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Visual navigation for mobile robot with Kinect camera in dynamic environment
To solve the problem of the visual navigation for mobile robot in dynamic environment, a visual navigation system for mobile robot with Kinect camera is designed. Firstly, the improved RBPF (RAO Blackwellized particle filters) algorithm is used to build the 2D grid map of the indoor environment and...
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creator | Xin, Jing Jiao, Xiao-Liang Yang, Yin Liu, Ding |
description | To solve the problem of the visual navigation for mobile robot in dynamic environment, a visual navigation system for mobile robot with Kinect camera is designed. Firstly, the improved RBPF (RAO Blackwellized particle filters) algorithm is used to build the 2D grid map of the indoor environment and on the basis of this map, operation of inflating obstacles is applied to build the global grid map considering the actual size of the robot. Secondly, cost map using motion primitives is built while using this cost map, anytime Repairing A*(ARA*) global path planning algorithm, which has property of anytime algorithm, is combined with Dynamic Window Approach (DWA ) local path planning algorithm to plan a smooth path from start point to target point and generate the optimal control input for robot motion. Finally, the adaptive monte carlo localization method (KLD-Sampling) is used to locate the robot and then a visual navigation system for mobile robot with Kinect camera is designed. The indoor mobile robot navigation experiment results show that the designed robot navigation system can plan a smooth path which is in accordance with robot kinematics and autonomously avoid the static and moving obstacles in the environment. |
doi_str_mv | 10.1109/ChiCC.2016.7554091 |
format | conference_proceeding |
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Firstly, the improved RBPF (RAO Blackwellized particle filters) algorithm is used to build the 2D grid map of the indoor environment and on the basis of this map, operation of inflating obstacles is applied to build the global grid map considering the actual size of the robot. Secondly, cost map using motion primitives is built while using this cost map, anytime Repairing A*(ARA*) global path planning algorithm, which has property of anytime algorithm, is combined with Dynamic Window Approach (DWA ) local path planning algorithm to plan a smooth path from start point to target point and generate the optimal control input for robot motion. Finally, the adaptive monte carlo localization method (KLD-Sampling) is used to locate the robot and then a visual navigation system for mobile robot with Kinect camera is designed. 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Firstly, the improved RBPF (RAO Blackwellized particle filters) algorithm is used to build the 2D grid map of the indoor environment and on the basis of this map, operation of inflating obstacles is applied to build the global grid map considering the actual size of the robot. Secondly, cost map using motion primitives is built while using this cost map, anytime Repairing A*(ARA*) global path planning algorithm, which has property of anytime algorithm, is combined with Dynamic Window Approach (DWA ) local path planning algorithm to plan a smooth path from start point to target point and generate the optimal control input for robot motion. Finally, the adaptive monte carlo localization method (KLD-Sampling) is used to locate the robot and then a visual navigation system for mobile robot with Kinect camera is designed. The indoor mobile robot navigation experiment results show that the designed robot navigation system can plan a smooth path which is in accordance with robot kinematics and autonomously avoid the static and moving obstacles in the environment.</description><subject>Algorithms</subject><subject>Cameras</subject><subject>Cost map</subject><subject>Dynamic environment</subject><subject>Dynamics</subject><subject>Indoor environments</subject><subject>Kinect Camera</subject><subject>Mobile robots</subject><subject>Motion primitives</subject><subject>Navigation</subject><subject>Navigation systems</subject><subject>Path planning</subject><subject>Robot kinematics</subject><subject>Robot vision systems</subject><subject>Robots</subject><subject>Visual navigation</subject><issn>2161-2927</issn><issn>1934-1768</issn><isbn>9789881563910</isbn><isbn>9881563917</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2016</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotkEtLxDAYRaMgOI7zB3STpZvWvJrHUoovHHCjbkuSfnUibTK2mZH59xZmVhcuhwP3InRDSUkpMff1JtR1yQiVpaoqQQw9QyujtNGaVpIbSs7RglFJC2aYukRX0_RDiJw5vkDrrzDtbI-j3Ydvm0OKuEsjHpILPeAxuZTxX8gb_BYi-Iy9HWC0OETcHqIdgscQ92FMcYCYr9FFZ_sJVqdcos-nx4_6pVi_P7_WD-siMKJz4b0krRBcE-Wk5sqC061mims7N8L6loPgneLgLeuk6xS11IFrKy00Ey1forujdzum3x1MuRnC5KHvbYS0mxqqeSWZmTfO6O0RDQDQbMcw2PHQnH7i_39aXPs</recordid><startdate>20160701</startdate><enddate>20160701</enddate><creator>Xin, Jing</creator><creator>Jiao, Xiao-Liang</creator><creator>Yang, Yin</creator><creator>Liu, Ding</creator><general>TCCT</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope></search><sort><creationdate>20160701</creationdate><title>Visual navigation for mobile robot with Kinect camera in dynamic environment</title><author>Xin, Jing ; Jiao, Xiao-Liang ; Yang, Yin ; Liu, Ding</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i208t-cc60d443807b6837aeb8d82738a07b4acd3e43f73eca2f6bf71a1bebd584824d3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Algorithms</topic><topic>Cameras</topic><topic>Cost map</topic><topic>Dynamic environment</topic><topic>Dynamics</topic><topic>Indoor environments</topic><topic>Kinect Camera</topic><topic>Mobile robots</topic><topic>Motion primitives</topic><topic>Navigation</topic><topic>Navigation systems</topic><topic>Path planning</topic><topic>Robot kinematics</topic><topic>Robot vision systems</topic><topic>Robots</topic><topic>Visual navigation</topic><toplevel>online_resources</toplevel><creatorcontrib>Xin, Jing</creatorcontrib><creatorcontrib>Jiao, Xiao-Liang</creatorcontrib><creatorcontrib>Yang, Yin</creatorcontrib><creatorcontrib>Liu, Ding</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Xin, Jing</au><au>Jiao, Xiao-Liang</au><au>Yang, Yin</au><au>Liu, Ding</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Visual navigation for mobile robot with Kinect camera in dynamic environment</atitle><btitle>2016 35th Chinese Control Conference (CCC)</btitle><stitle>ChiCC</stitle><date>2016-07-01</date><risdate>2016</risdate><spage>4757</spage><epage>4764</epage><pages>4757-4764</pages><eissn>2161-2927</eissn><eissn>1934-1768</eissn><eisbn>9789881563910</eisbn><eisbn>9881563917</eisbn><abstract>To solve the problem of the visual navigation for mobile robot in dynamic environment, a visual navigation system for mobile robot with Kinect camera is designed. Firstly, the improved RBPF (RAO Blackwellized particle filters) algorithm is used to build the 2D grid map of the indoor environment and on the basis of this map, operation of inflating obstacles is applied to build the global grid map considering the actual size of the robot. Secondly, cost map using motion primitives is built while using this cost map, anytime Repairing A*(ARA*) global path planning algorithm, which has property of anytime algorithm, is combined with Dynamic Window Approach (DWA ) local path planning algorithm to plan a smooth path from start point to target point and generate the optimal control input for robot motion. Finally, the adaptive monte carlo localization method (KLD-Sampling) is used to locate the robot and then a visual navigation system for mobile robot with Kinect camera is designed. The indoor mobile robot navigation experiment results show that the designed robot navigation system can plan a smooth path which is in accordance with robot kinematics and autonomously avoid the static and moving obstacles in the environment.</abstract><pub>TCCT</pub><doi>10.1109/ChiCC.2016.7554091</doi><tpages>8</tpages></addata></record> |
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identifier | EISSN: 2161-2927 |
ispartof | 2016 35th Chinese Control Conference (CCC), 2016, p.4757-4764 |
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language | eng |
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source | IEEE Xplore All Conference Series |
subjects | Algorithms Cameras Cost map Dynamic environment Dynamics Indoor environments Kinect Camera Mobile robots Motion primitives Navigation Navigation systems Path planning Robot kinematics Robot vision systems Robots Visual navigation |
title | Visual navigation for mobile robot with Kinect camera in dynamic environment |
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