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This study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.

In this study, an algorithm for job scheduling and cooperative work of multiple AGVs is designed. In the first part, with the goal of minimizing the total processing time and the total power consumption, the niche multi-objective evolutionary algorithm is used to determine the processing task arrangement on different machines. In the second part, AGV is called to transport workpieces, and an improved ant colony algorithm is used to generate the initial path of AGV. In the third part, to avoid path conflicts between running AGVs, the authors propose a simple priority-based waiting strategy to avoid collisions.

The experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the workshop.

In this paper, a collaborative work algorithm is proposed, which combines the job scheduling and AGV running problem to make the research results adapt to the real job environment in the workshop.

This study aims to address the challenge of automatic guided vehicle (AGV) scheduling for parcel storage and retrieval in an intelligent warehouse.

This study presents a scheduling solution that aims to minimize the maximum completion time for the AGV scheduling problem in an intelligent warehouse. First, a mixed-integer linear programming model is established, followed by the proposal of a novel genetic algorithm to solve the scheduling problem of multiple AGVs. The improved algorithm includes operations such as the initial population optimization of picking up goods based on the principle of the nearest distance, adaptive crossover operation evolving with iteration, mutation operation of equivalent exchange and an algorithm restart strategy to expand search ability and avoid falling into a local optimal solution. Moreover, the routing rules of AGV are described.

By conducting a series of comparative experiments based on the actual package flow situation of an intelligent warehouse, the results demonstrate that the proposed genetic algorithm in this study outperforms existing algorithms, and can produce better solutions for the AGV scheduling problem.

This paper optimizes the different iterative steps of the genetic algorithm and designs an improved genetic algorithm, which is more suitable for solving the AGV scheduling problem in the warehouse. In addition, a path collision avoidance strategy that matches the algorithm is proposed, making this research more applicable to real-world scheduling environments.

The paper aims to describe the design and development of an automated guided vehicle (AGV) that incorporates artificial intelligence techniques to increase its autonomy and flexibility. The aim is developing a flexible AGV that operates as a flexible material handling system (MHS) in dynamic industrial environments.

Introduces the entire on‐board control system including hardware and software designs. The sensory system consists of a laser navigation system for localisation and a security laser scanner for sensing the environment. The software architecture is instantiated in a CPU that is connected to low level controllers through a CAN bus. Simplicity, flexibility, robustness and safety were concerned in the design process.

The developed prototype is able to operate in partially structured and dynamic environments, is easily configured using an approximated description of the workplace and is able to adapt when slight floor layout modifications. This development shows that current technology permits introducing intelligent vehicles in complex manufacturing systems.

The prototype is successfully tested in a real factory, operating as a flexible MHS, transporting pallets between production and storage lines.

A novel flexible AGV is designed and developed to operate as a flexible MHS in dynamic industrial environments. The system satisfies the safety and robustness requirements of industrial applications. The flexible MHS results especially suitable for manufacturing systems that suffers from cyclic and seasonal variations and for flexible manufacturing systems where the possibility of choosing alternative routes is a must.

The paper aims to propose general design rules and route plan for automated guided vehicle system (AGVS). The AGVS is applied to automated meter verification areas through a case study of meter verification shop floor to verify the feasibility.

The paper gives an appropriate route design for AGVS and proposes an optimized strategy for designed routes and a control system to manage traffic conflict.

This case study indicates that the application of AGVS can highly improve the efficiency of manufacturing and production. Besides, a reasonable transportation plan is beneficial in making the system run smoothly and in cutting conveying time.

The application of AGVS integrates a variety of advanced technologies (i.e. information technology, artificial intelligence, etc.) into the electricity meter verification system, which brings great economic and social benefits via enhancing the verification efficiency and reducing the total labor costs.

The application proposed in the paper solves the problem that the verification almost relies on workers, labor intensity is high and work efficiency is hard to improve. Furthermore, the general rules and strategies of AGVS transportation can be applied not only to the automated electricity meter verification but also in other industrial areas.

This paper aims to propose and formulate a complicated routing/scheduling problem for multiple automated guided vehicles (AGVs) in a manufacturing system.

Considering the due date of AGVs requiring for material handling among shops in a jobshop layout, their earliness and tardiness are significant in satisfying the expected cycle time and from an economic view point. Therefore, the authors propose a mathematical program to minimize the penalized earliness and tardiness for a conflict-free and just-in-time production.

The model considers a new concept of turning point for deadlock resolution. As the mathematical program is difficult to solve with a conventional method, an optimization method in two stages, namely, searching the solution space and finding optimal solutions are proposed. The performance of the proposed mathematical model is tested in a numerical example.

A case study in real industrial environment is conducted. The findings lead the decision-makers to develop a user interface decision support as a simulator to plan the AGVs’ movement through the manufacturing network and help AGVs to prevent deadlock trap or conflicts. The proposed decision support can easily be commercialized.

The benefits of such commercialization are increase in the quality of material handling, improve the delivery time and prevent delays, decrease the cost of traditional handling, capability of computerized planning and control, intelligent tracking and validation experiments in simulation environment.

In the running of multiple automated guided vehicles (AGVs) in warehouses, delay problems in motions happen unavoidably as there might exist some disabled components of robots, the instability of networks and the interference of people walking. Under this case, robots would not follow the designed paths and the coupled relationship between temporal and space domain for paths is broken. And there is no doubt that other robots are disturbed by the ones where delays happen. Finally, this brings about chaos or even breakdown of the whole system. Therefore, taking the delay disturbance into consideration in the path planning of multiple robots is an issue worthy of attention and research.

This paper proposes a prioritized path planning algorithm based on time windows to solve the delay problems of multiple AGVs. The architecture is a unity consisting of three components which are focused on scheduling AGVs under normal operations, delays of AGVs, and recovery of AGVs. In the components of scheduling AGVs under normal operations and recovery, this paper adopts a dynamic routing method based on time windows to ensure the coordination of multiple AGVs and efficient completion of tasks. In the component for scheduling AGVs under delays, a dynamical prioritized local path planning algorithm based on time windows is designed to solve delay problems. The introduced planning principle of time windows would enable the algorithm to plan new solutions of trajectories for multiple AGVs, which could lower the makespan. At the same time, the real-time performance is acceptable based on the planning principle which stipulates the parameters of local time windows to ensure that the computation of the designed algorithm would not be too large.

The simulation results demonstrate that the proposed algorithm is more efficient than the state-of-the-art method based on homotopy classes, which aims at solving the delay problems. What is more, it is validated that the proposed algorithm can achieve the acceptable real-time performance for the scheduling in warehousing applications.

By introducing the planning principle and generating delay space and local adjustable paths, the proposed algorithm in this paper can not only solve the delay problems in real time, but also lower the makespan compared with the previous method. The designed algorithm guarantees the scheduling of multiple AGVs with delay disturbance and enhances the robustness of the scheduling algorithm in multi-AGV system.

The purpose of this study is to investigate the benefits of the turning point layout; a simulation model being applicable for strategic level is designed that compares systems with and without turning points. Specifically, the avoidance of deadlocks and prevention of conflicts is substantial.

Optimization process for different layouts and configuration of autonomous guided vehicles (AGVs) are worked out using statistical methods for design parameters. Regression analysis is used to find effective design parameters and analysis of variance is applied for adjusting critical factors. Also, the optimal design is then implemented in a manufacturing system for an industrial automation and the results are reported.

The outputs imply the effectiveness of the proposed approach for real industrial cases. This research will combine both simulation-based method and optimization technique to improve the quality of solutions.

In AGV systems, the begin-end combinations are usually connected by using a fixed layout, which is not the optimal path. The capability of these configurations is limited and often the conflict of multiple AGVs and deadlock are inevitable. By appearing more flexible layouts and advanced technology, the positioning and dispatching of AGVs increased. A new concept would be to determine each path dynamically. This would use the free paths for AGVs leading to overcome the conflicts and deadlocks.

Outlines the state‐of‐the‐art in obstacle avoidance and path planning for industrial robots that is practical on the current generation of computer hardware. Describes practical vehicle planners and planning for manipulators. Summarizes that obstacle avoidance and path planning are techniques with differing goals. Sonar is the standard method of obstacle avoidance systems which is largely limited by the reliability of the sensors used. Path planning however is limited by two things: the algorithms used and the quality of the data available to planners. Concludes that it is now possible to produce path planning and obstacle avoidance systems that can be used in practical robotic systems.

The adaptation of conventional robots to construction sites is fraught with problems. Most significant of these are in relation to positioning, means of collision avoidance, and appropriate navigation strategy. This paper reviews the different levels of navigational autonomy that are possible and describes the system requirements for each. A taxonomy based on the concept of a Mobility Automation Level (MAL) is proposed. Each level is described and the requirements from a robot design perspective are discussed. Finally, a case study, based on an excavator with autonomously optimised movement, known as LUCIE, is used to illustrate some of the design criteria previously described and discussed.

This paper aims to focus on cybernetic transportation systems (CTS) due to their effectiveness for solving mobility problems in cities. A new mobility concept is proposed which allows to attain the same flexibility of the private passenger car but with much less nuisances. It is based on small semi‐autonomous electric vehicles, which may be used to complement mass public transportation, by providing passenger service for any location at any time.

A set of automatic guided vehicles for public transportation are described. Two different control paradigms of the fleet are compared: centralized vs distributed control.

The pros and cons of both control approaches are highlighted so as to support decisions about the configuration of a CTS for people transportation on public places.

The paper provides a new offer of transportation for people in short path cities downtown or public gardens, in order to move people based on sustainable and efficient public transportation systems.