全文总字数：4374字
1. 毕业设计（论文）主要内容：
1.前期知识储备：通过阅读相关文献，了解机器学习、三维图像处理算法、机器人抓取姿态抓取位置决策、python、tensorflow、机器人操作系统（ROS）等相关知识。2.设计功能：本设计的任务包括：1）基于卷积神经网络，对待抓取的目标进行分析得到平行夹爪的抓取位置以及抓取姿态；2）使用空间的点组以及ROS标定模块，完成机器人伺服抓取的手眼标定；使用MoveIt模块依据映射规则完成机器人的控制； 3）在2）中的自定义环境中实现、验证和分析所设计的神经网络和机器人抓取算法。
2. 毕业设计（论文）主要任务及要求
1.查阅15篇相关文献（不少于5篇外文文献），并每篇书写200—300字文献摘要（装订成册，带封面）；2.认真填写周记，完成至少1500字开题报告（“设计的目的及意义”至少800汉字；“基本内容和技术方案”至少400汉字；进度安排应尽可能详细；）；3.完成5000中文字以上的相关英文专业文献翻译，并装订成册（中英文一起，带封面）；4.完成方法研究、算法设计与实现；5.按武汉理工大学理工类本科生毕业论文撰写规范撰写毕业论文，完成10000字以上的毕业论文；6.进行论文答辩。
3. 毕业设计（论文）完成任务的计划与安排
1.2020/1/11—2020/1/24：明确选题，查阅相关文献，外文翻译和撰写开题报告； 2.2020/1/25—2020/4/30：系统架构，系统设计与开发（或算法研究与设计）、系统测试、分析、比较与完善； 3.2020/5/1—2020/5/25：撰写论文初稿；修改论文，定稿并提交论文评审；4.2020/5/26—2020/6/6：准备论文答辩。
4. 主要参考文献
[1]Morrison D , Corke P , Leitner, Jürgen. Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach[J]. IEEE Robotics: Science and Systems,2018,14(2):21-31[2]Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. SegNet: A Deep Convolutional EncoderDecoder Architecture for Image Segmentation. arXiv preprint arXiv:1511.00561,2015.[3]Antonio Bicchi and Vijay Kumar. Robotic Grasping and Contact: A Review[C]. In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2000: 348-353.[4]Jeannette Bohg, Antonio Morales, Tamim Asfour, and Danica Kragic. Data-Driven Grasp Synthesis–A Survey [J]. IEEE Transactions on Robotics, 2014, 30(2):289-309.[5]S. Kumra and C. Kanan. Robotic Grasp Detection using Deep Convolutional Neural Networks[C]. In Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS),2017:769–776.[6]Berk Calli, Aaron Walsman, Arjun Singh, Siddhartha Srinivasa, Pieter Abbeel, and Aaron M Dollar. Benchmarking in Manipulation Research: Using the YaleCMU-Berkeley Object and Model Set [J]. IEEE Robotics Automation Magazine，2015，22(3):36-52.[7]Jürgen Leitner, Adam W Tow, Niko Sunderhauf, Jake EDean, Joseph W Durham, Matthew Cooper, Markus Eich, Christopher Lehnert, Ruben Mangels, Christopher McCool, et al. The ACRV Picking Benchmark: A Robotic Shelf Picking Benchmark to Foster Reproducible Research[C]. IEEE International Conference on Robotics and Automation (ICRA), 2017:4705-4712.[8]Ian Lenz, Honglak Lee, and Ashutosh Saxena. Deep learning for detecting robotic grasps[J]. The International Journal of Robotics Research,2015,34(4-5):705-724.[9]Sergey Levine, Peter Pastor, Alex Krizhevsky, and Deirdre Quillen. Learning Hand-Eye Coordination for Robotic Grasping with Large-Scale Data Collection[C]. International Symposium on Experimental Robotics,2016:173-184.[10]Kota Hara, Raviteja Vemulapalli, and Rama Chellappa. Designing Deep Convolutional Neural Networks for Continuous Object Orientation Estimation. arXiv preprint arXiv:1702.01499,2017[11]Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep Residual Learning for Image Recognition[C]. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2016:770-778.[12]Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully Convolutional Networks for Semantic Segmentation[C]. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2015:3431-3440.[13]Edward Johns, Stefan Leutenegger, and Andrew J. Davison. Deep Learning a Grasp Function for Grasping under Gripper Pose Uncertainty[C]. In Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),2016: 4461-4468.[14]Jeffrey Mahler, Florian T. Pokorny, Brian Hou, Melrose Roderick, MichaelLaskey, MathieuAubry, KaiKohlhoff, Torsten Kroger, James Kuffner, and Ken Goldberg. DexNet 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards[C]. In Proc. of the IEEE International Conference on Robotics and Automation (ICRA),2016:1957-1964.[15]Lerrel Pinto and Abhinav Gupta. Supersizing selfsupervision: Learning to grasp from 50k tries and 700 robot hours[C]. In Proc. of the IEEE International Conference on Robotics and Automation (ICRA),2016:3406-3413.[16]Carlos Rubert, Daniel Kappler, Antonio Morales, Stefan Schaal, and Jeannette Bohg. On the Relevance of Grasp Metrics for Predicting Grasp Success[C]. In Proc. of the IEEE/RSJ International Conference of Intelligent Robots and Systems (IROS), 2017:265-272.
以上是毕业论文任务书，课题毕业论文、开题报告、外文翻译、程序设计、图纸设计等资料可联系客服协助查找。
