Abstract
In the factories and distribution centers of the future, humans and robots shall work together in close proximity and even physically interact. This shift to joint human–robot teams raises the question of how to ensure worker safety. In this manuscript, we present a novel episodic memory system for safety-aware robots. Using this system, the robots can answer questions about their actions at the level of safety concepts. We built this system as an extension of the KnowRob framework and its notion of episodic memories. We evaluated the system in a safe physical human–robot interaction (pHRI) experiment, in which a robot had to sort surgical instruments while also ensuring the safety of its human co-workers. Our experimental results show the efficacy of the system to act as a robot’s belief state for online reasoning, as well as its ability to support offline safety analysis through its fast and flexible query interface. To this end, we demonstrate the system’s ability to reconstruct its geometric environment, course of action, and motion parameters from descriptions of safety-relevant events. We also show-case the system’s capability to conduct statistical analysis.
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Notes
The memory system that we present in this system does not process natural language questions. Instead, it processes logic queries. We use natural language questions as an illustration of the expressivity of the query language.
The wall-mounted RGB-D sensor is not a safety-rate sensor. It was used to demonstrate the use of the knowledge base and not to demonstrate the efficacy of the RGB-D sensor as a safety-rated sensor.
In our experiments, we used the CRAM extension described in [25] to automatically encoded the episodic memories from the safety-aware task executive.
Visual scene reconstruction is just one of the possible applications of our system, albeit a very intuitive one.
The symbolic human description we use is tailored to the modeling that the tracker provides. For instance, the tracker only detects one link when monitoring the hand of a human. Hence, human hands are modelled with only one link.
The safety-aware motion controller reports these collision types whenever the measured external torque of any joint exceeds \(5\%\), \(10\%\), \(15\%\), and \(30\%\) of the joint’s maximum torque, respectively.
For this experiment, we chose threshold values and collision classes on the basis of informed guesses. In the future, we plan to comply with recent industry standards, e.g. ISO/TS 15066 (https://www.iso.org/standard/62996.html).
Obviously, this is not a collaborative human–robot interaction experiment. At best, human co-workers would not interfere with the robot. In most cases, the interaction would disturb the robot and it had to safely cope.
http://www.open-ease.org please select experiment “Safe Interaction” and then “Preparing instruments”.
Such events typically occurred during pick-up actions when contact between gripper and table caused the safety-aware motion controller to raise a contact event.
The designator contains various constants, e.g. CARTESIAN-IMPEDANCE, light-collision, HARD-STOP. These are pre-defined constants of the communication protocol between the safety-aware task executive and the safety-aware motion controller.
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Acknowledgements
We gratefully acknowledge that this work was partially funded by the FP7 Project SAPHARI (Project ID: 287513) and by Deutsche Forschungsgemeinschaft (DFG) through the Collaborative Research Center 1320 EASE.
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Bartels, G., Beßler, D. & Beetz, M. Episodic Memories for Safety-Aware Robots. Künstl Intell 33, 123–130 (2019). https://doi.org/10.1007/s13218-019-00584-3
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DOI: https://doi.org/10.1007/s13218-019-00584-3