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) and Leipzig Germany (2016). We have also been participating in RoboCup China Open since it was launched in 2006.
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.
Bo Sun, Yadan Zeng, Houde Dai, Junhao Xiao, Jianwei Zhang, (2017) "A novel scan registration method based on the feature-less global descriptor – spherical entropy image", Industrial Robot: An International Journal, Vol. 44 Issue: 4, pp.552-563.
Junhao Xiao, Dan Xiong, Weijia Yao, Qinghua Yu, Huimin Lu, Zhiqiang Zheng, Building Software System and Simulation Environment for RoboCup MSL Soccer RobotsBased on ROS and Gazebo, Springer Book on Robot Operating System (ROS) –The Complete Reference (Volume 2), pp. 597-631, 2017.
Shaozun Hong, Meiping Wu, Junhao Xiao, Xiaohong Xu, Huimin Lu. Kylin: a transformable track-wheel hybrid robot. Proceedings of the 2017 International Conference on Advanced Mechatronic Systems, Xiamen, China, December 6-9, 2017.
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.
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.
Xieyuanli Chen, Huimin Lu, Junhao Xiao, Hui Zhang, PanWang. Robust relocalization based on active loop closure for real-time monocular SLAM. Proceedings of the 11th International Conference on Computer Vision Systems (ICVS), 2017.
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.
Zhiwen Zeng, Xiangke Wang, Zhiqiang Zheng, et al. Edge Agreement of Second-order Multi-agent System with Dynamic Quantization via Directed Edge Laplacian. Nonlinear Analysis: Hybrid Systems, Vol. 23, pp. 1-10, 2017.
Yuhua Zhong, Junhao Xiao, Huimin Lu, Hui Zhang. Real-Time Terrain Classification for Rescue Robot Based on Extreme Learning Machine. In: Sun F., Liu H., Hu D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2016. Communications in Computer and Information Science, vol 710, 2017. Springer, Singapore.
卢惠民，肖军浩，郑志强，ROS与中型组足球机器人，国防工业出版社，ISBN: 978-7-118-10952-8，31.7万字, 2016.10.
肖军浩，机器人操作系统浅析(译)，国防工业出版社，10万字，ISBN: 978-7-118-11056-2，18万字, 2016.09.
Lilian Zhang, Huimin Lu, Xiaoping Hu, Reinhard Koch. Vanishing Point Estimation and Line Classification in a Manhattan World with a Unifying Camera Model. International Journal of Computer Vision, Vol. 117, No. 2, pp. 111-130, 2016.
Dan Xiong, Junhao Xiao, Huimin Lu, Zhiwen Zeng, Qinghua Yu, Kaihong Huang, Xiaodong Yi, Zhiqiang Zheng.The design of an intelligent soccer-playing robot. Industrial Robot: An International Journal, Vol. 43, No.1, pp. 91-102, 2016. [PDF]
Huimin Lu, Lixing Jiang, Andreas Zell. Long Range Traversable Region Detection Based on Superpixels Clustering for Mobile Robots. Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2015), Hamburg, Germany, September 28~October 02, 2015, pp. 546-552. [PDF]
Shuai Cheng, Junhao Xiao, Huimin Lu. Real-time obstacle avoidance using subtargets and Cubic B-spline for mobile robots. Proceedings of 2014 IEEE International Conference on Information and Automation, China, 2014, pp. 634-639.[PDF]
Xun Li, Huimin Lu, Dan Xiong, Hui Zhang and Zhiqiang Zheng. A Survey on Visual Perception for RoboCup MSL Soccer Robots. International Journal of Advanced Robotic Systems, Vol.10, 110:2013, pp.1-10, 2013. [PDF]
Huimin Lu, Xun Li, Hui Zhang, and Zhiqiang Zheng. Robust Place Recognition Based on Omnidirectional Vision and Real-time Local Visual Features for Mobile Robots. Advanced Robotics, Vol.27, No.18, pp.1439-1453, 2013. [PDF]
Huimin Lu, Xun Li, Hui Zhang, Mei Hu and Zhiqiang Zheng. Robust and Real-time Self-Localization Based on Omnidirectional Vision for Soccer Robots. Advanced Robotics, Vol.27, No.10, pp.799-811, 2013. [PDF]
Zhiwen Zeng, Huimin Lu, Zhiqiang Zheng. High-speed Trajectory Tracking Based on Model Predictive Control for Omni-directional Mobile Robots. Proceedings of the 2013 25th Chinese Control and Decision Conference (CCDC), Guiyang, China, May 25-27, 2013, pp. 3179-3184. [PDF]
Hui Zhang, Huimin Lu, Peng Dong, Dan Xiong, and Zhiqiang Zheng. A Novel Generic Ball Recognition Algorithm Based on Omnidirectional Vision for Soccer Robots. International Journal of Advanced Robotic Systems, Vol. 10, 388:2013, pp. 1-12, 2013. [PDF]
YU Qinghua, HUANG Kaihong, Huimin Lu, GUO Hongwu. Object Motion Estimation and Interception Based on Stereo Vision for Soccer Robots in 3D Space. Proceedings of the 32nd Chinese Control Conference, Xi'an, China, July 26-28, 2013, pp. 5943-5948. [PDF]
Dan Xiong, Huimin Lu, Zhiwen Zeng, Zhiqiang Zheng. Topological Localization Based on Key-frames Selection and Vocabulary Tree for Mobile Robots. Proceeding of the IEEE International Conference on Robotics and Biomimetics (ROBIO), Shenzhen, China, December 2013, pp. 2505-2510.
Dan Xiong, Huimin Lu, Zhiqiang Zheng. A self-localization method based on omnidirectional vision and MTi for soccer robots. Proceedings of the 10th World Congress on Intelligent Control and Automation, Beijing, China, 2012, pp. 3731-3736. [PDF]
Huimin Lu, Hui Zhang, Zhiqiang Zheng. A Novel Real-Time Local Visual Feature for Omnidirectional Vision Based on FAST and LBP. RoboCup 2010: Robot Soccer World Cup XIV, LNAI 6556, Springer, pp. 291-302, 2011. [PDF]
Huimin Lu, Hui Zhang, Shaowu Yang, Zhiqiang Zheng. Camera Parameters Auto-Adjusting Technique for Robust Robot Vision. Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA 2010), Anchorage, Alaska, USA, May 5~8, 2010, pp. 1518-1523. [PDF]
Xiangke Wang, Hui Zhang, Huimin Lu, Zhiqiang Zheng. A New Triple-based Multi-robot System Architecture and Application in Soccer Robots. ICIRA 2010, Part II, LNAI 6425, Springer, pp. 105-115, 2010. [PDF]
Huimin Lu, Hui Zhang, Shaowu Yang, Zhiqiang Zheng. A Novel Camera Parameters Auto-Adjusting Method Based on Image Entropy. RoboCup 2009: Robot Soccer World Cup XIII, LNAI 5949, Springer, pp. 192-203, 2010. [PDF]
Huimin Lu, Hui Zhang, Shaowu Yang, Zhiqiang Zheng. Vision-based Ball Recognition for Soccer Robots without Color Classification. Proceedings of 2009 IEEE International Conference on Information and Automation, Zhuhai/Macau,China, 2009, pp. 916-921. [PDF]
卢惠民, 张辉, 郑志强. 基于视觉的移动机器人自定位问题. 中南大学学报（自然科学版）, Vol.40, Suppl.1, pp. 127-134, 2009.
Huimin Lu, Hui Zhang, Junhao Xiao, Fei Liu, Zhiqiang Zheng. Arbitrary Ball Recognition Based on Omni-directional Vision for Soccer Robots. RoboCup 2008: Robot Soccer World Cup XII, LNAI 5399, Springer, pp. 133-144, 2009.[PDF]
Huimin Lu, Zhiqiang Zheng, Fei Liu, Xiangke Wang. A Robust Object Recognition Method for Soccer Robots.Proceedings of 7th World Congress on Intelligent Control and Automation,Chongqing,China, 2008, pp. 1645-1650. [PDF]
刘斐, 卢惠民, 郑志强. 基于线性分类器的混合空间查找表颜色分类方法. 中国图象图形学报, Vol.13, No.1, pp. 104-108, 2008.
柳林, 刘斐, 季秀才, 卢惠民, 海丹, 郑志强. 全向移动机器人编队分布式控制研究. 机器人, Vol. 29, No.1, pp. 23-28, 2007.
Fei Liu, Huimin Lu, Zhiqiang Zheng. A Robust Approach of Field Features Extraction for Robot Soccer. Proceedings of 4th IEEE LARS 07/COMRob 07, ROBOTIC FORUM Monterrey 2007, November 05-09, 2007.
卢惠民, 刘斐, 郑志强. 一种新的用于足球机器人的全向视觉系统. 中国图象图形学报, Vol.12, No.7, pp. 1243-1248, 2007.
Fei Liu, Huimin Lu, Zhiqiang Zheng. A Modified Color Look-Up Table Segmentation Method for Robot Soccer. Proceedings of 4th IEEE LARS 07/COMRob 07, ROBOTIC FORUM Monterrey 2007, November 05-09, 2007.
卢惠民, 王祥科, 刘斐, 季秀才, 郑志强. 基于全向视觉和前向视觉的足球机器人目标识别. 中国图象图形学报, Vol.11, No.11, pp. 1686-1689, 2006.
Xiucai Ji, Lin Liu and Zhiqiang Zheng. A modular hierarchical architecture for autonomous robots based on task-driven behaviors. International Conference on Sensing, Computing and Automation, Chongqing, China, May 8-11, 2006: 631~636.
柳林, 季秀才, 郑志强. 基于市场法及能力分类的多机器人任务分配研究. 机器人, 2006, 28(3): 337~343.
LIU Lin and ZHENG Zhiqiang. Combinatorial bids based multi-robot task allocation method. Proceedings of the 2005 IEEE International Conference on Robotics and Automation（ICRA2005）, 2005: 1157~1162.
LIU Lin and ZHENG Zhiqiang. A novel multi-robot coordination method using capability category. Proceedings of the 16th IFAC World Congress, 2005.
LIU Lin, WANG Lei, ZHENG Zhiqiang, SUN Zengqi. A learning market based layered multi-robot architecture. Proceedings of the 2004 IEEE International Conference on Robotics and Automation（ICRA2004）, 2004: 3417~3422.
柳林, 郑志强. 多机器人任务分配及其在机器人足球中的应用. 控制理论与应用, 2004, 21(Suppl.): 46~50.
This video is about the experimental results of the following paper: Yi Li, Chenggang Xie, Huimin Lu, Xieyuanli Chen, Junhao Xiao and Hui Zhang. Scale-aware Monocular SLAM Based on Convolutional Neural Network. Proceedings of the 15th IEEE International Conference on Information and Automation 2018 ( ICIA 2018 ), Mount Wuyi, 2018.
Abstract—Remarkable performance has been achieved using the state-of-the-art monocular Simultaneous Localization and Mapping (SLAM) algorithms. However, due to the scale ambiguity limitation of monocular vision, the existing monocular SLAM systems can not directly restore the absolute scale in unknown environments. Given the amazing results in the field of depth estimation from Convolutional Neural Networks (CNNs), we propose a CNN-based monocular SLAM, where we naturally combine the CNN-predicted depth maps together with the monocular ORB-SLAM, overcoming the scale ambiguity limitation of the monocular SLAM. We test our method using the popular KITTI odometry benchmark, and the experimental results show that the overall performance of average translational and rotational error can reach 2.00% and 0.0051º/m. In addition, our approach can work well under the pure rotation motion, which shows the robustness and high accuracy of the proposed algorithm.
Abstract— Most robots in urban search and rescue (USAR) fulfill tasks teleoperated by human operators. The operator has to know the location of the robot and find the position of the target (victim). This paper presents an augmented reality system using a Kinect sensor on a customly designed rescue robot. Firstly, Simultaneous Localization and Mapping (SLAM) using RGB-D cameras is running to get the position and posture of the robot. Secondly, a deep learning method is adopted to obtain the location of the target. Finally, we place an AR marker of the target in the global coordinate and display it on the operator's screen to indicate the target even when the target is out of the camera’s field of view. The experimental results show that the proposed system can be applied to help humans interact with robots.
This video is the accompanying video of the paper: Junchong Ma, Weijia Yao, Wei Dai, Huimin Lu, Junhao Xiao, Zhiqiang Zheng. Cooperative Encirclement Control for a Group of Targets by Decentralized Robots with Collision Avoidance. Proceedings of the 37th Chinese Control Conference, 2018.
Abstract: This study focuses on multi-target capture and encirclement control problem for multiple mobile robots. With the distributed architecture, this problem involves a group of robots to encircle several moving targets in a coordinated circle formation. In order to efficiently allocate the targets to robots, a Hybrid Dynamic Task Allocation (HDTA) algorithm was proposed, in which a temporary "manager" robot was assigned to negotiate with other robots. For encirclement formation, a robust control law was introduced for any number of mobile robots to form a specific circle formation with arbitrary inter-robot angular spacing. In view of safety, an online collision avoidance algorithm combining the sub-targets and Artificial Potential Fields (APF) approaches was proposed, which ensures that the paths of robots are collision-free. To prove the validity and robustness of the proposed scheme, both theoretical analysis and simulation experiments were conducted.
The team description paper can be downloaded from here, with the main contribution of a newly designed three-wheel robot.
 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]
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
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
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
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.
Founder and director
Prof. Dr. Zhiqiang Zheng
Prof. Dr. Hui Zhang
Associate Prof. Dr. Huimin Lu
Dr. Junhao Xiao
Sha Luo (female)
Ruoyi Yan (female)
Bingxin Han (female)
Dr. Lin Liu
Dr. Fei Liu
Dr. Xiucai Ji
Associate Prof. Dr. Wenjie Shu
Dr. Dan Hai
Associate Prof. Dr. Xiangke Wang
Dr. Shaowu Yang
Dr. Lina Geng (female)
Dr. Shuai Tang
Dr. Zhiwen Zeng
Dr. Xiabin Dong
Dr. Dan Xiong
Mrs. Wei Liu
Mr. Yupeng Liu
Mr. Dachuan Wang
Mr. Baifeng Yu
Mr. Fangyi Sun
Mr. Lianhu Cui
Mr. Shengcai Lu
Mr. Peng Dong
Mr. Yubo Li
Mr. Xiaozhou Zhu
Mr. Qingzhu Cui
Mr. Xingrui Yang
Mr. Kaihong Huang
Mr. Shuai Cheng
Mr. Xiaoxiang Zheng
Mr. Yunlei Chen
Mr. Xianglin Yang
Mr. Yu Zhang
Mrs. Yaoyao Lan
Mr. Yuxi Huang
Mr. Yi Liu
Mr. Yuhua Zhong
Mr. Qiu Cheng
Mr. Peng Chen
Mrs. Minjun Xiong
Mr. Pan Wang
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.
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.
Real-time Terrain Classification for Rescue Robot Based on Extreme Learning Machine
Yuhua Zhong, Junhao Xiao, Huimin Lu and Hui Zhang
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.
The video can be found here if the below link does not work.