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Technology at the University of Nottingham. This project is in relation to the technical needs of Rolls-Royce to develop smart and robotic solutions to enable in-situ/on-wing repair and maintenance of gas
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. At the Rolls-Royce UTC, we have developed advanced robotic systems capable of navigating cramped and hazardous environments, performing inspection tasks, and carrying out active operations such as machining
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on What You See: Semantically Safe Robot Interaction (ID: TUEILSY-PHD20240930-SSR) Distributed Multiagent Planning and Control for Large Aerial Swarms in Changing Environments (ID: TUEILSY-PHD20240930-SAS
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Manufacturing and/or AI Robotics. Focus areas include: yield/defect causal analysis and XAI; process-variation monitoring and root-cause analysis; intelligent scheduling/dispatching for WIP/throughput (multi
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vehicles (UUVs) into a unified, collaborative robotics system designed for inspection and surveillance tasks. These vehicles will operate in challenging marine environments to perform monitoring of cables
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aerial vehicles (UAVs), unmanned surface vehicles (USVs), and unmanned underwater vehicles (UUVs) into a unified, collaborative robotics system designed for inspection and surveillance tasks
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from YouTube. Accept cookie and refresh page to watch video, or click here to open video) About the position Foundation models represent one of the most promising avenues pushing the frontier of robot
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projects in the Centre for AI and Robotics Research. Funded PhD projects Adaptive Systems Research Group Artificial Intelligence in Games Continual and Open-ended Reinforcement Learning Information and the
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flow reconstruction, enabling both real-time coarse diagnostics and high-fidelity offline velocity field estimation. Developing reinforcement learning (RL) algorithms for a multi-agent robotics system
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range of movement as well as performing the “sim2real” step for the control of physical musculoskeletal robots in the real world, i.e., use cass include neuromuscular model-based control of robotic legs