Edge-Cloud Collaborative Motion Planning for Autonomous Navigation with Large Language Models

Description

Join our cutting-edge research to develop efficient edge-cloud collaborative motion planning systems for autonomous navigation. This project focuses on leveraging large language models (LLMs) to enhance perception, prediction, and planning in dynamic and complex environments. The primary goal is to address challenges such as long latencies and adaptive decision-making in real-time for autonomous navigation systems, particularly in agriculture and other interdisciplinary fields.

PhD Candidate Responsibilities
•    Conduct research to design and implement efficient edge-cloud frameworks for motion planning.
•    Develop algorithms to integrate LLMs with real-time autonomous navigation systems.
•    Perform experiments to validate the system in dynamic, real-world environments.
•    Write technical reports, academic papers, and present findings at international conferences.
•    Complete PhD thesis on time.

Requirements
•    A minimum of 2:1 Bachelor’s or Master’s degree in Electrical Engineering, Computer Science, Robotics, or a related field.
•    Proficiency in Python and/or C++ and experience with Robot Operating System (ROS). Strong motivation, problem-solving skills, and the ability to work both independently and collaboratively.

Key Opportunities and Benefits
•    Access to state-of-the-art facilities for robotics and motion planning research.
•    Opportunities for interdisciplinary collaboration and industry partnerships, especially in the field of agriculture.
•    Funding for international conferences and extensive training programs.
•    This project is a funded Studentship for 3.5 years in total and will provide UK tuition fees (£4,786*) and maintenance at the UKRI Doctoral Stipend rate (£19,237* per annum - *2024/25 rates).