Autonomous navigation of an automated guided vehicle in industrial environments

Barberá, Humberto Martínez; Herrero-Pérez, David

doi:10.1016/j.rcim.2009.10.003

Cited by 126 publications

(55 citation statements)

References 24 publications

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“…Application of the Method to Use Case III Contrary to many AGVs, the fork-lift truck considered [39] incorporates a high degree of on-board autonomy to be flexible with respect to floor layout changes and to decrease the amount of manual work when establishing the a priori knowledge of the workplace.…”

Section: Comparison Of the Method's Outcome And The Use Casementioning

confidence: 99%

Motion Planning for Mobile Robots: A Method for the Selection of a Combination of Motion-Planning Algorithms

Lunenburg

Coenen

Naus

et al. 2016

IEEE Robot. Automat. Mag.

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A motion planner for mobile robots is commonly built out of a number of algorithms that solve the two steps of motion planning: 1) representing the robot and its environment and 2) searching a path through the represented environment. However, the available literature on motion planning lacks a generic methodology to arrive at a combination of representations and search algorithm classes for a practical application. This article presents a method to select appropriate algorithm classes that solve both the steps of motion planning and to select a suitable approach to combine those algorithm classes. The method is verified by comparing its outcome with three different motion planners that have been successfully applied on robots in practice. Motion PlanningMotion planning considers the planning of a path for a robot in an environment that can contain dynamic and static obstacles and is generally characterized by uncertainty. A motion planner can serve three different goals: 1) mapping, 2) covering, and 3) navigation. The focus of this article is on motion planning for mobile robots moving on the ground plane with navigation as a goal. Motion planning typically consists of representing the robot and its environment and searching a path in the represented environment [1]. These representations and search algorithms are abundant in [1]-[4]. Furthermore, as a single algorithm generally does not suffice for a practical application, planners consist of combinations of algorithms to satisfy the designer's requirements [2], [5], [6]. Because the combinations of possibly approximate algorithms are deemed necessary in practice, various approaches have been presented in [2], [4], and [6].However, the available literature lacks a generic methodology to arrive at a combination of representations and search algorithms that can handle all the requirements. The selection of an appropriate combination of representations and search algorithms depends on the desired behavior as specified by the designer, the dynamics and the kinematics of the robot at hand, the dynamics of the environment at hand, and the presence of uncertainty. Typically, these conditions are translated into seven requirements, serving as a basis for the selection of these algorithms. • Completeness: The guarantee that either a solution is returned if one exists or failure is returned if no solution exists. Completeness commonly refers only to search algorithms but, in fact, also concerns the representations because poor design choice for the representation may prohibit any search algorithm from returning a solution even though one exists. • Optimality: A solution can be optimal according to criteria such as distance, time, energy use, and so on. • Correctness: The guarantee that if a solution is returned, it will lead the robot to its goal. • Dealing with kinodynamic constraints: A robot has dynamic constraints, e.g., a limited acceleration, and can also be kinematically constrained, e.g., a car cannot move sideways. • Robustness against a dynamic environment: The...

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Section: Comparison Of the Method's Outcome And The Use Casementioning

confidence: 99%

Motion Planning for Mobile Robots: A Method for the Selection of a Combination of Motion-Planning Algorithms

Lunenburg

Coenen

Naus

et al. 2016

IEEE Robot. Automat. Mag.

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“…There are already solutions to improve these limitations. In [15] a decentralized coordination of multiple AGVs is provided and the motion of the vehicles is not constrained to a fixed route map. The work shown in [16] and [17] concerns with the development of a traffic manager able to coordinate the AGVs limiting the use of manual traffic rules.…”

Section: Related Workmentioning

confidence: 99%

Towards decentralized coordination of multi robot systems in industrial environments: A hierarchical traffic control strategy

Digani

Secchi

Fantuzzi

2013

2013 IEEE 9th International Conference on Intelligent Computer Communication and Processing (ICCP)

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This 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.

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“…This is connected with questions about the number of vehicles, vehicle routing, solving the collisions and the other problem areas [4,5]. The issue of path tracking of AGV systems was the object of the research presented in [6]. The authors dealt with AGV navigation systems in industrial environments.…”

Section: Introductionmentioning

confidence: 99%

The Use of Computer Simulation Methods to Reach Data for Economic Analysis of Automated Logistic Systems

Neradilová¹,

Fedorko

2016

Open Engineering

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Automated logistic systems are becoming more widely used within enterprise logistics processes. Their main advantage is that they allow increasing the efficiency and reliability of logistics processes. In terms of evaluating their effectiveness, it is necessary to take into account the economic aspect of the entire process. However, many users ignore and underestimate this area, which is not correct. One of the reasons why the economic aspect is overlooked is the fact that obtaining information for such an analysis is not easy. The aim of this paper is to present the possibilities of computer simulation methods for obtaining data for full-scale economic analysis implementation.

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Autonomous navigation of an automated guided vehicle in industrial environments

Cited by 126 publications

References 24 publications

Motion Planning for Mobile Robots: A Method for the Selection of a Combination of Motion-Planning Algorithms

Motion Planning for Mobile Robots: A Method for the Selection of a Combination of Motion-Planning Algorithms

Towards decentralized coordination of multi robot systems in industrial environments: A hierarchical traffic control strategy

The Use of Computer Simulation Methods to Reach Data for Economic Analysis of Automated Logistic Systems

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