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提供最先进的智能机器人科研平台,助力高校用户发文章和申请项目

室内外移动抓取

通过对Clearpath Husky UGV 和 Universal Robots UR5机械臂的优化布置与系统整合,实现更加智能通用的机器人柔性任务解决方案,例如:危险物品排爆、货物搬运、产品分拣,以及替代人到危险位置勘察,同时也可用于教学科研,包括人机协调,机器人学习,柔顺操作,远程遥操作,双臂协作,机器人抓取以及多模态协作装配等。

项目课程

本方案硬件需求简单,接口开放,对接方便,具体实施快捷,适合快速改造上手。 通过以上分析,以及我公司实际经验得知,本方案稳定性强,科研价值突出(多种传感器融合以及全部基于ROS开发)。另外,该方案在工业应用领域也具有很高得适用价值,比如物品的自动化搬运,远程拆弹操作,地面巡检以及一些特殊行业如危险作业等等。

【实验教学目标 】

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【主要仪器设备 】

该方案主要硬件包括:1台HUSKY移动平台车、1台UR5机械臂以及各种传感器视觉系统等,通过自主搭配满足演示移动抓取物体等需求。

【实验项目设置 】

THESIS CONTENTS

1. 地图构建、定位和导航(SLAM)

1) Husky的导航控制包用于其他机器人的控制

Sokolov M, Lavrenov R, Gabdullin A, et al. 3D modelling and simulation of a crawler robot in ROS/Gazebo[C]. Proceedings of the 4th International Conference on Control, Mechatronics and Automation, 2016: 61-65.

2) 基于里程计、IMU和激光雷达进行避障研究

Xie H, Gao J, Zuo L, et al. An Improved Obstacle Avoidance Method for Robot Based on Constraint of Obstacle Boundary Condition[C]. 2017 2nd International Conference on Cybernetics, Robotics and Control (CRC), 2017: 28-32.

3) Husky应用于农田、果园环境,进行SLAM建图和农作物智能监测

Habibie N, Nugraha A M, Anshori A Z, et al. Fruit mapping mobile robot on simulated agricultural area in Gazebo simulator using simultaneous localization and mapping (SLAM)[C]. 2017 International Symposium on Micro-NanoMechatronics and Human Science (MHS), 2017: 1-7.

4) 基于机器学习的方法识别目标物并规划机器人运动路径

Ø Paxton C, Katyal K, Rupprecht C, et al. Learning to Imagine Manipulation Goals for Robot Task Planning. arXiv e-prints. 2017.

Ø Paxton C, Barnoy Y, Katyal K, et al. Visual Robot Task Planning. arXiv e-prints. 2018.

5) 基于模糊推理的移动机器人路径选择

Shiwei W, Panzica A C, Padir T. Motion control for intelligent ground vehicles based on the selection of paths using fuzzy inference[C]. 2013 IEEE Conference on Technologies for Practical Robot Applications (TePRA), 2013: 1-6.

 

2. 人机协作

1) 战争环境中士兵与机器人通信与协作

Barber D J, Abich J, Phillips E, et al. Field Assessment of Multimodal Communication for Dismounted Human-Robot Teams[J]. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2015, 59(1): 921-925.

2) Husky与特警协作

Bethel C L, Carruth D, Garrison T. Discoveries from integrating robots into SWAT team training exercises[C]. 2012 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), 2012: 1-8.

3) 基于语言驱动的导航方法,用于在室外环境中控制移动机器人

Boularias A, Duvallet F, Oh J, et al. Grounding spatial relations for outdoor robot navigation[C]. 2015 IEEE International Conference on Robotics and Automation (ICRA), 2015: 1976-1982.

4) 移动机器人用于巡逻,监视,搜救和人类危险任务

Arokiasami W A, Vadakkepat P, Tan K C, et al. Interoperable multi-agent framework for unmanned aerial/ground vehicles: towards robot autonomy[J]. Complex & Intelligent Systems, 2016, 2(1): 45-59.

5) Husky应用于人机智能交互系统

Jevtić A, Lucet E, Kozlov A, et al. INTRO: A multidisciplinary approach to intelligent Human-Robot Interaction[C]. World Automation Congress 2012, 2012: 1-6.

6) 人机语音交互控制

Ø Chung I, Propp O, Walter M R, et al. On the performance of hierarchical distributed correspondence graphs for efficient symbol grounding of robot instructions[C]. 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015: 5247-5252.

Ø Pourmehr S, Thomas J, Vaughan R. What Untrained People Do When Asked "Make The Robot Come To You"[C]. The Eleventh ACM/IEEE International Conference on Human Robot Interaction, 2016: 495-496.

 

3. 危险场景作业

1) Husky被用于参加人道主义机器人和自动化技术挑战赛(HRATC)挑战赛,进行救援和危险作业。

Madhavan R, Marques L, Prestes E, et al. 2015 humanitarian robotics and automation technology challenge. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS, 2015.

2) 对机器人进行远程遥操作,以完成特定的任务或危险任务

Cabrita G, Madhavan R, Marques L. A Framework for Remote Field Robotics Competitions[C]. 2015 IEEE International Conference on Autonomous Robot Systems and Competitions, 2015: 192-197.

3) 战争环境中在任务命令和环境约束下机器人运动策略研究

Talone A B, Phillips E, Ososky S, et al. An Evaluation of Human Mental Models of Tactical Robot Movement[J]. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2015, 59(1): 1558-1562.

 

4. 多机器人协作

1) 多机器人分布式调度优化

Kattepur A, Dohare H, Mushunuri V, et al. Resource Constrained Offloading in Fog Computing[J], 2016.

2) 多机器人集群协同搜救

Kumar A S, Manikutty G, Bhavani R R, et al. Search and rescue operations using robotic darwinian particle swarm optimization[C]. 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017: 1839-1843.

3) 多机器人监控系统

Hill R C, Lafortune S. Scaling the formal synthesis of supervisory control software for multiple robot systems[C]. 2017 American Control Conference (ACC), 2017: 3840-3847.


5. 机器人控制和分析

1) 将神经网络用于机器人的运动控制

Hinkel G, Groenda H, Vannucci L, et al. A domain-specific language (DSL) for integrating neuronal networks in robot control[C]. Proceedings of the 2015 Joint MORSE/VAO Workshop on Model-Driven Robot Software Engineering and View-based Software-Engineering, 2015: 9-15.

2) 基于学习的机器人轨迹跟踪鲁棒控制

Ø Ostafew C J, Schoellig A P, Barfoot T D. Conservative to confident: Treating uncertainty robustly within Learning-Based Control[C]. 2015 IEEE International Conference on Robotics and Automation (ICRA), 2015: 421-427.

Ø Ostafew C J, Schoellig A P, Barfoot T D. Learning-based nonlinear model predictive control to improve vision-based mobile robot path-tracking in challenging outdoor environments[C]. 2014 IEEE International Conference on Robotics and Automation (ICRA), 2014: 4029-4036.

Ø Ostafew C J, Schoellig A P, Barfoot T D, et al. Learning-based Nonlinear Model Predictive Control to Improve Vision-based Mobile Robot Path Tracking[J]. Journal of Field Robotics, 2016, 33(1): 133-152.

Ø Ostafew C J, Schoellig A P, Barfoot T D. Robust Constrained Learning-based NMPC enabling reliable mobile robot path tracking[J]. The International Journal of Robotics Research, 2016, 35(13): 1547-1563.

3) 开发监视和调试分布式机器人系统的新工具,并对机器人中间件系统进行补充

Monajjemi V, Wawerla J, Vaughan R. Drums: A Middleware-Aware Distributed Robot Monitoring System[C]. 2014 Canadian Conference on Computer and Robot Vision, 2014: 211-218.

4) Husk验证在线学习机器人动力学的多模态模型

Mckinnon C D, Schoellig A P. Learning multimodal models for robot dynamics online with a mixture of Gaussian process experts[C]. 2017 IEEE International Conference on Robotics and Automation (ICRA), 2017: 322-328.

5) 机器人可达性分析,对多款机器人进行分析,包括Husky+UR5

Makhal A, Goins A K. Reuleaux: Robot Base Placement by Reachability Analysis[C]. 2018 Second IEEE International Conference on Robotic Computing (IRC), 2018: 137-142.

6) 野外滑移机器人路径跟随

Ø Rajagopalan V,   M, Kelly A. Slip-aware Model Predictive optimal control for Path following[C]. 2016 IEEE International Conference on Robotics and Automation (ICRA), 2016: 4585-4590.

Ø Ostafew C J, Schoellig A P, Barfoot T D. Visual teach and repeat, repeat, repeat: Iterative Learning Control to improve mobile robot path tracking in challenging outdoor environments[C]. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013: 176-181.

7) 滑移式机器人的功率特性研

Dogru S, Marques L. Power Characterization of a Skid-Steered Mobile Field Robot[C]. 2016 International Conference on Autonomous Robot Systems and Competitions (ICARSC), 2016: 15-20.

 

6. 勘测

1) Husky装载定位设备在冰冻湖面上对湖中鱼群进行探测定位。

Tokekar P, Hook J V, Isler V. Active target localization for bearing based robotic telemetry[C]. 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011: 488-493.

2) Husky搭载测量设备自动对一些基础设施如桥梁等进行结构健康监测。

Lins R G, Givigi S N. Autonomous robot system architecture for automation of structural health monitoring[C]. 2016 Annual IEEE Systems Conference (SysCon), 2016: 1-7.

3) 将气体传感器安装在Husky上进行气体探测和气源定位

Vuka M, Schaffernicht E, Schmuker M, et al. Exploration and localization of a gas source with MOX gas sensors on a mobile robot — A Gaussian regression bout amplitude approach[C]. 2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN), 2017: 1-3.

4) Husky上安装光谱仪,寻找地面潜在的生物气体排放

Anderson G T, Mahdi S, Khidir J, et al. Field studies of a robot system to measure ground emissions of methane[C]. 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2014: 808-812.

5) 机器人辅助气体断层扫描,气源定位

Bennetts V H, Schaffernicht E, Stoyanov T, et al. Robot assisted gas tomography — Localizing methane leaks in outdoor environments[C]. 2014 IEEE International Conference on Robotics and Automation (ICRA), 2014: 6362-6367.

7) 矿区测绘

Ø Neumann T, Ferrein A, Kallweit S, et al. Towards a mobile mapping robot for underground mines[C]. Proceedings of the 2014 PRASA, RobMech and AfLaT International Joint Symposium, Cape Town, South Africa, 2014: 27-28.

Ø Gallant M J, Marshall J A. Two-Dimensional Axis Mapping Using LiDAR[J]. IEEE Transactions on Robotics, 2016, 32(1): 150-160.

Ø Gallant M J, Marshall J A. Automated rapid mapping of joint orientations with mobile LiDAR[J]. International Journal of Rock Mechanics & Mining Sciences, 2016, 90: 1-14.

Ø Ferrein A, Kallweit S, Lautermann M. Towards an autonomous pilot system for a tunnel boring machine[C]. 2012 5th Robotics and Mechatronics Conference of South Africa, 2012: 1-6.

 

7. 运动目标跟踪

1) 将昆虫神经生理学知识应用于移动机器人目标跟踪

Bagheri Z M, Cazzolato B S, Grainger S, et al. An autonomous robot inspired by insect neurophysiology pursues moving features in natural environments[J]. Journal of neural engineering, 2017, 14(4): 046030.

2) Husky对人进行跟踪和跟随

Ø Olmedo N A, Zhang H, Lipsett M. Mobile robot system architecture for people tracking and following applications[C]. 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014), 2014: 825-830.

Ø Leigh A, Pineau J, Olmedo N, et al. Person tracking and following with 2D laser scanners[C]. 2015 IEEE International Conference on Robotics and Automation (ICRA), 2015: 726-733.

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    应用案例—多伦多大学

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    方案五

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    方案四

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    方案三

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    方案二

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    基础版

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