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MILP based autonomous vehicle path-planning controller for unknown environments with dynamic obstacles.

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Title: MILP based autonomous vehicle path-planning controller for unknown environments with dynamic obstacles.
Authors: Perumal, D. Ganesha1 d.ganeshaperumal@klu.ac.in, Srinivasan, Seshadhri2 clk0602@gmail.com, Subathra, B.2 seshucontrol@gmail.com, Saravanakumar, G.3 saravana.control@gmail.com, Ayyagari, Ramakalyan4 rkalyn@nitt.edu
Source: International Journal of Heavy Vehicle Systems. 2016, Vol. 23 Issue 4, p350-369. 20p.
Subjects: Robotic path planning, Mixed integer linear programming, Autonomous vehicles, Electric controllers, Fuzzy logic, Kalman filtering
Abstract: Autonomous vehicles (AVs) manoeuvring in unknown environment require path-planning algorithms that are safe, yet optimal to circumvent dynamic obstacles with minimum fuel-cost. This investigation presents an autonomous vehicle path-planning (AVPP) controller that uses mixed integer linear programming to decide the blending and switching actions among possible vehicle behaviours depending on local sensed information. Our results illustrate the safety and optimality of the controller for AVPP in unknown environments with dynamic obstacles. Comparison with existing methods shows that the proposed method is more robust to collisions than the fuzzy and extended Kalman filter based arbitration mechanism studied in literature. Further, as behaviours breakdown the complex path-planning problem into simple tasks, controller realisation becomes simple. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Heavy Vehicle Systems is the property of Inderscience Enterprises Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 119614944
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: MILP based autonomous vehicle path-planning controller for unknown environments with dynamic obstacles.
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  Data: <searchLink fieldCode="AR" term="%22Perumal%2C+D%2E+Ganesha%22">Perumal, D. Ganesha</searchLink><relatesTo>1</relatesTo><i> d.ganeshaperumal@klu.ac.in</i><br /><searchLink fieldCode="AR" term="%22Srinivasan%2C+Seshadhri%22">Srinivasan, Seshadhri</searchLink><relatesTo>2</relatesTo><i> clk0602@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Subathra%2C+B%2E%22">Subathra, B.</searchLink><relatesTo>2</relatesTo><i> seshucontrol@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Saravanakumar%2C+G%2E%22">Saravanakumar, G.</searchLink><relatesTo>3</relatesTo><i> saravana.control@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Ayyagari%2C+Ramakalyan%22">Ayyagari, Ramakalyan</searchLink><relatesTo>4</relatesTo><i> rkalyn@nitt.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Heavy+Vehicle+Systems%22">International Journal of Heavy Vehicle Systems</searchLink>. 2016, Vol. 23 Issue 4, p350-369. 20p.
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  Data: <searchLink fieldCode="DE" term="%22Robotic+path+planning%22">Robotic path planning</searchLink><br /><searchLink fieldCode="DE" term="%22Mixed+integer+linear+programming%22">Mixed integer linear programming</searchLink><br /><searchLink fieldCode="DE" term="%22Autonomous+vehicles%22">Autonomous vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+controllers%22">Electric controllers</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+logic%22">Fuzzy logic</searchLink><br /><searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink>
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  Data: Autonomous vehicles (AVs) manoeuvring in unknown environment require path-planning algorithms that are safe, yet optimal to circumvent dynamic obstacles with minimum fuel-cost. This investigation presents an autonomous vehicle path-planning (AVPP) controller that uses mixed integer linear programming to decide the blending and switching actions among possible vehicle behaviours depending on local sensed information. Our results illustrate the safety and optimality of the controller for AVPP in unknown environments with dynamic obstacles. Comparison with existing methods shows that the proposed method is more robust to collisions than the fuzzy and extended Kalman filter based arbitration mechanism studied in literature. Further, as behaviours breakdown the complex path-planning problem into simple tasks, controller realisation becomes simple. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of International Journal of Heavy Vehicle Systems is the property of Inderscience Enterprises Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1504/IJHVS.2016.079272
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      – Code: eng
        Text: English
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        PageCount: 20
        StartPage: 350
    Subjects:
      – SubjectFull: Robotic path planning
        Type: general
      – SubjectFull: Mixed integer linear programming
        Type: general
      – SubjectFull: Autonomous vehicles
        Type: general
      – SubjectFull: Electric controllers
        Type: general
      – SubjectFull: Fuzzy logic
        Type: general
      – SubjectFull: Kalman filtering
        Type: general
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      – TitleFull: MILP based autonomous vehicle path-planning controller for unknown environments with dynamic obstacles.
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            NameFull: Perumal, D. Ganesha
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              Text: 2016
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