皮带廊智能巡检中运动导航研究

皮带廊作为大宗商品制造业企业生产的“大动脉”，关乎着企业日常正常生产工作的开展。为保障皮带廊的工作状态，目前绝大多数企业均采用人工巡检的方式，判断皮带廊是否正常工作。然而随着我国人口红利逐渐减弱，伴随着近年来新冠肺炎疫情的冲击，有经验的工人越来越少，工业界亟需智能巡检的方式来更好地完成巡检工人的日常工作，确保皮带廊可正常运转，第一时间发现问题，清除隐患。

本课题组利用皮带廊原有基础桁架作为轨道，设计一款皮带廊智能巡检机器人，通过收集支撑皮带的托辊运动时所产生的声音信号，判断托辊是否损坏，并通过数据积累，以期未来实现托辊健康状态全生命周期监管。本文主要研究在仅仅依托皮带廊原有钢桁架而不额外架设机器人运行轨道的基础上，为实现准确收集声音信号而必然存在的机器人定位及控制问题。

本课题需要对机器人控制系统进行设计，采用工控机与单片机配合方案完成机器人的巡检功能要求。通过专为巡检机器人设计的搭载STM32G4系列芯片的巡检机器人控制板，配合以调度不同窗口发送指定ID的节点消息的通信方式，驱动机器人各个单元模块。

利用仿真分析，对机器人巡检运动过程中的越障动作和上坡动作两个特殊运动过程进行动力学及运动学仿真。通过仿真分析，解算机器人在越障运动时的控制指标。进而通过分析在上坡运动过程中，机器人自身与轨道之间的几何关系，解算机器人在上坡时所需的速度及动力。

凭借工控机强大的算力，基于高速工业相机，本课题提出基于类事件相机的机器人定位方案，通过对相邻帧的差分、积分，输出事件帧及HDR增强帧，用以识别指定目标，实现机器人精准定位。

[1] 周锐.煤矿皮带机常见故障原因分析及对策[J].科技与创新,2014(06):18-19.
[2] 李治平.煤矿运输机运行状态分析[J].科技创新导报,2010(13):1-1.
[3] CAI Li Mei, QIAN Jia Sheng. A method for detecting miners based on helmets detection in underground coal mine videos[J]. Mining Science and Technology, 2011, 21(4): 553-556.
[4] BIRK A, KENN H. An industrial application of behavior-oriented robotics[C]. IEEE International Conference on Robotics and Automation, 2001(1): 749-754.
[5] 胡绍刚,贾明月,刘君.轨道式变电站巡检机器人的研究与应用[J].电气应用,2013,32(S2):613-616.
[6] 沈超.矿用自动巡检机器人在黄陵一号煤矿的应用[J]. 陕西煤炭,2020,39(02):118-120+141.
[7] 黄嘉盛,卢润戈,邱冠武,等.轮式智能巡检机器人在电力隧道环境应用[J].电工技术,2019(04):78-79.
[8] 虞鸿江.电缆隧道智能巡检机器人设计探析[J].通信电源术,2020,37(02):142-144.
[9] GUEAIEB W, MIAH MS. An Intelligent Mobile Robot Navigation Technique Using RFID Technology[J]. IEEE Transactions on Instrumentation & Measurement, 2008, 57(9): 1908-1917.
[10] ISHINO R, TSUTSUMI F, Detection system of damaged cables using video obtained from an aerial inspection of transmission lines[C]. Power Engineering Society General Meeting, 2004(2): 1857-1862.
[11] 万华,康琛,徐碧川,等.基于AR技术的穿戴式智能巡检系统开发及应用[J].江西电力,2019,43(12):19-21.
[12] 董磊书.基于滑触线监控的控制系统设计与实现[J].自动化应用,2018(10):17-18.
[13] 魏彩颖.无人机管道智能巡检中图像导航关键技术研究[D].银川.北方民族大学,2020:30-45.
[14] 吕小文.悬挂轨道式智能巡检机器人研究[D].杭州.浙江大学,2019:23-25.
[15] 曹军.带式输送机巡检机器人设计与研究[D].徐州.中国矿业大学,2019:15-26.
[16] CHEN Hanxing, TIAN Jun. Research on the Controller Area Network[C]. International Conference on Networking & Digital Society, 2009: 251-254.
[17] 陈曦,刘鲁源,吕伟杰,等.基于排队论的CAN总线消息响应时间建模与分析[J].天津大学学报,2012,45(03):228-235.
[18] LIU Jianxin, GUAN Xuefeng, TAN Ping. The analysis and test of real-time performance for time-triggered CAN bus[C]. 2008 IEEE International Conference on Automation and Logistics, 2008: 3005-3009.
[19] SHEN Y, WANG H, BLAABJERG F, Thermal Modeling and Sizing of PCB Copper Pads, 2018 IEEE Energy Conversion Congress and Exposition (ECCE), 2018: 5087-5093.
[20] 单祥茹.从F3升级到G4,意法半导体STM32锁定下一代数字电源应用[J].中国电子商情(基础电子),2019(07):16-18.
[21] TIE S F, TAN C W. A review of energy sources and energy management system in electric vehicles[J]. Renewable and Sustainable Energy Reviews, 2013, 20(4): 82-102.
[22] SHOSHANI G, MITSCHKE S, STEPHAN S. Industrial Fieldbus technology and Fieldbus cable overview — Cable standards and electrical qualifications[C]. Petroleum and Chemical Industry Conference (PCIC), 2010: 1-10.
[23] 喜崇彬.物流移动机器人电机市场状况与采购选型[J].物流技术与应用.2021,26(12):138-140.
[24] GUEAIEB W, MIAH M S. An Intelligent Mobile Robot Navigation Technique Using RFID Technology[J]. IEEE Transactions on Instrumentation & Measurement, 2008, 57(9): 1908-1917.
[25] 王高山.带式输送机的托辊状态监测及材料失效相关性的研究[D].深圳.南方科技大学, 2021:54-55.
[26] MEAD C. Neuromorphic electronic systems[J]. Proceedings of the IEEE, 1990, 78(10): 1629-1636.
[27] BOAHEN K A. A burst-mode word-serial address-event link-I: transmitter design[J]. IEEE Transactions on Circuits & Systems I Regular Papers, 2004, 51(7): 1269-1280.
[28] POSCH C, MATOLIN D, WOHLGENANNT R. A QVGA 143 DB Dynamic Range Frame-Free PWM Image Sensor With Lossless Pixel-Level Video Compression and Time-Domain CDS[J]. IEEE Journal of Solid-State Circuits, 2011, 46(1): 259–275.
[29] MUEGGLER E, HUBER B, SCARAMUZZA D. Event-based, 6-DOF pose tracking for high-speed maneuvers[C]. IEEE/RSJ International Conference on Intelligent Robots & Systems. IEEE, 2014: 2761−2768.
[30] FINATEU T, NIWA A, MATOLIN D, TSUCHIMOTO K., et al. 5.10 a 1280×720 back illuminated stacked temporal contrast event-based vision sensor with 4.86μm pixels, 1.066geps readout, programmable event-rate controller and compressive data-formatting pipeline[C]. 2020 IEEE International Solid- State Circuits Conference - (ISSCC), 2020: 112-114.
[31] REBECQ H, HORSTSCHAEFER T, GALLEGO G, et al. EVO: A Geometric Approach to Event-Based 6-DOF Parallel Tracking and Mapping in Real Time[J]. IEEE Robotics & Automation Letters, 2017, 2(2): 593-600.
[32] SCIMECA D. Vision Systems Design: Evaluation Kit for HD Event-based Image Sensor[M]. Tulsa: Endeavor Business Media Press.,2021: 175-200.
[33] CONRADT J, COOK M, BERNER R, LICHTSTEINER P, et al. Live Demonstration: A Pencil Balancing Robot using a Pair of AER Dynamic Vision Sensors[C]. International Symposium on Circuits and Systems (ISCAS 2009), 2009: 785-785.
[34] SERRANO-GOTARREDONA R, OSTER M, LICHTSTEINER P, et al. CAVIAR: A 45k Neuron, 5M Synapse, 12G Connects/s AER Hardware Sensory–Processing– Learning–Actuating System for High-Speed Visual Object Recognition and Tracking[J]. IEEE Transactions on Neural Networks, 2009, 20(9): 1417-38.
[35] DELBRUCK T, LANG M. Robotic goalie with 3ms reaction time at 4% CPU load using event-based dynamic vision sensor[J]. Frontiers in neuroscience, 2013(7): 223-24.
[36] LITZENBERGER M, KOHN B, BELBACHIR A N, et al. Estimation of Vehicle Speed Based on Asynchronous Data from a Silicon Retina Optical Sensor[C]. Intelligent Transportation Systems Conference, 2006: 653-658.
[37] ZHU A, YUAN L, CHANEY K, et al. EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras[OL]. (2018-8-13)
[2022-4-8]. https://arxiv.org/abs/1802.06898.
[38] SON B, SUH Y, KIM S, et al. 4.1 A 640×480 dynamic vision sensor with a 9m pixel and 300Meps address-event representation[C]. 2017 IEEE International Solid-State Circuits Conference - (ISSCC), 2017: 66-67.
[39] LICHTSTEINER P, POSCH C, DELBRUCK T. A 128× 128 120 dB 15 μs Latency Asynchronous Temporal Contrast Vision Sensor[J]. IEEE Journal of Solid-State Circuits, 2008, 43(2): 566-576.
[40] GALLEGO G, DELBRUCK T, ORCHARD G M, et al. Event-based Vision: A Survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020(99): 1-1.
[41] Industrial DVS design; key features and applications [OL]. (2020-8-5)
[2022-4-8]. http://rpg.ifi.uzh.ch/docs/CVPR19workshop/CVPRW19_Eric_Ryu_Samsung.pdf.
[42] REBECQ H, RANFTL R, KOLTUN V, et al. High Speed and High Dynamic Range Video with an Event Camera[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019(99): 1-1.