Autonomous vans - the planning process of transport tasks

Nieoczym, A.; Caban, Jacek; Dudziak, Agnieszka; Stoma, Monika

doi:10.1515/eng-2020-0006

Cited by 13 publications

(9 citation statements)

References 31 publications

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“…The basic task of mapping the route, the results of which can be used as input data to AI algorithms, can be solved on the set of N points making up the transport system which is served by m vehicles [105]. In such a situation, each point sends a request for service with the intensity λ.…”

Section: Modeling Examplementioning

confidence: 99%

The Planning Process of Transport Tasks for Autonomous Vans—Case Study

et al. 2022

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Transport is an area that is developing at a tremendous pace. This development applies not only to electric and hybrid cars appearing more and more often on the road but also to those of an autonomous or semi-autonomous nature. This applies to both passenger cars and vans. In many different publications, you can find a description of a number of benefits of using automated guided vehicles (AGV) for logistics and technical tasks, e.g., in the workplace. An important aspect is the use of knowledge management and machine learning, i.e., artificial intelligence (AI), to design these types of processes. An important issue in the construction of autonomous vehicles is the IT connection of sensors receiving signals from the environment. These signals are data for deep learning algorithms. The data after IT processing enable the decision-making by AI systems, while the used machine learning algorithms and neural networks are also needed for video image analysis in order to identify and classify registered objects. The purpose of this article is to present and verify a mathematical model used to respond to vehicles’ demand for a transport service under set conditions. The optimal conditions of the system to perform the transport task were simulated, and the efficiency of this system and benefits of this choice were determined.

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Section: Modeling Examplementioning

confidence: 99%

The Planning Process of Transport Tasks for Autonomous Vans—Case Study

et al. 2022

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“…For the application of the location problem and calculation of transport work, it is imperative to identify individual values of the graph edges [23]. As aforementioned, the values will represent the individual real routes with the distances (in km) among the relevant branches according to the graph in Figure 1B (see Table 2 above).…”

Section: Minimum Distance Matrixmentioning

confidence: 99%

Modeling transportation routes of the pick-up system using location problem: a case study

et al. 2020

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In today’s increasingly globalizing world, the demand for the transport of more and more specific kinds of goods is constantly growing. Greater diversity of the objects of transport extends the range of consignees and consignors, which implies the reduction of the number and volume of consignments. Modern enterprises are no longer interested in mass supplies of large quantities of the same material but strive for flexible supplies of smaller quantities of specific materials out of more suppliers. This manuscript deals with the streamlining of specific transport-logistics processes, including a detailed analysis of the existing pick-up system in relation to transport serviceability of the certain territory starting in the city of České Budějovice (Czech Republic) and a determination of more effective variants for supply. The primary objective will be to find and analyze specific options for streamlining the processes being optimized using the selected method of the graph theory, namely the location problem. In the context of optimization, a specific case study is elaborated with the aim to increase the efficiency of the current pickup system, i.e. serviceability of a particular territory.

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“…With the rapid development of science and technology, the automobile industry has become one of the most important industries in the world, playing an essential role in daily life, industrial production, and other fields [ 1 , 2 , 3 ]. In recent years, autonomous vehicles that can be characterized as driving that do not need a driver but are only navigated by sensors, detection, and computer systems are getting more and more attention and are supposed to bring many benefits to society [ 4 , 5 ]. However, as it remains challenging to make self-driving cars absolutely safe, the public is skeptical that they can be put to mass use anytime soon [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

A Localization and Mapping Algorithm Based on Improved LVI-SAM for Vehicles in Field Environments

Han

Shi

Wang

2023

Sensors

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Quickly grasping the surrounding environment’s information and the location of the vehicle is the key to achieving automatic driving. However, accurate and robust localization and mapping are still challenging for field vehicles and robots due to the characteristics of emptiness, terrain changeability, and Global Navigation Satellite System (GNSS)-denied in complex field environments. In this study, an LVI-SAM-based lidar, inertial, and visual fusion using simultaneous localization and mapping (SLAM) algorithm was proposed to solve the problem of localization and mapping for vehicles in such open, bumpy, and Global Positioning System (GPS)-denied field environments. In this method, a joint lidar front end of pose estimation and correction was designed using the Super4PCS, Iterative Closest Point (ICP), and Normal Distributions Transform (NDT) algorithms and their variants. The algorithm can balance localization accuracy and real-time performance by carrying out lower-frequency pose correction based on higher-frequency pose estimation. Experimental results from the complex field environment show that, compared with LVI-SAM, the proposed method can reduce the translational error of localization by about 4.7% and create a three-dimensional point cloud map of the environment in real time, realizing the high-precision and high-robustness localization and mapping of the vehicle in complex field environments.

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Autonomous vans - the planning process of transport tasks

Cited by 13 publications

References 31 publications

The Planning Process of Transport Tasks for Autonomous Vans—Case Study

The Planning Process of Transport Tasks for Autonomous Vans—Case Study

Modeling transportation routes of the pick-up system using location problem: a case study

A Localization and Mapping Algorithm Based on Improved LVI-SAM for Vehicles in Field Environments

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