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Rapid Navigation Function Control for Two-Wheeled Mobile Robots
This paper presents a kinematic controller for a differentially driven mobile robot. The controller is based on the navigation function (NF) concept that guarantees goal achievement from almost all initial states. Slow convergence in some cases is a significant disadvantage of this approach, especia...
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Published in: | Journal of intelligent & robotic systems 2019-03, Vol.93 (3-4), p.687-697 |
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container_title | Journal of intelligent & robotic systems |
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creator | Kowalczyk, Wojciech |
description | This paper presents a kinematic controller for a differentially driven mobile robot. The controller is based on the navigation function (NF) concept that guarantees goal achievement from almost all initial states. Slow convergence in some cases is a significant disadvantage of this approach, especially when narrow passages exist in the environment and/or specific values of design parameters are set. The main reason of this phenomenon is that the velocity control strongly depends on the slope of the NF. The algorithm proposed in this paper is based on a method introduced in Urakubo (Nonlin. Dyn.
81
(3): 1475–1487
2015
), that extends NF to nonholonomic mobile platforms and allows stabilizing not only the position of robots but also their orientation. This algorithm is used as a reference in experimental performance comparison. In the new algorithm, the gradient of the NF is used to generate motion direction but the velocity is computed as a function of position and orientation errors. This approach results in much better state converge. Analysis of the convergence shows how the location of the eigenvalues of linearized system affects time of goal achievement. The paper describes saddle point detection and avoidance methodology and presents their experimental verification. It also shows what happens in practice if initial position is located exactly in the saddle point and its detection/avoidance procedures are turned off. |
doi_str_mv | 10.1007/s10846-018-0879-4 |
format | article |
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81
(3): 1475–1487
2015
), that extends NF to nonholonomic mobile platforms and allows stabilizing not only the position of robots but also their orientation. This algorithm is used as a reference in experimental performance comparison. In the new algorithm, the gradient of the NF is used to generate motion direction but the velocity is computed as a function of position and orientation errors. This approach results in much better state converge. Analysis of the convergence shows how the location of the eigenvalues of linearized system affects time of goal achievement. The paper describes saddle point detection and avoidance methodology and presents their experimental verification. It also shows what happens in practice if initial position is located exactly in the saddle point and its detection/avoidance procedures are turned off.</description><identifier>ISSN: 0921-0296</identifier><identifier>EISSN: 1573-0409</identifier><identifier>DOI: 10.1007/s10846-018-0879-4</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Algorithms ; Analysis ; Artificial Intelligence ; Avoidance ; Control ; Convergence ; Design parameters ; Eigenvalues ; Electrical Engineering ; Engineering ; Mechanical Engineering ; Mechatronics ; Navigation ; Robot control ; Robotics ; Robots ; Saddle points</subject><ispartof>Journal of intelligent & robotic systems, 2019-03, Vol.93 (3-4), p.687-697</ispartof><rights>The Author(s) 2018</rights><rights>COPYRIGHT 2019 Springer</rights><rights>Journal of Intelligent & Robotic Systems is a copyright of Springer, (2018). All Rights Reserved. © 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c398t-f673282e9d0603448e56bd740ff38af18263f3db84b818c4855a148411c102103</citedby><cites>FETCH-LOGICAL-c398t-f673282e9d0603448e56bd740ff38af18263f3db84b818c4855a148411c102103</cites><orcidid>0000-0001-5762-6301</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Kowalczyk, Wojciech</creatorcontrib><title>Rapid Navigation Function Control for Two-Wheeled Mobile Robots</title><title>Journal of intelligent & robotic systems</title><addtitle>J Intell Robot Syst</addtitle><description>This paper presents a kinematic controller for a differentially driven mobile robot. The controller is based on the navigation function (NF) concept that guarantees goal achievement from almost all initial states. Slow convergence in some cases is a significant disadvantage of this approach, especially when narrow passages exist in the environment and/or specific values of design parameters are set. The main reason of this phenomenon is that the velocity control strongly depends on the slope of the NF. The algorithm proposed in this paper is based on a method introduced in Urakubo (Nonlin. Dyn.
81
(3): 1475–1487
2015
), that extends NF to nonholonomic mobile platforms and allows stabilizing not only the position of robots but also their orientation. This algorithm is used as a reference in experimental performance comparison. In the new algorithm, the gradient of the NF is used to generate motion direction but the velocity is computed as a function of position and orientation errors. This approach results in much better state converge. Analysis of the convergence shows how the location of the eigenvalues of linearized system affects time of goal achievement. The paper describes saddle point detection and avoidance methodology and presents their experimental verification. It also shows what happens in practice if initial position is located exactly in the saddle point and its detection/avoidance procedures are turned off.</description><subject>Algorithms</subject><subject>Analysis</subject><subject>Artificial Intelligence</subject><subject>Avoidance</subject><subject>Control</subject><subject>Convergence</subject><subject>Design parameters</subject><subject>Eigenvalues</subject><subject>Electrical Engineering</subject><subject>Engineering</subject><subject>Mechanical Engineering</subject><subject>Mechatronics</subject><subject>Navigation</subject><subject>Robot control</subject><subject>Robotics</subject><subject>Robots</subject><subject>Saddle 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81
(3): 1475–1487
2015
), that extends NF to nonholonomic mobile platforms and allows stabilizing not only the position of robots but also their orientation. This algorithm is used as a reference in experimental performance comparison. In the new algorithm, the gradient of the NF is used to generate motion direction but the velocity is computed as a function of position and orientation errors. This approach results in much better state converge. Analysis of the convergence shows how the location of the eigenvalues of linearized system affects time of goal achievement. The paper describes saddle point detection and avoidance methodology and presents their experimental verification. It also shows what happens in practice if initial position is located exactly in the saddle point and its detection/avoidance procedures are turned off.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s10846-018-0879-4</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-5762-6301</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Analysis Artificial Intelligence Avoidance Control Convergence Design parameters Eigenvalues Electrical Engineering Engineering Mechanical Engineering Mechatronics Navigation Robot control Robotics Robots Saddle points |
title | Rapid Navigation Function Control for Two-Wheeled Mobile Robots |
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