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基于激光雷達(dá)的巡檢機器人導(dǎo)航系統(tǒng)研究
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北京市科技計劃項目（D161100001416002）,、國家國際科技合作專項（2015DFG12280）、國家自然科學(xué)基金項目（31571570）和國家重點研發(fā)計劃項目（2017YFD0700400-2017YFD0700403）


Navigation System for Inspection Robot Based on LiDAR
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    摘要:

    智能巡檢機器人能夠高效,、可靠地完成巡檢任務(wù),，降低工作人員的勞動強度,，準(zhǔn)確,、穩(wěn)定的導(dǎo)航定位是巡檢機器人執(zhí)行巡檢任務(wù)的基礎(chǔ),。本文研究了基于激光雷達(dá)的巡檢機器人導(dǎo)航系統(tǒng),，可實現(xiàn)機器人在室內(nèi)外環(huán)境下的地圖建立,、路徑規(guī)劃和導(dǎo)航定位,。導(dǎo)航系統(tǒng)由遠(yuǎn)程監(jiān)控平臺與巡檢機器人組成，遠(yuǎn)程監(jiān)控平臺發(fā)布巡檢任務(wù),、監(jiān)控機器人狀態(tài),、查詢與存儲檢測數(shù)據(jù),，巡檢機器人可實現(xiàn)自主導(dǎo)航定位、遍歷檢測點,、執(zhí)行數(shù)據(jù)采集等巡檢任務(wù),，二者通過無線網(wǎng)絡(luò)實現(xiàn)遠(yuǎn)程數(shù)據(jù)交互。融合激光雷達(dá)與編碼器信息,，使用高魯棒性Gmapping算法建立二維環(huán)境地圖,。根據(jù)地圖與檢測點信息，采用分支界定算法搜索最優(yōu)巡檢路線,，以減少巡檢時間和能源消耗,。使用自適應(yīng)蒙特卡羅定位（AMCL）算法估計機器人位置和姿態(tài)，結(jié)合巡檢路線,，進行導(dǎo)航定位,。根據(jù)橫向偏差與航向偏差，通過經(jīng)典的PID算法完成機器人驅(qū)動控制,。機器人搭載可見光相機與紅外相機,，可對目標(biāo)進行可見光通道與紅外通道的融合圖像檢測。對巡檢機器人進行了室內(nèi)導(dǎo)航定位試驗,，試驗結(jié)果表明,，在1m/s的速度下，位置與航向偏差的平均絕對誤差（MAE）分別小于5cm和1.1°,，標(biāo)準(zhǔn)差（SD）分別小于5cm和1.5°,，能夠滿足巡檢導(dǎo)航定位的要求。

    Abstract:

    The intelligent inspection robots can complete the inspection task efficiently and reliably, and reduce the labor intensity of staff. Accurate and stable navigation positioning is the basis for inspection tasks of inspection robots. A navigation system based on light detection and ranging (LiDAR) was developed to achieve mapping, path planning and navigation positioning of the robot in both indoor and outdoor environments. The navigation system was composed of remote monitoring platform and inspection robot. The remote monitoring platform can issue inspection tasks, monitor robot status, query and store detection data, while the inspection robot can conduct navigation and positioning autonomously, traverse detection point, complete data acquisition and other inspection tasks. The platform and inspection robot exchanged remote data through wireless network. To build the 2D environment map, the information from LiDAR and encoder was combined by using a robust Gmapping algorithm. Based on the information of the map and detection points, the branch-and-bound algorithm was applied to search the optimal inspection route in order to reduce inspection time and energy consumption. Adaptive Monte Carlo localization (AMCL) algorithm was used to estimate the position and posture of the robot. The navigation and positioning was achieved by combining the position, posture and the inspection route calculated previously. Robot driver control was completed based on classic PID algorithm with the input of lateral deviation and heading deviation. The robot was equipped with RGB camera and IR camera, and targets can be detected more efficiently using the fusion information of visible and infrared images. In order to verify the accuracy of the navigation system, the indoor navigation positioning experiment was carried out in this research. With the speed of 1m/s, the mean absolute error (MAE) of the position and heading deviation was less than 5cm and 1.1° and the standard deviation (SD) was less than 5cm and 1.5°, which can meet the requirements of inspection navigation and positioning.

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季宇寒,李寒,張漫,王琪,賈稼,王庫.基于激光雷達(dá)的巡檢機器人導(dǎo)航系統(tǒng)研究[J].農(nóng)業(yè)機械學(xué)報,2018,49(2):14-21. JI Yuhan, LI Han, ZHANG Man, WANG Qi, JIA Jia, WANG Ku. Navigation System for Inspection Robot Based on LiDAR[J]. Transactions of the Chinese Society for Agricultural Machinery,2018,49(2):14-21.

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  • 收稿日期:2017-11-10
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  • 在線發(fā)布日期: 2018-02-10
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