some amount of its initially allocated resources to other agents or by shifting its initially allocated resources. The negotiation
can be viewed as a process of exchanging proposals (of cooperative strategies) between
conflicting agents, where a cooperative strategy is a possible resolution to a conflict
according to the viewpoint of the proposing agent. However, since agents are designed
to be rational, each agent that is involved in the conflicts will try hard to relax its
intra-agent constraints as little as possible. Further, it is reasonably acceptable that
the more an intra-agent constraint has been relaxed the less the respective agent is
willing to relax it further. This feature can be modeled by a spring â the more it
has been compressed the harder it is to compress it further. Based on this inspiration,
a spring-based computational model of metaphor-based negotiation is proposed: each
agent's local schedule is represented by a local spring network in which each spring element represents a soft intra-agent constraint. Relaxation of an intra-agent constraint
is likened to a spring being compressed by external forces from other agents. As a
consequence, the compressed spring will also show a reacting force upon those compressing agents. An agreement will be reached when those forces and reacting forces
are balanced. This is the common sense principle in the spring-based negotiation. The
model solves some key issues, e.g., how to select negotiation techniques and skills during the process of negotiation, that have not been solved by Kwa's iterative negotiation
model. Some experimental evidence of the value of this model is presented
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