为避免物流仓库灾害的发生，保证仓库安全，需要对其进行定期巡检。本研究以物流仓巡检任务为背景，研究设计了基于激光雷达的巡检机器人。该机器人包括同步定位与地图构建（Simultaneous Localization and Mapping，SLAM）和路径规划两个单元，并在移动过程中利用红外热成像仪、可燃性气体传感器及高清广角摄像头对现场温度、可燃性气体和巡检现场信息进行采集，最后通过厂内局域网上传至中控室对物流仓进行智慧化管理。本文的主要贡献如下：

本研究针对扩展卡尔曼滤波（Extended Kalman Filter，EKF）算法对非线性函数采用线性截断策略时滤波精度和收敛性都受到影响的问题，提出一种对非线性系统的前二阶矩进行线性近似滤波的方法。在对系统状态方程和观测方程进行泰勒展开时保留二阶项来克服原算法只保留一阶项而造成的滤波精度和收敛性问题。与EKF SLAM相比，二阶EKF SLAM具有较高的滤波精度和收敛精度，整体提升了算法的建图精度。

本研究针对传统A*算法搜索路径转折点较多、路径不够平滑等问题，本文提出一种16邻域A*路径算法。该算法是在传统A*的搜索基础上，引入了16邻域的搜索方法，以此解决原A*算法搜索维度少而造成的路径次优问题。与传统的A*搜索算法相比，16邻域A*搜索路径更加平滑，更加接近实测最优路径。

为避免物流仓库灾害发生，保证仓库安全，本研究设计开发了基于激光雷达的智能化巡检机器人平台。该移动机器人平台实现了对巡检环境热成像、可燃性气体检测、远程视频监控功能，并实现了巡检现场地图构建和定位以及路径最优规划功能。

本文采用EKF SLAM和二阶EKF SLAM进行了场景地图构建。根据选取测试点得出二阶EKF SLAM建图精度比原算法精度平均相对误差降低了4.6%。采用16邻域A*和A*算法规划的距离与实测距离对比，16邻域A*算法平均相对误差降低了4.47%。

In order to avoid disasters in logistics warehouses and ensure warehouse safety, regular inspections are required. In this research, based on the inspection task of logistics warehouse, the research and design of the inspection robot based on lidar are studied. The robot includes two units: Simultaneous Localization and Mapping and path planning, and uses infrared thermal imaging cameras, flammable gas sensors, and high-definition wide-angle cameras to monitor field temperature, flammable gas, and The inspection site information is collected, and finally uploaded to the central control room through the factory LAN for intelligent management of the logistics warehouse. The main contributions of this article are as follows:

This research aims at the problem that the filtering accuracy and convergence are affected when the extended Kalman Filter algorithm adopts the linear truncation strategy for the nonlinear function, and proposes a kind of first second moment of the nonlinear system The method of linear approximation filtering. When performing Taylor expansion on the system state equation and the observation equation, the second-order term is retained to overcome the filtering accuracy and convergence problems caused by the original algorithm only retaining the first-order term. Compared with EKF SLAM, the second-order EKF SLAM has higher filtering accuracy and convergence accuracy, and the overall accuracy of the algorithm is improved.

In this research, the traditional A* algorithm searches for many turning points in the path and the path is not smooth enough. This paper proposes a 16-neighbor A* path algorithm. This algorithm is based on the traditional A* search, and introduces a 16-neighbor search method to solve the suboptimal path problem caused by the original A* algorithm with a small search dimension. Compared with the traditional A* search algorithm, the 16-neighbor A* search path is smoother and closer to the measured optimal path.

In order to avoid logistics warehouse disasters and ensure warehouse safety, this research designed and developed an intelligent inspection robot platform based on lidar. The mobile robot platform realizes the functions of thermal imaging of the inspection environment, flammable gas detection, and remote video monitoring, and realizes the functions of map construction and positioning of the inspection site and optimal path planning.

This paper uses EKF SLAM and second-order EKF SLAM to construct the scene map. According to the selected test points, the accuracy of the second-order EKF SLAM mapping is lower than the average relative error of the original algorithm by 4.6%. Comparing the distance planned by 16-neighbor A* and A* algorithms with the measured distance, the average relative error of the 16-neighbor A* algorithm is reduced by 4.47%.

[1]刘耀华. 人工智能各国战略解读:美国机器人发展路线图[J]. 电信网技术, 2017(02): 39-41.

[2]芦春凡, 宋黎明. 俄罗斯机器人产业发展现状及趋势分析[J]. 黑龙江科技信息, 2017(15): 30.

[3]陶永, 王田苗, 刘辉, 等. 智能机器人研究现状及发展趋势的思考与建议[J]. 高技术通讯, 2019, 29(02): 149-163

[4]徐冽, 何庆柏, 任静, 等. “互联网+”背景下智能配送现状与发展对策[J]. 物流技术, 2021, 40(01): 19-22.

[5]卢月品, 刘晨曦. 2019年中国机器人产业发展形势[J]. 互联网经济, 2019(Z1): 44-49.

[6]许柏杨, 王冬青. 一种改进的EKF-SLAM算法[J]. 制造业自动化, 2019, 41(12): 67-71+94.

[7]杨林志, 高宏力, 宋兴国, 等. 自适应渐消EKF在移动机器人SLAM中的应用[J]. 机械设计与制造, 2019(11): 249-252.

[8]吴晓锋, 陈少斌, 黄宴委. 基于激光雷达的复杂环境定位研究[J]. 机电技术, 2021(01): 31-34.

[9]刘鸿勋, 王伟. 双目相机和激光雷达的融合SLAM研究[J]. 南京师范大学学报(工程技术版), 2021, 21(01): 64-71.

[10]杜边境, 李富强, 刘勇, 等. 基于激光SLAM的移动机器人实验室巡检[J]. 电子测试, 2020(22): 52-53.

[11]郭建军. 矿井变电所机器人巡检平台的研究与应用[J]. 煤矿机电, 2020, 41(05): 1-6.

[12]杨健健, 王超, 张强, 等. 井工巷道环境建模与掘进障碍检测方法研究[J]. 煤炭科学技术, 2020, 48(11): 12-18.

[13]朱蟋蟋, 孙兵, 朱大奇. 基于改进D~*算法的AUV三维动态路径规划[J]. 控制工程, 2021, 28(04): 736-743.

[14]茹锋, 喻阳俭, 王萍, 等. 基于多优化快速扩展随机树算法的足球机器人路径规划[J]. 科学技术与工程, 2019, 19(28): 189-195.

[15]梁彧. 基于改进Dijkstra算法的AGV智能车路径规划[J]. 科技与创新, 2020(24): 159-161.

[16]张许有, 刘有余. 基于位置代价A~*算法的机械臂避障路径规划[J]. 机械设计, 2021, 38(02): 108-113.

[17]王猛, 邢关生. 基于改进蚁群算法的机器人路径规划[J]. 电子测量技术, 2020, 43(24): 52-56.

[18]潘青慧. 自主移动机器人SLAM与路径规划算法研究[D]. 郑州: 郑州大学, 2020.

[19]刘洋, 马建伟, 臧绍飞, 等. 基于融合Bezier优化遗传算法的路径规划[J]. 控制工程, 2021, 28(02): 284-292.

[20]S. Karam, V. Lehtola, G. Vosselman. Strategies To Integrate Imu And Lidar Slam For Indoor Mapping[J]. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020, V-1-2020: 223-230.

[21]Mathieu Labbé, François Michaud. RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation[J]. Journal of Field Robotics, 2019, 36(2): 416-446.

[22]Singh R, Katyal A, Kumar M, et al. Removal of sonar wave interference in multi-robot system for the efficient SLAM by randomized triggering time technique[J]. World Journal of Engineering, 2020, 17(4): 535-542.

[23]An Juan, Mou Hairong, Lu Rui, et al. Localization and Navigation Analysis of Mobile Robot Based on SLAM[J]. Journal of Physics: Conference Series, 2021, 1827(1): 012089.

[24]Shchekotov S V, Smirnov N S, Pashkin M P. The ontology driven SLAM based indoor localization technique[J]. Journal of Physics: Conference Series, 2021, 1801(1): 012007.

[25]Mansour Mostafa, Davidson Pavel, Stepanov Oleg, et al. Towards Semantic SLAM: 3D Position and Velocity Estimation by Fusing Image Semantic Information with Camera Motion Parameters for Traffic Scene Analysis[J]. Remote Sensing, 2021, 13(3).

[26]A Pancham, D Withey, G Bright. Amore: Cnn-Based Moving Object Detection And Removal Towards Slam In Dynamic Environments[J]. South African Journal of Industrial Engineering, 2020, 31(4): 46-58.

[27]Tomono M.. Loop detection for 3D LiDAR SLAM using segment-group matching[J]. Advanced Robotics, 2020 ,34(23): 1530-1544.

[28]H. K. Paikray,P. K. Das,S. Panda. Optimal Multi-robot Path Planning Using Particle Swarm Optimization Algorithm Improved by Sine and Cosine Algorithms[J]. Arabian Journal for Science and Engineering, 2021, 46(4): 3357-3381..

[29]Lisowski Józef. Synthesis of a Path-Planning Algorithm for Autonomous Robots Moving in a Game Environment during Collision Avoidance[J]. Electronics, 2021, 10(6): 675.

[30]Mohammed Yousif,Ahmad Salim,Wisam K. Jummar. A Robotic Path Planning by Using Crow Swarm Optimization Algorithm[J]. International Journal of Mathematical Sciences and Computing(IJMSC), 2021, 7(1): 22-25.

[31]Sangeetha Viswanathan, Krishankumar Raghunathan, Ravichandran Kattur Soundarapandian, et al. A Fuzzy Gain-Based Dynamic Ant Colony Optimization for Path Planning in Dynamic Environments[J]. Symmetry, 2021, 13(2): 280.

[32]Ajeil Fatin Hassan, Ibraheem Ibraheem Kasim, Humaidi Amjad J, et al. A novel path planning algorithm for mobile robot in dynamic environments using modified bat swarm optimization[J]. The Journal of Engineering, 2021, 2021(1): 37-48.

[33]Falcó Antonio, Hilario Lucía, Montés Nicolás, et al. A Path Planning Algorithm for a Dynamic Environment Based on Proper Generalized Decomposition[J]. Mathematics, 2020, 8(12): 2245.

[34]Hussein Mohammed, Lotfi Romdhane, Mohammad A. Jaradat. RRT*N: an efficient approach to path planning in 3D for Static and Dynamic Environments[J]. Advanced Robotics, 2020, 35(3-4): 168-180.

[35]Juliana Veiga C. F. de Lima, Eduardo Morgado Belo, Vinícius Abrão da Silva Marques. Multi-agent path planning with nonlinear restrictions[J]. Evolutionary Intelligence, 2021: 1-11.

[36]Sollehudin I.M, Heerwan P.M, Ishak M.I, Zakaria M.A. Electric Powered Wheelchair Trajectory Planning on Artificial Potential Field Method[J]. IOP Conference Series: Materials Science and Engineering, 2021, 1068(1): 012012.

[37]Cook G, Zhang F. Mobile Robots: Navigation, Control and Sensing, Surface Robots and AUVs[M]. John Wiley & Sons, 2020.

[38]李欲晓, 孙吉利, 吕玮. 重型发动机机器人涂胶系统空间坐标系定位研究[J]. 现代车用动力, 2021(01): 44-47.

[39]Abdalla T Y, Abed A A, Ahmed A A. Mobile robot navigation using PSO-optimized fuzzy artificial potential field with fuzzy control[J]. Journal of Intelligent & Fuzzy Systems, 2017, 32(6): 3893-3908.

[40]Agarwal Divya, Bharti Pushpendra S. Implementing modified swarm intelligence algorithm based on Slime moulds for path planning and obstacle avoidance problem in mobile robots[J]. Applied Soft Computing Journal, 2021, 107: 107372.

[41]刘文之. 基于激光雷达的SLAM和路径规划算法研究与实现[D]. 哈尔滨: 哈尔滨工业大学, 2018.

[42]李增元, 刘清旺, 庞勇. 激光雷达森林参数反演研究进展[J]. 遥感学报, 2016, 20(5): 1138-1150.

[43]Schlarp J, Csencsics E, Schitter G. Optical scanning of a laser triangulation sensor for 3-D imaging[J]. IEEE Transactions on Instrumentation and Measurement, 2019, 69(6): 3606-3613.

[44]刘凯媚, 陶卫, 陈潇, 等. 可自适应位移变化的玻璃厚度激光三角测量方法[J]. 中国激光, 2020, 47(01): 155-162.

[45]夏才初, 那通兴, 张平阳, 等. 智能隧道激光收敛仪的研制和应用[J]. 同济大学学报 (自然科学版), 2017, 45(4): 504-510.

[46]Riemensberger J, Lukashchuk A, Karpov M, et al. Massively parallel coherent laser ranging using a soliton microcomb[J]. Nature, 2020, 581(7807): 164-170.

[47]蔡贵霞, 钱惟贤, 杨锦清, 等. 差分信号时刻鉴别法的脉冲激光测距技术分析[J]. 红外与激光工程, 2019, 48(12): 125-134.

[48]刘耀. 基于激光雷达的室内移动机器人定位技术研究[D]. 大庆: 东北石油大学, 2019.

[49]郭隆臻, 徐定明, 李子信. 移动送料机器人地图构建与运动控制研究[J]. 智能计算机与应用, 2021, 11(01): 55-60.

[50]Tada K. Bayesian estimation of equivalent circuit parameters of photovoltaic cells[J]. Applied Physics Express, 2021, 14(4): 046502.

[51]Sena H J, da Silva F V, Fileti A M F. ANN model adaptation algorithm based on extended Kalman filter applied to pH control using MPC[J]. Journal of Process Control, 2021, 102: 15-23.

[52]崔宾阁, 吴亚男, 钟勇, 等. 高光谱图像滚动引导递归滤波与地物分类[J]. 遥感学报, 2019, 23(03): 431-442.

[53]周振, 王冬青, 许柏杨, 等. 基于改进多新息理论的EKF-SLAM算法[J]. 自动化与仪器仪表, 2020(06): 21-25.

[54]王小娟. 超大规模图的最短路径距离近似算法[D]. 扬州: 扬州大学, 2016.

[55]宫明辉, 乌江, 焦朝勇. 基于模糊自适应扩展卡尔曼滤波器的锂电池SOC估算方法[J]. 电工技术学报, 2020, 35(18): 3972-3978.

[56]Hu Liyao, Li Xiaohua. Adaptive neural finite-time stabilisation for a class of p-normal form nonlinear systems with unknown virtual control coefficients[J]. International Journal of Control, 2021, 94(5): 1386-140.

[57]Zhang C F, Xu J W, Men Y P, et al. Fast radio burst detection in the presence of coloured noise[J]. Monthly Notices of the Royal Astronomical Society, 2021, 503(4): 5223-5231.

[58]朱亚强. 利用蒙特卡洛随机数算法定位时间瓶颈[J]. 科技资讯, 2018, 16(11): 28+30.

[59]Grigorieva Ellina. Optimal Control Theory: Introduction to the Special Issue[J]. Games, 2021, 12(1): 29.

[60]Sedeno-Noda A, Colebrook M. A biobjective Dijkstra algorithm[J]. European Journal of Operational Research, 2019, 276(1): 106-118.

[61]张洋洋. 社交网络中基于内积空间坐标系的距离预测算法研究[D]. 哈尔滨: 哈尔滨工业大学, 2017.

[62]李大林. 基于激光雷达数据的行人探测方法研究[D]. 武汉: 武汉大学, 2018.

[63]刘鹏. 视觉建图技术在智慧医院室内定位导诊中的应用与研究[J]. 中国数字医学, 2018, 13(10): 17-19.

[64]Guan W, Chen S, Wen S, et al. High-accuracy robot indoor localization scheme based on robot operating system using visible light positioning[J]. IEEE Photonics Journal, 2020, 12(2): 1-16.

[65]王帅, 张云洲, 吴成东. 以科研项目为依托建设机器人操作系统实践课程[J]. 实验技术与管理, 2019, 36(01): 188-191+248.

[66]A. Bhargavi Haripriya, K.A. Sunitha, B. Mahima. Development of Low-cost Thermal Imaging System as a Preliminary Screening Instrument[J]. Procedia Computer Science, 2020, 172: 283-288.

[67]Cobianu C, Dumbravescu N, Serban B, et al. Facile 3D nanostructured ZnO-graphene hybrids for gas sensing applications[C]. 2019 International Semiconductor Conference (CAS). IEEE, 2019: 27-30.

[68]葛畅, 白光伟, 沈航, 等. 基于边缘计算的视频监控框架[J]. 计算机工程与设计, 2019, 40(1): 32-39.