Activity
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We are super excited to be at #Berryquest25 this week! 🎉 Come and visit us at our joint stand with the fantastic team from Nanobubble Agritech…
We are super excited to be at #Berryquest25 this week! 🎉 Come and visit us at our joint stand with the fantastic team from Nanobubble Agritech…
Liked by Annie Wang
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Supra Chiluka from #BitwiseAg in #Morocco today meeting with the forecasting team from AFRICAN BLUE and Costa. We love getting the opportunity to…
Supra Chiluka from #BitwiseAg in #Morocco today meeting with the forecasting team from AFRICAN BLUE and Costa. We love getting the opportunity to…
Liked by Annie Wang
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We love seeing #BitwiseAg CEO Fiona Turner sharing her experiences of being a #StartupFounder #FemaleFounder through Enterprize Tasmania💡✨
We love seeing #BitwiseAg CEO Fiona Turner sharing her experiences of being a #StartupFounder #FemaleFounder through Enterprize Tasmania💡✨
Liked by Annie Wang
Experience
Education
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UNSW
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Some key work has been published here: https://www.sciencedirect.com/science/article/abs/pii/S0168169921001411
https://www.sciencedirect.com/science/article/abs/pii/S0168169920307006
Other related videos and material for Automatic Apple Flower Monitoring System:
http://www.robotics.unsw.edu.au/srv/project/apple-sensing.html -
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Thesis with Associate Professor Jay Katupitiya
Model Predictive Control for Unmanned Agricultural Ground Vehicle (UAGV) Guidance
● Ground vehicle modelling using C++ and Matlab.
● Proposed a novel model predictive control method for guidance control of three different kinds of ground vehicles (tractor, tractor-trailer, and tracked vehicle-trailer) in the presence of slip.
● Implement the proposed controller in both kinematic simulation and dynamic simulation using C++ and Matlab;…Thesis with Associate Professor Jay Katupitiya
Model Predictive Control for Unmanned Agricultural Ground Vehicle (UAGV) Guidance
● Ground vehicle modelling using C++ and Matlab.
● Proposed a novel model predictive control method for guidance control of three different kinds of ground vehicles (tractor, tractor-trailer, and tracked vehicle-trailer) in the presence of slip.
● Implement the proposed controller in both kinematic simulation and dynamic simulation using C++ and Matlab; by simply changing some parameters, the proposed method is really easy to adapt to control different vehicles.
● Test the proposed controller in an autonomous tractor in the field at Elizabeth Macarthur Agricultural Institute, Menangle, NSW.
● Generate peer reviewed international publications. -
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Activities and Societies: School Photograph Contest (1st prize); Liaoning Province Mathematical modelling Contest; Performer in End of Year Celebration;
Thesis: Design of truck tyre changer
Licenses & Certifications
Publications
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Data-centric analysis of on-tree fruit detection: Experiments with deep learning
Computers and Electronics in Agriculture
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DeepPhenology: Estimation of apple flower phenology distributions based on deep learning
Computers and Electronics in Agriculture
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Side-view apple flower mapping using edge-based fully convolutional networks for variable rate chemical thinning
Computers and Electronics in Agriculture
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Robust model predictive control for path tracking of a tracked vehicle with a steerable trailer in the presence of slip
5th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture AGRICONTROL 2016 — Seattle, WA, USA
In comparison to general autonomous robots, high precision guidance of farm vehicles becomes more complex and challenging as these vehicles are inevitably subjected to significant slip caused by very rough and unsteady terrain which is sometimes undulating. Therefore, it is essential to design controllers that have the capability to react to significant and uncertain disturbances. This paper presents a robust model predictive controller for path tracking of a tracked vehicle towing a steerable…
In comparison to general autonomous robots, high precision guidance of farm vehicles becomes more complex and challenging as these vehicles are inevitably subjected to significant slip caused by very rough and unsteady terrain which is sometimes undulating. Therefore, it is essential to design controllers that have the capability to react to significant and uncertain disturbances. This paper presents a robust model predictive controller for path tracking of a tracked vehicle towing a steerable trailer in the presence of unknown but bounded disturbances. Based on kinematics and a virtual spring, a virtual error vector model with slip is used to model path offset and orientation offset for the tracked vehicle and the trailer. An adaptive min-max model predictive control method is proposed to guarantee robustness and accuracy in the path tracking. Finally, the proposed controller is compared with a min-max model predictive controller and a sliding mode controller in a realistic dynamic simulation platform. The results prove that the proposed adaptive min-max model predictive controller provides the required accuracy and robustness in the presence of slip.
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A sliding mode controller with a nonlinear disturbance observer for a farm vehicle operating in the presence of wheel slip(Link)
Autonomous Robots/Springer
A sliding mode controller with a nonlinear disturbance observer is proposed and developed to control a farm vehicle to accurately track a specified path. The vehicle is subjected to lateral and longitudinal slips at front and rear wheels. The unpredictability of ground contact forces which occur at the wheels while traversing undulating, rough and sloping terrains require the controllers to be sufficiently robust to ensure stability. The work presented in this paper is directed at the…
A sliding mode controller with a nonlinear disturbance observer is proposed and developed to control a farm vehicle to accurately track a specified path. The vehicle is subjected to lateral and longitudinal slips at front and rear wheels. The unpredictability of ground contact forces which occur at the wheels while traversing undulating, rough and sloping terrains require the controllers to be sufficiently robust to ensure stability. The work presented in this paper is directed at the practicality of its application with both matched and unmatched uncertainties considered in the controller design. The controller is designed using an offset model derived from the kinematic model and its operation is verified by simulation and field experiments. In the simulations, the kinematic model based controller is used to control both a kinematic model and a dynamic model of a tractor to verify the performance of the kinematic model based controller. The proposed controller is compared with two other nonlinear controllers, namely, back stepping control and model predictive control. In the field experiments, the three controller were used to control the physical tractor to follow a specified path. Simulation and experimental results are presented to show that the proposed controller demonstrated the required robustness and accuracy at all times.
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Honors & Awards
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Excellent Graduate Awards
Northeastern University
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The City of Shenyang Excellent Undergraduate Student
The City of Shenyang Educational and Scientific Committee
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National Scholarship
Ministry of Education of the People's Republic of China
The top-level scholarship set up by central government
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Student Leadership Awards
Northeastern University
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STX Named Scholarship
STX (Dalian) Shipbuilding Co., Ltd.
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CAI Guan-Shen Named Scholarship
Sunwah Group
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Excellent Undergraduate Student Awards
Northeastern University
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Excellent Undergraduate Student Scholarship, 1st Class (2009-2010, 3 times)
Northeastern University
More activity by Annie
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Tassie #agtech friends - join us for the next Innovate Ag Tas MeetUp - the bright & shiny future of agtech! Hear from #BitwiseAg CEO Fiona Turner…
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What lecturers get up to when they are not teaching and researching :D
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What a year! I am looking forward to continuing to grow in 2025!
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The #BitwiseAg team is excited to be attending FRUIT LOGISTICA in Berlin, 5-7 Feb! Is 2025 your year to improve forecasting, reduce operating costs…
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Thanks for share my journey Berry Insights® . Bitwise Agronomy has grown a lot over the last 5 years! I will be Fruit Attraction in Madrid next…
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#BitwiseAg often works fully remotely with our customers. Which is why CEO Fiona Turner was excited to able to meet Salih Hodzhov and the WB Chambers…
#BitwiseAg often works fully remotely with our customers. Which is why CEO Fiona Turner was excited to able to meet Salih Hodzhov and the WB Chambers…
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