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Hanlin Niu

Hanlin Niu

Dr

Personal profile

Overview

Hanlin is a Postdoctoral Research Associate at the University of Manchester, being involved in Project Digital twin-based Bilateral Teleautonomous System for Nuclear Remote Operation, funded by EPSRC and  Project Robotics and AI in Nuclear (RAIN), funded by EPSRC.

Before joining the University of Manchester, Dr Hanlin Niu worked as a Postdoc in Cardiff University working on robotic system and computer vision. He obtained bachelor degree from Tianjin University and obtained PhD degree in Autonomous and Intelligent System Group of Cranfield University (UK). He has worked on guidance, navigation and control of Unmanned Aerial Vehicles and Unmanned Surface Vehicles for more than 5 years. His research area covers collision avoidance algorithm, path following algorithm, path planning algorithm, decision making, deep learning, computer vision and system formal verification.

Hanlin has a strong project experience and he has participated in several projects during his PhD, which includes two industrial projects and funded by Innovative UK, National Oceanography Centre (NOC), Technology Strategy Board (TSB) and the Defence, Science, Technology Laboratories (DSTL). Hanlin was also the first president of Unmanned and Intelligent Systems Society of Cranfield University and he proposed, developed, and demonstrated 5 drone projects. In part time, Hanlin also developed and demonstrated multi-drone platform independently.

Hanlin has a strong industrial experience and he has a strong programming skill in Matlab, Simulink, C++ and Python. He also has extended experience in embedded systems, robotics and systems integration. Hanlin also developed and implemented collision avoidance algorithm and precise path following algorithm on C-Enduro USV and C-worker USV for ASV Global, Ltd (Portsmouth, UK). Hanlin also developed randomly pick and place solution using Kuka iiwa robot arm in Astute Project of Cardiff University. 

Keywords

  • Path Planning
  • Collision Avoidance
  • Deep Reinforcement Learning
  • Formal Verification
  • Deep Learning

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being
  2. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy
  3. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure
  4. SDG 12 - Responsible Consumption and Production
    SDG 12 Responsible Consumption and Production

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Collaborations and top research areas from the last five years

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  • 8 Article
  • 4 Conference contribution
  • 1 Review article
View all 13 research outputs