Industry 4.0 projects ubiquitous collaborative robots in smart factories of the future, in particular, assembly and material handling. To ensure efficient and safe human-robot collaborative interactions, this paper presents a novel algorithm for estimating Risk of Passage (ROP) a robot incurs by passing between dynamic obstacles (humans, moving equipment, etc.).&nbsp; This paper posits that robot trajectory durations will be shorter and safer if the robot can react proactively to predicted collision between a robot and human worker before it occurs, compared to reacting when it is imminent.&nbsp; I.e., if the risk that obstacles may prohibit robot passage at a future time in the robot’s trajectory is greater than a user defined risk limit, then an Obstacle Pair Volume (OPV), encompassing the obstacles at that time, is added to the planning scene.&nbsp; Results found from simulation show that an ROP algorithm can be trained in ~120 workcell cycles.&nbsp; Further, it is demonstrated that when a trained ROP algorithm introduces an OPV, trajectory durations are shorter compared to those avoiding obstacles without the introduction of an OPV.&nbsp; In summary, the use of ROP estimation with addition of OPV allows workcells to operate with shorter cycle times in the presence of unforeseen obstacles. Keywords: human-robot collaboration, adaptive control, augmented intelligence, machine learning, smart factory, industry 4.0

Collaborative Robot Risk of Passage Among Dynamic Obstacles

Paper Proceedings: https://asmedigitalcollection.asme.org/MSEC/MSEC2021/volume/85062

2021 International Manufacturing Science and Engineering Conference (MSEC2021)

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