Alison Turner
Papers
7
Total Citations
59
H-Index
4
About
Alison Turner is a leading researcher in reconfigurable and intelligent manufacturing systems, with a focus on digital twin technology, multi-agent learning, and human–robot collaboration. Her most-cited work, “Multi-agent cooperative swarm learning for dynamic layout optimisation of reconfigurable robotic assembly cells based on digital twin” (2024, 18 citations), pioneers a swarm-based approach to autonomously optimise factory layouts in response to shifting production demands. Turner’s 2022 paper on “Imitation learning for coordinated human–robot collaboration based on hidden state-space models” (16 citations) advances the field by enabling robots to learn complex assembly tasks from human demonstration, improving adaptability in flexible manufacturing. Her contributions to the UK’s Evolvable Assembly Systems project (2019, 9 citations) helped define the vision for transformable manufacturing, demonstrating how modular, self-reconfiguring cells can handle product variety and short production cycles. Turner has also developed probabilistic frameworks for movement primitives (2021, 6 citations) that allow robots to generalise skills across varied assembly tasks. With over 60 total citations and a consistent focus on repeatable, rapid auto-reconfiguration, her work is shaping the next generation of smart, adaptive factories that respond dynamically to market changes.
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