Papers
8
Total Citations
53
H-Index
5
About
Joel Greenyer is a leading researcher in the formal synthesis and optimization of multi-robot systems, with a particular focus on automotive production cells. His work bridges the gap between high-level formal specifications and executable controller code, enabling the automation of complex, safety-critical robot choreography. Greenyer’s major contributions include pioneering the use of GR(1) reactive synthesis to automatically generate correct-by-construction controllers from scenario-based specifications, as demonstrated in his most-cited 2018 paper (17 citations). He has further advanced this field by integrating Monte Carlo Tree Search with synthesis for task planning (8 citations) and by developing distributed execution frameworks for IoT-connected robots (7 citations). His research also addresses practical industrial challenges, such as optimizing cycle time and energy efficiency through braking energy recuperation. Greenyer’s work is notable for its direct applicability: he has created tools that automate multi-robot choreography planning and execution, reducing manual, error-prone design processes. With a cumulative citation count exceeding 50 across his top papers, Greenyer’s research is highly influential in the domains of cyber-physical systems, formal methods, and industrial robotics.
Research Focus
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Top Papers
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