访问计数 200738 (自2016年5月)

Led by Prof. Zhiqiang Zheng, our NuBot team was founded in 2004. Currently we have two full professors (Prof. Zhiqiang Zheng and Prof. Hui Zhang), one associate professor (Prof. Huimin Lu), one assistant professor (Dr. Junhao Xiao), and several graduate students. Till now, 8 team members have obtained their doctoral degree with the research on RoboCup Middle Size League (MSL), and more than 20 have obtained their master degrees. For more detail of each member please see NuBoters.


As shown in the figure below, five generations of robots have been created since 2004. We participated in RoboCup Simulation and Small Size League (SSL) initially. Since 2006, we have been participating in RoboCup MSL actively, e.g., we have been to Bremen, Germany (2006), Atlanta, USA (2007), Suzhou, China (2008), Graz, Austria (2009), Singapore (2010), Eindhoven, Netherlands (2013), Joao Pessoa, Brazil (2014), Hefei, China (2015),  Leipzig Germany (2016), Nagoya Japan(2017), Montréal Canada(2018) and Sydney Australia(2019) . We have also been participating in RoboCup China Open since it was launched in 2006.


5 generations NuBot robots


The NuBot robots have been employed not only for RoboCup, but also for other research as an ideal test bed more than robot soccer. As a result, we have published more than 70 journal papers and conference papers. For more detail please see the publication list. Our current research mainly focuses on multi-robot coordination, robust robot vision and formation control.


The following items are our team description papers (TDPs) which illustrates our research progress over the past years.




发布时间:01/05/2016 12:58
最后编辑:周智千
0?1470885445
代维 TO  NuBot Research Team | Qualifications
发布时间:01/17/2018 19:37
更新时间:02/17/2018 11:35

1. Team description Paper

The team description paper can be downloaded from here, with the main contribution of a newly designed three-wheel robot.


2. 5 Papers in recent 5 years

[1] Wei Dai, Huimin Lu, Junhao Xiao and Zhiqiang Zheng. Task Allocation without Communication Based on Incomplete Information Game Theory for Multi-robot Systems. Journal of Intelligent & Robotic Systems, 2018. [PDF]

[2] Junhao Xiao, Dan Xiong, Weijia Yao, Qinghua Yu, Huimin Lu and Zhiqiang Zheng. Building Software System and Simulation Environment for RoboCup MSL Soccer Robots Based on ROS and Gazebo. Springer Book on Robot Operating System (ROS) – The Complete Reference (Volume 2), pp. 597-631, Springer, 2017. [PDF]

[3] Sha Luo, Weijia Yao, Qinghua Yu, Junhao Xiao, Huimin Lu and Zongtan Zhou. Object Detection Based on GPU Parallel Computing for RoboCup Middle Size League. Proceedings of the 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO 2017), Macau, 2017, pp. 86-91. [PDF]

[4] Wei Dai, Qinghua Yu, Junhao Xiao and Zhiqiang Zheng. Communication-Less Cooperation Between Soccer Robots. RoboCup 2016: Robot World Cup XX, pp. 356-367, Springer, 2016. [PDF]
[5] Dan Xiong, Junhao Xiao, Huimin Lu, et al, The design of an intelligent soccer-playing robot, Industrial Robot: An International Journal, 43(1): 91-102, 2016. [PDF]


3. Results and awards in recent 3 years

2017

  • 3rd place in MSL scientific challenge in RoboCup 2017, Nagoya, Japan

  • 3rd place in MSL technique challenge in RoboCup 2017, Nagoya, Japan

  • 4th place in MSL of RoboCup 2017, Nagoya, Japan

  • 3rd place in MSL of RoboCup 2017 ChinaOpen, RiZhao, China

  • 1st place in MSL scientific challenge of RoboCup 2016 ChinaOpen, RiZhao, China

2016

  • 3rd place in MSL scientific challenge in RoboCup 2016, Leipzig, Germany

  • 4th place in MSL of RoboCup 2016, Leipzig, Germany

  • 3rd place in MSL of RoboCup 2016 ChinaOpen, Hefei, China

  • 1st place in MSL scientific challenge of RoboCup 2016 ChinaOpen, Hefei, China

2015

  • 2rd place in MSL technique challenge in RoboCup 2015, Hefei, China

  • 3rd place in MSL scientific challenge in RoboCup 2015, Hefei, China

  • 6th place in MSL of RoboCup 2015, Hefei, China


4. Qualification video

The qualification video for RoboCup 2018 Montreal, Canada can be found at our youku channel (recommended for users in China) or our YouTube channel (recommended for users out of China).


5. Mechanical and Electrical Description and Software Flow Chart

NuBot Team Mechanical and Electrical Description together with a Software Flow Chart can be downloaded from here.


6. Contributions to the RoboCup MSL community

  • Junhao Xiao, one member of NuBot team, served to MSL community as an EC member. He also served as a MSL TC member of RoboCup 2016 Leipzig, Germany, a member of MSL TC and OC of RoboCup 2015 Hefei, China, and local chair of RoboCup 2015 MSL.
  • Huimin Lu, one member of NuBot team, served to MSL community as a member of TC and OC of RoboCup 2008 Suzhou, and he was also appointed as the local chair of RoboCup 2008 MSL. He was a member of TC of RoboCup 2011 Istanbul.
  • Junchong Ma, one member of NuBot team, served to MSL community as a member of TC RoboCup 2018 Montreal, Canada.
  • We released the source code of our robots and a simulation system under an open source license. Particularly, this simulation system supports 3D simulation of the MSL competition between two teams, which was employed in 2016 and 2017 China Robot Competition. It can also be used for the research of multi-robot coordination control, such as task allocation and formation control.


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发布时间:06/23/2016 22:35
更新时间:12/22/2017 17:37

This video is the accompanying video of the paper: Yi Liu, Yuhua Zhong, Xieyuanli Chen, Pan Wan, Huimin Lu, Junhao Xiao, Hui Zhang, The Design of a Fully Autonomous Robot System for Urban Search and Rescue, Proceedings of the 2016 IEEE International Conference on Information and Automation, 2016.

Abstract: Autonomous robots in urban search and rescue (USAR) have to fulfill several tasks at the same time: localization, mapping, exploration, object recognition, etc. This paper describes the whole system and the underlying research of the NuBot rescue robot for participating RoboCup Rescue competition, especially in exploring the rescue environment autonomously. A novel path following strategy and a multi-sensor based controller are designed to control the robot for traversing the unstructured terrain. The robot system has been successfully applied and tested in the RoboCup Rescue Robot League (RRL) competition and won the championship of 2016 RoboCup China Open RRL competition.


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发布时间:10/17/2016 08:43
更新时间:12/22/2017 17:36

This video is the accompanying video for the following paper: Huimin Lu, Junhao Xiao, Lilian Zhang, Shaowu Yang, Andreas Zell. Biologically Inspired Visual Odometry Based on the Computational Model of Grid Cells for Mobile Robots. Proceedings of the 2016 IEEE Conference on Robotics and Biomimetics, 2016.

Abstract: Visual odometry is a core component of many visual navigation systems like visual simultaneous localization and mapping (SLAM). Grid cells have been found as part of the path integration system in the rat's entorhinal cortex, and they provide inputs for place cells in the rat's hippocampus. Together with other cells, they constitute a positioning system in the brain. Some computational models of grid cells based on continuous attractor networks have also been proposed in the computational biology community, and using these models, self-motion information can be integrated to realize dead-reckoning. However, so far few researchers have tried to use these computational models of grid cells directly in robot visual navigation in the robotics community. In this paper, we propose to apply continuous attractor network model of grid cells to integrate the robot's motion information estimated from the vision system, so a biologically inspired visual odometry can be realized. The experimental results show that good dead-reckoning can be achieved for different mobile robots with very different motion velocities using our algorithm. We also implement a full visual SLAM system by simply combining the proposed visual odometry with a quite direct loop closure detection derived from the well-known RatSLAM, and comparable results can be achieved in comparison with RatSLAM.

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发布时间:07/16/2016 16:10
更新时间:12/22/2017 17:34

Title

Real-time Terrain Classification for Rescue Robot Based on Extreme Learning Machine

Author


Yuhua Zhong, Junhao Xiao, Huimin Lu and Hui Zhang

Abstract

Full autonomous robots in urban search and rescue (USAR) have to deal with complex terrains. The real-time recognition of terrains in front could effectively improve the ability of pass for rescue robots. This paper presents a real-time terrain classification system by using a 3D LIDAR on a custom designed rescue robot. Firstly, the LIDAR state estimation and point cloud registration are running in parallel to extract the test lane region. Secondly, normal aligned radial feature (NARF) is extracted and downscaled by a distance based weighting method. Finally, an extreme learning machine (ELM) classifier is designed to recognize the types of terrains. Experimental results demonstrate the effectiveness of the proposed system.

Video

The video can be found here if the below link does not work.


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发布时间:06/23/2016 22:05
更新时间:12/22/2017 17:33

This video is the accompanying video for the paper: Yuxi Huang, Ming Lv, Dan Xiong, Shaowu Yang, Huimin Lu, An Object Following Method Based on Computational Geometry and PTAM for UAV in Unknown Environments. Proceedings of the 2016 IEEE International Conference on Information and Automation, 2016.

Abstract: This paper introduces an object following method based on the computational geometry and PTAM for Unmanned Aerial Vehicle(UAV) in unknown environments. Since the object is easy to move out of the field of view(FOV) of the camera, and it is difficult to make it back to the field of camera view  just by relative attitude control, we propose a novel solution to re-find the object based on the visual simultaneous localization and mapping (SLAM) results by PTAM. We use a pad as the object which includes a letter H surrounded by a circle. We can get the 3D position of the center of the circle in camera coordinate system using the computational geometry. When the object moves out of the FOV of the camera, the Kalman filter is used to predict the object velocity, so the pad can be searched effectively. We demonstrate that the ambiguity of the pad's localization has little impact on object following through experiments. The experimental results also validate the effectiveness and efficiency of the proposed method.


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发布时间:03/01/2017 13:40
更新时间:12/22/2017 17:30

This video is the accompanying video for the following paper: Weijia Yao, Zhiwen Zeng, Xiangke Wang, Huimin Lu, Zhiqiang Zheng. Distributed Encirclement Control with Arbitrary Spacing for Multiple Anonymous Mobile Robots. Proceedings of the 36th Chinese Control Conference, 2017.

Abstract: Encirclement control enables a multi-robot system to rotate around a target while they still preserve a circular formation, which is useful in real world applications such as entrapping a hostile target. In this paper, a distributed control law is proposed for any number of anonymous and oblivious robots in random three dimensional positions to form a specified circular formation with any desired inter-robot angular distances (i.e. spacing) and encircle around the target. Arbitrary spacing is useful for a system composed of heterogeneous robots which, for example, possess different kinematics capabilities, since the spacing can be designed manually for any specific purpose. The robots are modelled by single-integrator models, and they can only sense the angular positions of their two neighboring robots, so the control law is distributed. Theoretical analysis and simulation results are provided to prove the stability and effectiveness of the proposed control strategy.

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发布时间:05/19/2017 08:11
更新时间:12/22/2017 17:28

This video is about the experimental results of the following paper: Xieyuanli Chen, Huimin Lu, Junhao Xiao, Hui Zhang, Pan Wang. Robust relocalization based on active loop closure for real-time monocular SLAM. Proceedings of the 11th International Conference on Computer Vision Systems (ICVS), 2017.


Abstract. Remarkable performance has been achieved using the state-of-the-art monocular Simultaneous Localization and Mapping (SLAM) algorithms. However, tracking failure is still a challenging problem during the monocular SLAM process, and it seems to be even inevitable when carrying out long-term SLAM in large-scale environments. In this paper, we propose an active loop closure based relocalization system, which enables the monocular SLAM to detect and recover from tracking failures automatically even in previously unvisited areas where no keyframe exists. We test our system by extensive experiments including using the most popular KITTI dataset, and our own dataset acquired by a hand-held camera in outdoor large-scale and indoor small-scale real-world environments where man-made shakes and interruptions were added. The experimental results show that the least recovery time (within 5ms) and the longest success distance (up to 46m) were achieved comparing to other relocalization systems. Furthermore, our system is more robust than others, as it can be used in different kinds of situations, i.e., tracking failures caused by the blur, sudden motion and occlusion. Besides robots or autonomous vehicles, our system can also be employed in other applications, like mobile phones, drones, etc.

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发布时间:06/29/2017 00:31
更新时间:09/22/2017 23:44

This video is about the experimental results of the following paper: Xieyuanli Chen, Hui Zhang, Huimin Lu, Junhao Xiao, Qihang Qiu and Yi Li. Robust SLAM system based on monocular vision and LiDAR for robotic urban search and rescue. Proceedings of the 15th IEEE International Symposium on Safety, Security, and Rescue Robotics 2017 (SSRR 2017), Shanghai, 2017


Abstract. In this paper, we propose a monocular SLAM system for robotic urban search and rescue (USAR). Based on it, most USAR tasks (e.g. localization, mapping, exploration and object recognition) can be fulfilled by rescue robots with only a single camera. The proposed system can be a promising basis to implement fully autonomous rescue robots. However, the feature-based map built by the monocular SLAM is difficult for the operator to understand and use. We therefore combine the monocular SLAM with a 2D LiDAR SLAM to realize a 2D mapping and 6D localization SLAM system which can not only obtain a real scale of the environment and make the map more friendly to users, but also solve the problem that the robot pose cannot be tracked by the 2D LiDAR SLAM when the robot climbing stairs and ramps. We test our system using a real rescue robot in simulated disaster environments. The experimental results show that good performance can be achieved using the proposed system in the USAR. The system has also been successfully applied in the RoboCup Rescue Robot League (RRL) competitions, where our rescue robot team entered the top 5 and won the Best in Class Small Robot Mobility in 2016 RoboCup RRL Leipzig Germany, and the champions of 2016 and 2017 RoboCup China Open RRL.



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发布时间:09/14/2017 20:29
更新时间:09/14/2017 20:29
This video shows a track-wheel hybrid robot, named Kylin, which is designed to integrate the advantages of both wheeled locomotion and tracked locomotion. To save the research and development time, the robot is built upon our tracked robot named NuBot, by integrating modular components for wheeled locomotion without changing the main body of NuBot. Kylin can run 3.7 m/s on the ground, climb up 45 degree slops and 0.5 m steps. It has been employed in UGVC 2016, and won the vice-champion.
( 26.4 MB) 肖军浩, 09/14/2017 20:31
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发布时间:03/26/2017 17:49
更新时间:06/23/2017 23:36
视频中展示的是基于三维建图和虚拟现实技术的新型人机交互系统,便于审稿专家对系统进行全面了解和评价。
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