Comparative study of soft computing techniques for mobile robot navigation in an unknown environment.
An autonomous mobile robot operating in an unstructured environment must be able to deal with dynamic changes of the environment. Navigation and control of a mobile robot in an unstructured environment are one of the most challenging problems. Fuzzy logic control is a useful tool in the field of nav...
| Publicado en: | Computers in Human Behavior Vol. 50; pp. 42 - 57 |
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| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Elsevier B.V.
Sep2015
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=102981926&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 102981926 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 07475632 JC4 jtl: Computers in Human Behavior issn: 07475632 maglogo: N pubinfo: dt: Sep2015 vid: 50 pid: 2410 pub: Elsevier B.V. artinfo: ui: 102981926 10.1016/j.chb.2015.03.062 ppf: 42 ppct: 15 formats: tig: atl: Comparative study of soft computing techniques for mobile robot navigation in an unknown environment. aug: au: Algabri, Mohammed Mathkour, Hassan Ramdane, Hedjar Alsulaiman, Mansour affil: Department of Computer Science, College of Computer and Information Sciences, King Saud University, Saudi Arabia Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Saudi Arabia su: Computer simulation Logic Algorithms High performance computing Artificial neural networks Robotics sug: subj: Computer simulation Logic Algorithms High performance computing Artificial neural networks Robotics keyword: ANFIS Fuzzy logic Genetic algorithm Mobile robot navigation Particle swarm optimization Soft computing ANFIS Fuzzy logic Genetic algorithm Mobile robot navigation Particle swarm optimization Soft computing ab: An autonomous mobile robot operating in an unstructured environment must be able to deal with dynamic changes of the environment. Navigation and control of a mobile robot in an unstructured environment are one of the most challenging problems. Fuzzy logic control is a useful tool in the field of navigation of mobile robot. In this research, fuzzy logic controller is optimized by integrating fuzzy logic with other soft computing techniques like genetic algorithm, neural networks, and Particle Swarm Optimization (PSO). Soft computing techniques are used in this work to tune the membership function parameters of fuzzy logic controller to improve the navigation performance. Four methods have been designed and implemented: manually constructed fuzzy logic (M-Fuzzy), fuzzy logic with genetic algorithm (GA-Fuzzy), fuzzy logic with neural network (Neuro-Fuzzy), and fuzzy logic with PSO (PSO-Fuzzy). The performances of these approaches are compared through computer simulations and experiment number of scenarios using Khepera III mobile robot platform. Hybrid fuzzy logic controls with soft computing techniques are found to be most efficient for mobile robot navigation. The GA-Fuzzy technique is found to perform better than the other techniques in most of the test scenarios in terms of travelling time and average speed. The performances of both PSO-Fuzzy and Neuro-Fuzzy are found to be better than the other methods in terms of distance travelled. In terms of bending energy, the PSO-Fuzzy and Neuro-Fuzzy are found to be better in simulation results. Although, the M-Fuzzy is found to be better using real experimental results. Hence, the most important system parameter will dictate which of the four methods to use. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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