Papers
3
Total Citations
73
H-Index
3
About
Fares Innal is a leading researcher in the safety and risk analysis of autonomous multi-robot systems, with a particular focus on their operation in hazardous and dynamic environments such as industrial plants and chemical laboratories. His work centers on developing robust methodologies to model and mitigate collision hazards and control architecture risks. Innal’s most influential contribution is the integration of Systems-Theoretic Process Analysis (STPA) with Stochastic Petri Nets (SPN) and Bowtie analysis, enabling a comprehensive approach to hazard modeling that accounts for complex robot-robot and human-robot interactions. His 2023 paper on collision hazard modeling has garnered 34 citations, while his 2020 study comparing centralized and hierarchical control architectures using STPA and Bowtie has received 33 citations. Innal has also advanced the field by applying the Analytic Hierarchy Process (AHP) to evaluate distributed versus hybrid control architectures, as demonstrated in his 2021 work. Through these achievements, Innal has established himself as a key figure in enhancing the safety and reliability of autonomous mobile robot fleets, providing critical frameworks for their deployment in high-risk settings.
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