An Optimized Energy and Time Constraints-Based Path Planning for the Navigation of Mobile Robots Using an Intelligent Particle Swarm Optimization Technique

Mobile robots (MRs) typically require running for many hours on one charge of the battery. Electric autonomous mobile robots (AMRs) have become increasingly common in the manufacturing process in the last few years. MRs must often complete difficult assignments while gathering information across an...

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Published inApplied sciences Vol. 13; no. 17; p. 9667
Main Authors Raj, Ravi, Kos, Andrzej
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2023
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Abstract Mobile robots (MRs) typically require running for many hours on one charge of the battery. Electric autonomous mobile robots (AMRs) have become increasingly common in the manufacturing process in the last few years. MRs must often complete difficult assignments while gathering information across an unknown area involving energy constraints and time-sensitive preferences. This paper estimates the information collection assignment for surveillance as a multi-objective optimization dilemma with both energy and time constraints. In this study, three main objectives during acquiring data are taken into consideration, including the greatest quantity of data acquired for surveillance, following a path where obstacles are least likely to be experienced, and traveling the smallest feasible path. To obtain the optimal path for an MR by addressing the presented issue, this approach presents an intelligent particle swarm optimization (PSO) technique that determines fitness value by simplifying the optimization task for achieving the shortest path for MR navigation. It allows particles to execute variable operations while maintaining most of the prior search information. The findings of the simulation show that this technique of the PSO algorithm can realize swift convergence and high accuracy when compared with different benchmark functions derived for PSO. A comparative discussion on various energy-efficient navigation techniques for MRs is also provided. Lastly, this study describes the possible future research directions.
AbstractList Mobile robots (MRs) typically require running for many hours on one charge of the battery. Electric autonomous mobile robots (AMRs) have become increasingly common in the manufacturing process in the last few years. MRs must often complete difficult assignments while gathering information across an unknown area involving energy constraints and time-sensitive preferences. This paper estimates the information collection assignment for surveillance as a multi-objective optimization dilemma with both energy and time constraints. In this study, three main objectives during acquiring data are taken into consideration, including the greatest quantity of data acquired for surveillance, following a path where obstacles are least likely to be experienced, and traveling the smallest feasible path. To obtain the optimal path for an MR by addressing the presented issue, this approach presents an intelligent particle swarm optimization (PSO) technique that determines fitness value by simplifying the optimization task for achieving the shortest path for MR navigation. It allows particles to execute variable operations while maintaining most of the prior search information. The findings of the simulation show that this technique of the PSO algorithm can realize swift convergence and high accuracy when compared with different benchmark functions derived for PSO. A comparative discussion on various energy-efficient navigation techniques for MRs is also provided. Lastly, this study describes the possible future research directions.
Audience Academic
Author Raj, Ravi
Kos, Andrzej
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Cites_doi 10.1108/AA-11-2015-094
10.1016/j.dt.2019.04.011
10.1016/j.robot.2014.07.002
10.1109/MRA.2008.921540
10.1007/978-3-642-13498-2
10.1016/j.eswa.2010.08.041
10.1109/MCI.2006.329691
10.3390/en12010027
10.1109/TASE.2011.2182509
10.1109/TCYB.2021.3079346
10.1016/j.ins.2018.03.035
10.3390/en16031532
10.1109/4235.996017
10.3390/en12102010
10.1007/s10898-007-9149-x
10.1109/ICTAI.2015.78
10.1007/s12559-011-9117-0
10.3390/en14123517
10.1007/s10514-009-9130-2
10.1109/LRA.2017.2729666
10.21203/rs.3.rs-2074771/v1
10.15199/48.2023.02.01
10.3390/s20226423
10.1109/TITS.2015.2505323
10.3390/app12146951
10.1109/TSMCB.2005.862724
10.1109/TCST.2016.2599486
10.1109/TMECH.2013.2241777
10.1007/978-3-319-61994-1
10.3390/en12061136
10.1007/BFb0040753
10.1109/TEVC.2004.826071
10.1109/TEVC.2008.927706
10.1109/4235.771166
10.1109/TRO.2004.837232
10.1080/02564602.2021.1894250
10.1002/rob.20143
10.1109/ROBOT.2009.5152387
10.1109/INTERCON.2019.8853557
10.1109/TCYB.2020.2977661
10.1109/JIOT.2020.2991198
10.3390/en16031210
10.26599/TST.2021.9010012
10.1109/ComplexSys.2015.7385991
10.1109/SMC.2017.8122814
10.1109/ICEPE50861.2021.9404505
10.1155/2018/8269698
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References Dorigo (ref_13) 2006; 1
Willms (ref_6) 2006; 36
ref_11
Karaboga (ref_14) 2007; 39
ref_19
ref_18
Tang (ref_39) 2011; 38
ref_16
Sun (ref_29) 2005; 21
Marzec (ref_51) 2021; 69
Vergnano (ref_52) 2012; 9
ref_25
Liu (ref_30) 2014; 19
ref_21
Qin (ref_15) 2009; 13
Brochu (ref_54) 2009; 27
Tian (ref_20) 2016; 17
ref_28
ref_27
ref_26
Xie (ref_36) 2020; 7
Zhu (ref_49) 2021; 26
Koyuncu (ref_45) 2018; 6
Leedy (ref_46) 2006; 23
ref_35
ref_34
ref_33
ref_32
ref_31
Patle (ref_50) 2019; 15
Deb (ref_17) 2002; 6
Raj (ref_3) 2023; 2
ref_38
Bhattacharya (ref_37) 2008; 15
Samar (ref_9) 2011; 4
Zhang (ref_22) 2022; 52
ref_42
ref_41
Ratnaweera (ref_43) 2004; 8
ref_40
ref_1
Han (ref_10) 2018; 450
Ayawli (ref_47) 2018; 2018
Kim (ref_55) 2018; 3
Song (ref_23) 2016; 36
Halal (ref_2) 2015; 48
ref_48
Hossain (ref_7) 2015; 64
ref_8
ref_5
ref_4
Eiben (ref_12) 1999; 3
Li (ref_24) 2021; 51
Sharma (ref_44) 2021; 39
Setter (ref_53) 2017; 25
References_xml – volume: 36
  start-page: 138
  year: 2016
  ident: ref_23
  article-title: A new genetic algorithm approach to smooth path planning for mobile robots
  publication-title: Assem. Autom.
  doi: 10.1108/AA-11-2015-094
  contributor:
    fullname: Song
– volume: 15
  start-page: 582
  year: 2019
  ident: ref_50
  article-title: A Review: On Path Planning Strategies for Navigation of Mobile Robot
  publication-title: Def. Technol.
  doi: 10.1016/j.dt.2019.04.011
  contributor:
    fullname: Patle
– volume: 64
  start-page: 137
  year: 2015
  ident: ref_7
  article-title: Autonomous Robot Path Planning in Dynamic Environment Using a New Optimization Technique Inspired by Bacterial Foraging Technique
  publication-title: Robot. Auton. Syst.
  doi: 10.1016/j.robot.2014.07.002
  contributor:
    fullname: Hossain
– volume: 15
  start-page: 58
  year: 2008
  ident: ref_37
  article-title: Roadmap-Based Path Planning-Using the Voronoi Diagram for a Clearance-Based Shortest Path
  publication-title: IEEE Robot. Autom. Mag.
  doi: 10.1109/MRA.2008.921540
  contributor:
    fullname: Bhattacharya
– ident: ref_16
  doi: 10.1007/978-3-642-13498-2
– volume: 38
  start-page: 2523
  year: 2011
  ident: ref_39
  article-title: Parameters Identification of Unknown Delayed Genetic Regulatory Networks by a Switching Particle Swarm Optimization Algorithm
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2010.08.041
  contributor:
    fullname: Tang
– volume: 1
  start-page: 28
  year: 2006
  ident: ref_13
  article-title: Ant Colony Optimization
  publication-title: IEEE Comput. Intell. Mag.
  doi: 10.1109/MCI.2006.329691
  contributor:
    fullname: Dorigo
– ident: ref_28
  doi: 10.3390/en12010027
– volume: 9
  start-page: 423
  year: 2012
  ident: ref_52
  article-title: Modeling and Optimization of Energy Consumption in Cooperative Multi-Robot Systems
  publication-title: IEEE Trans. Autom. Sci. Eng.
  doi: 10.1109/TASE.2011.2182509
  contributor:
    fullname: Vergnano
– ident: ref_42
– ident: ref_35
– volume: 52
  start-page: 9871
  year: 2022
  ident: ref_22
  article-title: Moving-Distance-Minimized PSO for Mobile Robot Swarm
  publication-title: IEEE Trans. Cybern.
  doi: 10.1109/TCYB.2021.3079346
  contributor:
    fullname: Zhang
– volume: 450
  start-page: 39
  year: 2018
  ident: ref_10
  article-title: Path Regeneration Decisions in a Dynamic Environment
  publication-title: Inf. Sci.
  doi: 10.1016/j.ins.2018.03.035
  contributor:
    fullname: Han
– ident: ref_26
  doi: 10.3390/en16031532
– volume: 6
  start-page: 182
  year: 2002
  ident: ref_17
  article-title: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II
  publication-title: IEEE Trans. Evol. Comput.
  doi: 10.1109/4235.996017
  contributor:
    fullname: Deb
– ident: ref_32
  doi: 10.3390/en12102010
– volume: 39
  start-page: 459
  year: 2007
  ident: ref_14
  article-title: A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm
  publication-title: J. Glob. Optim.
  doi: 10.1007/s10898-007-9149-x
  contributor:
    fullname: Karaboga
– ident: ref_11
  doi: 10.1109/ICTAI.2015.78
– volume: 4
  start-page: 515
  year: 2011
  ident: ref_9
  article-title: Optimal Path Computation for Autonomous Aerial Vehicles
  publication-title: Cogn. Comput.
  doi: 10.1007/s12559-011-9117-0
  contributor:
    fullname: Samar
– ident: ref_41
– ident: ref_27
  doi: 10.3390/en14123517
– volume: 27
  start-page: 93
  year: 2009
  ident: ref_54
  article-title: A Bayesian Exploration-Exploitation Approach for Optimal Online Sensing and Planning with a Visually Guided Mobile Robot
  publication-title: Auton. Robot.
  doi: 10.1007/s10514-009-9130-2
  contributor:
    fullname: Brochu
– volume: 3
  start-page: 68
  year: 2018
  ident: ref_55
  article-title: Anticipatory Robot Assistance for the Prevention of Human Static Joint Overloading in Human–Robot Collaboration
  publication-title: IEEE Robot. Autom. Lett.
  doi: 10.1109/LRA.2017.2729666
  contributor:
    fullname: Kim
– ident: ref_38
  doi: 10.21203/rs.3.rs-2074771/v1
– volume: 2
  start-page: 3
  year: 2023
  ident: ref_3
  article-title: Artificial Intelligence: Evolution, Developments, Applications, and Future Scope
  publication-title: Prz. Elektrotechniczny
  doi: 10.15199/48.2023.02.01
  contributor:
    fullname: Raj
– ident: ref_48
  doi: 10.3390/s20226423
– volume: 17
  start-page: 3009
  year: 2016
  ident: ref_20
  article-title: Dual-Objective Scheduling of Rescue Vehicles to Distinguish Forest Fires via Differential Evolution and Particle Swarm Optimization Combined Algorithm
  publication-title: IEEE Trans. Intell. Transp. Syst.
  doi: 10.1109/TITS.2015.2505323
  contributor:
    fullname: Tian
– ident: ref_1
  doi: 10.3390/app12146951
– volume: 36
  start-page: 755
  year: 2006
  ident: ref_6
  article-title: An Efficient Dynamic System for Real-Time Robot-Path Planning
  publication-title: IEEE Trans. Syst. Man Cybern. Part B (Cybern.)
  doi: 10.1109/TSMCB.2005.862724
  contributor:
    fullname: Willms
– volume: 6
  start-page: 129
  year: 2018
  ident: ref_45
  article-title: A PSO Based Approach: Scout Particle Swarm Algorithm for Continuous Global Optimization Problems
  publication-title: J. Comput. Des. Eng.
  contributor:
    fullname: Koyuncu
– volume: 25
  start-page: 1257
  year: 2017
  ident: ref_53
  article-title: Energy-Constrained Coordination of Multi-Robot Teams
  publication-title: IEEE Trans. Control Syst. Technol.
  doi: 10.1109/TCST.2016.2599486
  contributor:
    fullname: Setter
– volume: 19
  start-page: 401
  year: 2014
  ident: ref_30
  article-title: Minimizing Energy Consumption of Wheeled Mobile Robots via Optimal Motion Planning
  publication-title: IEEE/ASME Trans. Mechatron.
  doi: 10.1109/TMECH.2013.2241777
  contributor:
    fullname: Liu
– ident: ref_5
  doi: 10.1007/978-3-319-61994-1
– ident: ref_18
– ident: ref_31
  doi: 10.3390/en12061136
– ident: ref_40
  doi: 10.1007/BFb0040753
– volume: 8
  start-page: 240
  year: 2004
  ident: ref_43
  article-title: Self-Organizing Hierarchical Particle Swarm Optimizer With Time-Varying Acceleration Coefficients
  publication-title: IEEE Trans. Evol. Comput.
  doi: 10.1109/TEVC.2004.826071
  contributor:
    fullname: Ratnaweera
– ident: ref_21
– volume: 13
  start-page: 398
  year: 2009
  ident: ref_15
  article-title: Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization
  publication-title: IEEE Trans. Evol. Comput.
  doi: 10.1109/TEVC.2008.927706
  contributor:
    fullname: Qin
– volume: 3
  start-page: 124
  year: 1999
  ident: ref_12
  article-title: Parameter Control in Evolutionary Algorithms
  publication-title: IEEE Trans. Evol. Comput.
  doi: 10.1109/4235.771166
  contributor:
    fullname: Eiben
– volume: 21
  start-page: 102
  year: 2005
  ident: ref_29
  article-title: On Finding Energy-Minimizing Paths on Terrains
  publication-title: IEEE Trans. Robot.
  doi: 10.1109/TRO.2004.837232
  contributor:
    fullname: Sun
– volume: 39
  start-page: 675
  year: 2021
  ident: ref_44
  article-title: Path Planning for Multiple Targets Interception by the Swarm of UAVs Based on Swarm Intelligence Algorithms: A Review
  publication-title: IETE Tech. Rev.
  doi: 10.1080/02564602.2021.1894250
  contributor:
    fullname: Sharma
– volume: 23
  start-page: 709
  year: 2006
  ident: ref_46
  article-title: Virginia Tech’s Twin Contenders: A Comparative Study of Reactive and Deliberative Navigation
  publication-title: J. Field Robot.
  doi: 10.1002/rob.20143
  contributor:
    fullname: Leedy
– ident: ref_4
  doi: 10.1109/ROBOT.2009.5152387
– ident: ref_33
  doi: 10.1109/INTERCON.2019.8853557
– volume: 51
  start-page: 3103
  year: 2021
  ident: ref_24
  article-title: Deep Reinforcement Learning for Multiobjective Optimization
  publication-title: IEEE Trans. Cybern.
  doi: 10.1109/TCYB.2020.2977661
  contributor:
    fullname: Li
– volume: 48
  start-page: 778
  year: 2015
  ident: ref_2
  article-title: Multi-Strategy Spatial Data Acquisition Missions Using Genetic Algorithms
  publication-title: IFAC-Pap.
  contributor:
    fullname: Halal
– volume: 7
  start-page: 7734
  year: 2020
  ident: ref_36
  article-title: Energy- and Time-Aware Data Acquisition for Mobile Robots Using Mixed Cognition Particle Swarm Optimization
  publication-title: IEEE Internet Things J.
  doi: 10.1109/JIOT.2020.2991198
  contributor:
    fullname: Xie
– volume: 69
  start-page: e136038
  year: 2021
  ident: ref_51
  article-title: Thermal navigation for blind people
  publication-title: Bull. Pol. Acad. Sci. Tech. Sci.
  contributor:
    fullname: Marzec
– ident: ref_25
  doi: 10.3390/en16031210
– volume: 26
  start-page: 674
  year: 2021
  ident: ref_49
  article-title: Deep Reinforcement Learning Based Mobile Robot Navigation: A Review
  publication-title: Tsinghua Sci. Technol.
  doi: 10.26599/TST.2021.9010012
  contributor:
    fullname: Zhu
– ident: ref_34
  doi: 10.1109/ComplexSys.2015.7385991
– ident: ref_8
  doi: 10.1109/SMC.2017.8122814
– ident: ref_19
  doi: 10.1109/ICEPE50861.2021.9404505
– volume: 2018
  start-page: 8269698
  year: 2018
  ident: ref_47
  article-title: An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning
  publication-title: J. Adv. Transp.
  doi: 10.1155/2018/8269698
  contributor:
    fullname: Ayawli
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Snippet Mobile robots (MRs) typically require running for many hours on one charge of the battery. Electric autonomous mobile robots (AMRs) have become increasingly...
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StartPage 9667
SubjectTerms Accuracy
Algorithms
data acquisition
energy
Energy consumption
Energy efficiency
Genetic algorithms
Information management
Literature reviews
Mathematical optimization
Methods
mobile robot (MR)
navigation
Optimization techniques
particle swarm optimization (PSO)
path planning
Planning
Robotics industry
Robots
Surveillance
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Title An Optimized Energy and Time Constraints-Based Path Planning for the Navigation of Mobile Robots Using an Intelligent Particle Swarm Optimization Technique
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https://doaj.org/article/41fe8b31b7fb47bc83ee39cb9fedeffe
Volume 13
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