Simplified integration of external sensors in industrial robot programs

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The integration of external sensors, such as cameras and force/torque sensors, into industrial robots is crucial for expanding their applicability across various businesses. These sensors allow robots to adapt to changes in their environment, yet their integration poses challenges. Firstly, incorporating sensor data into the robot's programming is complex, requiring accurate processing and interpretation of the information. Secondly, managing sensor data during task execution complicates operations and can extend execution time, leading to their limited use in industrial settings. Our work focuses on simplifying sensor integration for industrial handling tasks, targeting individuals who may not be experts in robot programming or sensor data processing. We explore optimizing automated sensor data processing to minimize execution time. Initially, we classify sensor signals into geometric properties and object state information. This classification informs a programming paradigm that decouples sensor data processing from the robot's movement instructions, linking it instead to a positional database. We address two workspace changes that require multiple motions: search motions, which can be expedited using probability-based search maps, and object drift, which can be compensated by analyzing historical position data. These enhancements are integrated into our programming paradigm, streamlining sensor integration. We evaluate th