About this position
The growing adoption of autonomous mobile robots (AMRs) and automated guided vehicles (AGVs) is transforming modern manufacturing, warehousing, logistics, and smart factory environments. Accurate indoor positioning remains a critical challenge for these systems, as traditional satellite-based navigation technologies such as GPS are ineffective in indoor industrial settings. Ultra-Wideband (UWB) technology has emerged as a promising solution due to its high positioning accuracy, low latency, and robustness in complex environments.
This PhD project aims to develop intelligent UWB-based positioning and navigation systems for industrial robotics using Artificial Intelligence (AI) and advanced sensor fusion techniques. The research will investigate how UWB measurements can be combined with data from onboard sensors such as inertial measurement units (IMUs), cameras, and LiDAR to improve localization accuracy, reliability, and real-time navigation performance in dynamic industrial environments.
The project will focus on the design of AI-enhanced localization algorithms, intelligent path planning techniques, and adaptive navigation strategies capable of operating in environments with obstacles, moving personnel, and changing factory layouts. Digital Twin technologies may also be explored to create virtual representations of industrial environments for system testing, optimisation, and performance evaluation.
The research aligns with Industry 5.0 priorities, supporting the development of intelligent, connected, and human-centric manufacturing systems. Potential application areas include smart factories, warehouse automation, logistics, healthcare facilities, and collaborative robotics.
The successful candidate will gain expertise in UWB systems, robotics, Artificial Intelligence, Industrial IoT, sensor fusion, and autonomous navigation. The project is expected to generate multiple high-quality journal and conference publications and provide opportunities for collaboration with industrial partners and international research networks.
Self Funded (Scholarship not available. Fees & Materials to be paid by the student.)
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