523 research outputs found
Optimized Direction Assignment in Roadmaps for Multi-AGV Systems Based on Transportation Flows
No full textCoordinazione del Traffico per sistemi AGV: un approccio completo per la modellazione
No full textIl tema della tesi consiste nello sviluppo di strategie globali per l'ottimizzazione delle performance in sistemi multi-robot. In particolare, si sono analizzati e sviluppati metodi per la coordinazione, l'ottimizzazione e la gestione del traffico di una flotta di automated guided vehicles (AGV) operanti in ambienti industriali.
Il lavoro di tesi ha visto per primo lo sviluppo di un algoritmo per la creazione automatica delle roadmap per sistemi AGV, massimizzandone la connettività, la ridondanza e l'indice di copertura. La roadmap generata è tale che l'ambiente risulti riempito dal massimo numero possibile di strade, mentre le direzioni sono assegnate ottimizzando la connettività del grado associato.
In seguito, è stato sviluppato un coordinatore del traffico che garantisce ottime prestazioni qualora usato per coordinare la flotta su roadmap generate con il metodo automatico.
Si sono analizzati due metodi di coordinazione tra veicoli: decentralizzato e centralizzato. Il primo si basa su uno schema di priorità con lo scopo di allocare risorse, nel secondo il problema è affrontato tramite una modellazione di programmazione quadratica (QP) dove si minimizza il tempo totale per completare le missioni della flotta.
A fianco del coordinatore, si è sviluppata una architettura di controllo basata su due livelli. La natura gerarchica permette così di ridurre la complessità totale del problema. Il primo livello (più astratto) è un grafo topologico rappresentate la roadmap e l'ambiente. Ogni nodo è una area specifica della mappa chiamata settore. Il secondo livello rappresenta la roadmap vera e propria all'interno dei singoli settori. Tale metodo permette di modellare il sistema a parametri concertati e il traffico è così racchiuso solo in alcune aree.
In fine, si è sviluppato un modello probabilistico del traffico tra i vari settori, che permette di predirne l'evoluzione all'interno di un certo orizzonte temporale. Ciò è inglobato in un pianificatore di percorsi che tiene in conto lo stato del traffico e coordina la flotta minimizzandone il tempo totale necessario per il completamento delle missioni.
A supporto della tesi, è stata condotta una intensa campagna di validazione sia tramite simulazioni che esperimenti relativi a scenari realistici, nello specifico, a magazzini automatici.This thesis deals with an ensemble strategy for optimizing the overall performance of multi-robot systems. Specifically, methodologies are presented for the coordination, optimization and traffic management of a fleet of automated guided vehicles (AGV) operating in industrial environments.
This dissertation presents first an algorithm for the automatic creation of a roadmap for AGV systems which maximizes the connectivity, the redundancy and the coverage. The roadmap is built in such a way that the environment is filled by as roads as possible whose directions are assigned by maximizing the connectivity of the associated graph.
A traffic coordinator is then developed in such a way that the optimal performance is guaranteed when it is used with a roadmap generated with the previous method. The coordination is performed by means of both decentralized and centralized control policies. The former is based on a priority scheme for the resource allocation, the latter is performed by modeling the coordination problem as a quadratic programming problem (QP) by aiming at minimizing the total time required for the fleet in order to accomplish its tasks.
Along with the traffic coordinator, a hierarchical 2-layers control architecture is developed. The architecture exploits two layers to manage the problem. A layer is a topological graph representing the roadmap where each node is an area of the roadmap called sector. Then the other layer represents the actual roadmap within each sector. In this way, the overall scenario is modeled as a lumped parameter model where the traffic congestions and then the complexity are bounded in specific areas.
Furthermore a probabilistic dynamic model of the traffic is introduced. The model is used to predict the evolution of the traffic in a future horizon. This information is then exploited by a traffic-based planner which coordinated the vehicles in order to minimize the total arrival time.
The methodologies are supported by simulations and experiments in real world scenario, specifically in real automatic warehouses
A Probabilistic Eulerian Traffic Model for the Coordination of Multiple AGVs in Automatic Warehouses
No full textThe co-ordination of multiple automated guided vehicles (AGVs) is one of the main issues to address for the implementation of an efficient autonomous warehouse. Traffic jams are highly undesirable and, therefore, the motion of the AGVs should be planned by considering the current and future state of the fleet. This letter proposes a probabilistic model of the traffic in an autonomous warehouse to predict the evolution of possibly congested areas. Such a model is then exploited for building a predictive planner that is embedded in the traffic manager recently proposed in [1] to explicitely consider the evolution of the traffic up to a given horizon and to increase the efficiency of the fleet of AGVs in terms of delivery time. The proposed traffic manager is validated by means of comparative simulations on real plants
Optimized simultaneous conflict-free task assignment and path planning for multi-AGV systems
No full textIn this paper we address the problem of assigning a set of tasks to a set of Automated Guided Vehicles (AGVs), in a conflict-free manner. Specifically, we consider a system of multiple AGVs, moving along a predefined roadmap, and utilized for transportation of goods in automated warehouses. Sequential application of task assignment and path planning often gives rise to pathological situations, such as deadlocks, in which AGVs block each other, thus preventing tasks completion. In this paper we propose a method for assigning tasks while taking into account the subsequent path planning, encoding possible conflicts into a conflict graph, that is subsequently utilized for defining constraints of an optimization problem. Simulations are performed on maps of real industrial environments, to compare the proposed method with traditional task assignment
Hierarchical traffic control for partially decentralized coordination of multi AGV systems in industrial environments
No full textThis paper deals with decentralized coordination of
Automated Guided Vehicles (AGVs). We propose a
hierarchical traffic control algorithm, that
implements path planning on a two layer
architecture. The high-level layer describes the
topological relationships among different areas of
the environment. In the low-level layer, each area
includes a set of fixed routes, along which the AGVs
have to move. An algorithm is also introduced for
the automatic definition of the route map
itself. The coordination among the AGVs is obtained
exploiting shared resources (i.e. centralized
information) and local negotiation
(i.e. decentralized coordination). The proposed
strategy is validated by means of simulations using
real plan
Towards decentralized coordination of multi robot systems in industrial environments: A hierarchical traffic control strategy
No full textThis paper describes an innovative approach to manage multiple Automated Guided Vehicles (AGVs) in an industrial environment. The proposed approach is based on a two layer architecture for path planning. This architecture consists of a topological layer, composed by macro-cells, and of a route map layer in which the AGVs have to move along fixed paths. The traffic is managed in a decentralized manner. Each AGV computes autonomously its own path both at topological layer and at route map layer. The coordination among the AGVs is based on the negotiation of shared resources. An early phase of validation is provided by the simulation in a structured environment
An automatic approach for the generation of the roadmap for multi-AGV systems in an industrial environment
No full textThis paper deals with the automatic generation of a
roadmap. We propose an approach to build a roadmap
for Automated Guided Vehicles (AGVs) used for
logistics operations in industrial environments. The
algorithm computes a roadmap in such a way that the
coverage, the connectivity and the redundancy of the
paths are maximized. In this way the flexibility and
the efficiency of the AGV system can be
increased. The proposed approach is validated by
means of comparison with different roadmaps manually
built in real plant
Hierarchical coordination strategy for multi-AGV systems based on dynamic geodesic environment partitioning
No full textIn this paper we consider the problem of coordinating the motion of a group of Automated Guided Vehicles (AGVs) utilized in industrial environments for logistics operations. In particular, we consider a hierarchical coordination strategy, where the environment is partitioned into sectors: coordination on the top layer defines the sequence of sectors to be traveled, while coordination on the bottom layer deals with traffic management inside each sector. In this paper we introduce a novel partitioning algorithm, that defines the sectors in a dynamic manner, taking into account both the shape of the (generally non-convex) environment, and the current distribution of the AGVs. This is achieved exploiting a clustering algorithm, and subsequently defining the sectors based on the geodesic distance
Coordination of multiple AGVs: a quadratic optimization method
No full textThis paper presents an optimization strategy to coordinate a fleet of Automated Guided Vehicles (AGVs) traveling on ad-hoc pre-defined roadmaps. Specifically, the objective is to maximize traffic throughput of AGVs navigating in an automated warehouse by minimizing the time AGVs spend negotiating complex traffic patterns to avoid collisions with other AGVs. In this work, the coordination problem is posed as a Quadratic Program where the optimization is performed in a centralized manner. The proposed method is validated by means of simulations and experiments for different industrial warehouse scenarios. The performance of the proposed strategy is then compared with a recently proposed decentralized coordination strategy that relies on local negotiations for shared resources. The results show that the proposed coordination strategy successfully maximizes vehicle throughput and significantly minimizes the time vehicles spend negotiating traffic under different scenarios
Cooperative cloud robotics architecture for the coordination of multi-AGV systems in industrial warehouses
No full textIn this paper we introduce a novel cloud robotics architecture that provides different functionalities to support enhanced coordination of groups of Automated Guided Vehicles (AGVs) used for industrial logistics. In particular, we define a cooperative data fusion system that, gathering data from different sensing sources, provides a constantly updated global live view of the industrial environment, for coordinating the motion of the AGVs in an optimized manner. In fact, local sensing capabilities are complemented with global information, thus extending the field of view of each AGV. This knowledge extension allows to support a cooperative and flexible global route assignment and local path planning in order to avoid congestion zones, obstacles reported in the global live view map and deal with unexpected obstacles in the current path. The proposed methodology is validated in a real industrial environment, allowing an AGV to safely perform an obstacle avoidance procedure
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