Autonomous vehicles energy based operation capacity planning

• Autorzy: Szpytko Janusz , Hyla Pawel , Duarte Yorlandys Salgado

Identyfikatory

DOI

10.36897/jme/130719

• Języki publikacji: EN

Abstrakty

EN

The paper presents an optimization model for an Automatic Guided Vehicle (AGV) operation capacity planning with focused to complete predicted mission. To successfully complete the mission the available resources related to the mission task we need to predict set of the device operation capacity indicator: technical status of the device structure and functions, device control strategy, access to the energetic resources type and others. Paper is focusing on device control strategy of the AGV under operation optimisation results minimizing possible gaps corresponded with the access to the energy. The scenarios are proposed by a Particle Swarm Optimization (PSO) algorithm, and the AGV operation is evaluated with the State of Charge (SoC) variable. The selected SoC variable allows us to describe the simulated operation in detail over time. The model output is the optimal trajectory for the AGV system considering the working environment and the satisfaction of the mission preestablished by the user. The inputs parameters of the optimization model are validated by a real environment created in a laboratory scale. The localization system, trajectories planning, workspace mapping and AGV control system concepts are briefly described, as well as the artificial intelligence used as methods and tools for AGV working control, to guide the discussion towards the contribution proposed.

Słowa kluczowe

EN

AGV; planning movement trajectory; optimization

• Wydawca: Wydawnictwo Wrocławskiej Rady FSNT NOT
• Czasopismo: Journal of Machine Engineering
• Rocznik: 2020
• Tom: Vol. 20, No. 4
• Strony: 126--138
• Opis fizyczny: Bibliogr. 17 poz., rys., tab.

Twórcy

autor

Szpytko Janusz

• AGH University of Science and Technology, Krakow, Poland

autor

Hyla Pawel

• AGH University of Science and Technology, Krakow, Poland

autor

Duarte Yorlandys Salgado

• AGH University of Science and Technology, Krakow, Poland

Bibliografia

• [1] SZPYTKO J., 2004, Integrated Decision Making Supporting the Exploitation and Control of Transport Devices. Kraków, Uczelniane Wydawnictwa Naukowo-Dydaktyczne AGH, (in Polisch).
• [2] BRUCE G., RAGHAVAN S., WASIL E., 2008, The Vehicle Routing Problem: Latest Advances and New Challenges, Springer.
• [3] HUANG Y., LIANG CH., YANG Y., 2009, The Optimum Route Problem by Genetic Algorithm for Loading/Unloading of Yard Crane, Computers & Industrial Engineering, 56/3, 993–1001.
• [4] NISHI T., MORINAKAB S., KONISHIB M., 2007, A Distributed Routing Method for AGVs Under Motion Delay Disturbance, Robotics and Computer-Integrated Manufacturing, 23/5, 517–532.
• [5] QIU L., HSU W.J, HUANG S.Y., WANG H., 2002, Scheduling and Routing Algorithms for AGVs: a Survey, Int. J. Prod. Res., 40/3, 745–760.
• [6] NEWMAN P.M., DURRANT-WHYTE H.F., 2001, A New Solution to the Simultaneous Localization and Map Building (SLAM) Problem, IEEE Transactions on Robotics and Automation, 17/3, 229–241.
• [7] SZPYTKO J., HYLA P., 2011, Automated Guided Vehicles Navigating Problem in Container Terminal, Logistics and Transport, 13/2, 107–116.
• [8] HYLA P., SZPYTKO J., 2017, Automated Guided Vehicles – the Survey. Journal of KONES: Powertrain and Transport, 24/3, 101–110,
• [9] FARAHANI Z.F., LAPORTE G., MIANDOABCHI E., BINA S., 2008, Designing Efficient Methods for the Tandem AGV Network Design Problem Using Tabu Search and Genetic Algorithm, Int. J. Adv. Manuf. Technol., 36, 996–1009.
• [10] SCHMIDT J., MEYER-BARLAG C., EISEL M., KOLBE L.M., APPELRATH H.J., 2015, Using Battery-Electric AGVs in Container Terminals-Assessing the Potential and Optimizing the Economic Viability, Research in Transportation Business & Management, 17, 99–111.
• [11] KABIRA S.Q., SUZUKI Y., 2018, Increasing Manufacturing Flexibility Through Battery Management of Automated Guided Vehicles, Computers & Industrial Engineering, 117, 225–236.
• [12] ZHAN X., XU LI, ZHANG J., LI A., 2019, Study on AGVs Battery Charging Strategy for Improving Utilization, 52nd CIRP Conference on Manufacturing Systems, Procedia CIRP, 81, 558–563.
• [13] MA N., ZHOU C., STEPHEN A., 2021, Simulation Model and Performance Evaluation of Battery-Powered AGV Systems in Automated Container Terminals, Simulation Modelling Practice and Theory, 106, 102–146.
• [14] MATLAB, 2010, version 9.7.9.1319299 (R2019b), Natick, Massachusetts: The MathWorks Inc.
• [15] KENNEDY J., EBERHART R., 1995, Particle Swarm Optimization, Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, 1942–1945.
• [16] MEZURA-MONTES E., COELLO C.A., 2011, Constraint-Handling in Nature-Inspired Numerical Optimization: Past, Present and Future, Swarm and Evolutionary Computation, 173–194.
• [17] WANG R., SELL R., RASSOLKIN A., OTTO T., MALAYJERDI E., 2020, Intelligent Functions Development on Autonomous Electric Vehicle Platform, Journal of Machine Engineering., 20/2, 114–125.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).