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C h a p t e r 3 | S e n s i n g a n d Pe r c e p t i o n Sensor Intelligence Explained Finite State Machine (FSM) FSMs provide simple rule-based logic, allowing sensors to trigger actions when conditions exceed a threshold (e.g., a vibration sensor detecting excessive movement). Because of this simplicity, FSMs are highly efficient and well suited to applications that require deterministic behavior. Machine Learning Core (MLC) MLCs increase the sophistication of sensor outputs, typically using decision trees trained to recognize specific patterns or behaviors. Unlike an FSM, a decision tree can offer more nuanced output, as it can compare several criteria before providing a "pass/fail" output. For example, the acceleration characteristics of a robot while running are different from those experienced while walking. An MLC will consider the actions of the robot before deciding whether a dangerous condition has emerged. Intelligent Sensor Processing Unit (ISPU) ISPUs extend capabilities further by embedding a programmable digital signal processor inside the sensor. This enables the sensor itself to perform customized logic, advanced filtering, and even complex signal analysis. Engineers can program ISPUs to meet specific needs, giving them maximum flexibility. An escalating scale, from simple triggers to complex logic, allows smart sensing to follow a staged decision-making process. Picture a traditional Russian nesting doll, where each increase of sensing sophistication corresponds to another layer of the doll. Sensors are crucial for achieving autonomy, especially in industrial settings. Autonomy without environmental awareness is unattainable. Robots need to know their surroundings, their actions, and their positions. Without this information, true autonomy is impossible." Arnaud Deleule Director of Application, STMicroelectronics 22 Autonomy Meets Intelligence: Enabling the Future of Factory Automation