CoBOS: Constraint-Based Online Scheduler for Human-Robot Collaboration
CoRR(2024)
摘要
Assembly processes involving humans and robots are challenging scenarios
because the individual activities and access to shared workspace have to be
coordinated. Fixed robot programs leave no room to diverge from a fixed
protocol. Working on such a process can be stressful for the user and lead to
ineffective behavior or failure. We propose a novel approach of online
constraint-based scheduling in a reactive execution control framework
facilitating behavior trees called CoBOS. This allows the robot to adapt to
uncertain events such as delayed activity completions and activity selection
(by the human). The user will experience less stress as the robotic coworkers
adapt their behavior to best complement the human-selected activities to
complete the common task. In addition to the improved working conditions, our
algorithm leads to increased efficiency, even in highly uncertain scenarios. We
evaluate our algorithm using a probabilistic simulation study with 56000
experiments. We outperform all baselines by a margin of 4-10
robot experiments using a Franka Emika Panda robot and human tracking based on
HTC Vive VR gloves look promising.
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