Prof Xiu Yan

Design, Manufacture and Engineering Management


Autonomous navigation with ROS for a mobile robot in agricultural fields
Post Mark A., Bianco Alessandro, Yan Xiu T.
14th International Conference on Informatics in Control, Automation and Robotics (ICINCO), (2017)
In-process monitoring and quality control of hot forging processes towards Industry 4.0
Onyeiwu Chimaeze , Yang Erfu, Rodden Tony, Yan Xiu-Tian, Zante Remi C, Ion William
Industrial Systems in the Digital Age Conference 2017, pp. 1, (2017)
Robots in industry : a shift towards autonomous and intelligent systems in the digital age
Wong Cuebong, Yang Erfu, Yan Xiutian, Gu Dongbing
Industrial Systems in the Digital Age Conference 2017, pp. 1, (2017)
An overview of robotics and autonomous systems for harsh environments
Wong Cuebong, Yang Erfu, Yan Xiu, Gu Dongbing
The 23rd International Conference on Automation and Computing (ICAC'17), (2017)
Adaptive and intelligent navigation of autonomous planetary rovers – a survey
Wong Cuebong, Yang Erfu, Yan Xiu-Tian, Gu Dongbing
The 11th NASA/ESA Conference on Adaptive Hardware and Systems, (2017)
Study on Mode I fatigue behaviour of Nylon 6,6 nanoreinforced CFRP laminates
Brugo T., Minak G., Zucchelli A., Yan X.T., Belcari J., Saghafi H., Palazzetti R.
Composite Structures Vol 164, pp. 51-57, (2017)

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Professional activities

UK-China Partnership Workshop on Collaborative RASMA research and its applications in Manufacturing and Agricultural PROGRAMME
Prof. Guoyuan Zhang
UK-China Workshop on Agriculture Robotics and Space Robotics Application in Agriculture
Space Mechatronic Technologies for Agriculture Applications
Agribot – Space Mechatronic Technologies for Agriculture Applications
Hosted a visit from Mr. Ying Lu of China Academy of Launch Vehicle Technology

more professional activities


Novel strategies for robotic control through the study of dynamic interactions between robots and their environments
Yang, Erfu (Principal Investigator) Yan, Xiu (Co-investigator) Wong, Cuebong (Post Grad Student)
The advances of mechatronics, control and communication technologies have significantly increased the applications of different robotic systems in extreme or harsh environments. These environments can often arise from many industry sectors ranging from manufacturing to operations, such as nuclear, oil and gas, and aerospace, etc. One particular promising area is the on-orbital satellite refuelling application in harsh space environment. It is a challenging task how to deal with the dynamic interactions between robots and space environment. During these interactions the unpredicted forces could be generated due to the uncertainty of space environment and also the impact effects following the capturing action exerting on the satellite to be refuelled. Therefore there needs a pressing innovative solution to the control of dynamic interactions between the robotic system and its environment. There are also many other interesting applications in the areas of smart manufacturing and smart systems where harsh environments have to be dealt properly with the robotic systems. The specific aim of this PhD project is to develop novel strategies for adaptive, intelligent and robust control of autonomous robots through the study of dynamic interactions between robots and their harsh environments. Through these advanced control strategies, it will solve the challenging tasks on how to efficiently deal with the dynamic interactions between autonomous robots and their environments. The novelty of the advanced control is how to make the robots in harsh environments behave adaptively and robustly by using the information from on-board robotic vision system and multiple sensory devices equipped for intelligent task planning and object manipulation. Another novelty of the advanced control strategies lies in that they will be also developed in this project together with other sensor-based control techniques, such as hybrid force-position control. The innovative technology proposed in this project incorporates and integrates both tactile sensing and vision based sensing in object recognition and handlibility analysis, pose estimation, etc. For the on-board robotic vision system, the novelty is to investigate the use of multiple vision sensors, e.g. 2 camera systems coupled with a laser scanning system. The key success factors include the identification of on-board vision sensing systems, development of software systems for 3D object recognition and pose estimation as well as the integration of the sensing system with the control and communication system.
Period 01-Oct-2016 - 31-Mar-2020
Doctoral Training Partnership (DTP 2016-2017 University of Strathclyde) | Wong, Cuebong
Yang, Erfu (Principal Investigator) Yan, Xiu (Co-investigator) Wong, Cuebong (Research Co-investigator)
Period 01-Oct-2016 - 01-Apr-2020
UK-China Space Mechatronic Systems Technology Laboratory Research Programme | McMaster, Thomas
Yan, Xiu (Principal Investigator) Post, Mark (Co-investigator) McMaster, Thomas (Research Co-investigator)
Period 01-Oct-2014 - 01-Oct-2018
An EPSRC Life Sciences Interface Doctoral Training Centre for Medical Devices | Went, April
Grant, Mary (Principal Investigator) Yan, Xiu (Co-investigator) Went, April (Research Co-investigator)
Period 01-Oct-2009 - 30-Sep-2014
Investigation of process information required to create an autonomous forging system – AIMHI0
Yan, Xiu (Principal Investigator) Yang, Erfu (Co-investigator)
The overall aim is to develop a holistic understanding of the requirements for autonomous forging process by investigate the following objectives; Capability finding of current forging machine and other equipment used at AFRC, including sensing capability, forging process and machine capability; Develop a small case study scenario to describe the forging tasks into 3 levels: goal, object and manufacturing equipment level. This can be used to explain to the interview panels what AIMHi can do for forging process; Develop simple decision model to support best use of information acquired; Investigate the causes of variations in the forged part by developing a overall strategy. Develop an autonomous forging process and its support system; Online Decision Making for Product Quality Control Currently, the decision-making is made offline and the product quality is also normally inspected after the parts are finished. It means that the product quality is not controlled online as there is no in-process ability to make any decision to change or optimise the process parameters in real time. This task is to investigate how the process data could be exploited to make online decision for the product quality control of the hot forging process available in the AFRC.
Period 01-Oct-2015 - 31-Mar-2016
INFUSE Infusing Data Fusion in Space Robotics (H2020 COMPET-4 OG3)
Post, Mark (Principal Investigator) Vasile, Massimiliano (Co-investigator) Yan, Xiu (Co-investigator)
Period 01-Nov-2016 - 31-Jan-2019

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Design, Manufacture and Engineering Management
James Weir Building

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