Research and Develop of AGV Platform for the Logistics Warehouse Environment

Ngo, Ha Quang Thinh; Nguyễn, Thanh Phương; Nguyen, Hung T.

doi:10.1007/978-3-030-02683-7_32

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…They can make decisions in real-time, avoiding obstacles, optimizing routes, and ensuring the safe and efficient delivery of goods. In manufacturing plants and warehouses [17,18], autonomous forklifts and robots collaborate seamlessly, reducing human labour requirements and improving precision and speed.…”

Section: Introductionmentioning

confidence: 99%

Manipulation of the Multi-Vehicle System for the Industrial Applications

Vincent

2023

EAI Endorsed Trans Context Aware Syst App

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This approach should indicate some challenges in routing and scheduling for the multi-vehicle system. The proposed method delivers a novel method to generate the free-collision trajectory as well as optimal route from starting point to destination. The estimated time at one node and the classification of load level support vehicle to decide which proper route is and stable movement is reached. From these results, it could be observed that the proposed approach is feasible and effective for many applications. The proposed method for routing and scheduling might be useful in the multi-vehicle system. In the large scale system, some intelligent schemes should be considered to integrate.

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Section: Introductionmentioning

confidence: 99%

Manipulation of the Multi-Vehicle System for the Industrial Applications

Vincent

2023

EAI Endorsed Trans Context Aware Syst App

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“…Autonomous vehicles, which are programmed to move materials from one department to another without human intervention, are seen as part of smart manufacturing and reduce the workload. Determining route tracking strategies and real-time route tracking is an important research topic for autonomous vehicles used in industrial production areas [1,2]. It is important to follow a flexible route suitable for the variable obstacle structure depending on the working environment.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Floor Path Model for Route Tracking of Autonomous Vehicles

Erginli

Cil²

2022

Systems

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Real-time route tracking is an important research topic for autonomous vehicles used in industrial facilities. Traditional methods such as copper line tracking on the ground, wireless guidance systems, and laser systems are still used in route tracking. In this study, a deep-learning-based floor path model for route tracking of autonomous vehicles is proposed. A deep-learning floor path model and algorithm have been developed for highly accurate route tracking, which avoids collisions of vehicles and follows the shortest route to reach the destination. The floor path model consists of markers. Routes in the floor path model are created by using these markers. The floor path model is transmitted to autonomous vehicles as a vector by a central server. The server dispatches the target marker address to the vehicle to move. The vehicle calculates all possible routes to this address and chooses the shortest one. Marker images on the selected route are processed using image processing and classified with a pre-trained deep-CNN model. If the classified image and the image on the selected route are the same, the vehicle proceeds toward its destination. While the vehicle moves on the route, it sends the last classified marker to the server. Other autonomous vehicles use this marker to determine the location of this vehicle. Other vehicles on the route wait to avoid a collision. As a result of the experimental studies we have carried out, the route tracking of the vehicles has been successfully achieved.

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“…Robot has changed people's life. More and more laborintensive jobs can be accomplished by robots such as automatic logistics [1], automatic sweeping [2] and so on. Among them, the wheeled mobile robot is the most promising one for its simple mechanical structure and prominent autonomous motion capabilities.…”

Section: Introductionmentioning

confidence: 99%

Visual Servoing of Nonholonomic Mobile Robots: A Review and a Novel Perspective

Huang

2019

IEEE Access

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Mobile robots combined with visual feedback have been an indispensable role of the society, including in industry and in service, which have attracted extensive researchers over the past years. In this paper, we provide an overview of the visual servoing for nonholonomic mobile robots and introduce two feasible system modeling methods in perspective of active disturbance rejection control (ADRC). Firstly, the visual servoing systems of mobile robots are classified into independent type and coupling type based on the robot and camera configuration. Then three mainstream strategies are discussed according to the different requirements for camera calibration, and the efficiency of which are presented from existing works. In order to cope with the uncalibrated visual servoing of nonholonomic mobile robots, we give two feasible attempts to convert the robot system model into the canonical integrator chain of the ADRC by using the input-state scaling technique and flat output. Finally, future work is outlined accompanied with the challenges. INDEX TERMSVisual servoing, nonholonomic mobile robot, active disturbance rejection control (ADRC).

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Research and Develop of AGV Platform for the Logistics Warehouse Environment

Cited by 8 publications

References 9 publications

Manipulation of the Multi-Vehicle System for the Industrial Applications

Manipulation of the Multi-Vehicle System for the Industrial Applications

Deep-Learning-Based Floor Path Model for Route Tracking of Autonomous Vehicles

Visual Servoing of Nonholonomic Mobile Robots: A Review and a Novel Perspective

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