BOMP: Bin-Optimized Motion Planning

In logistics, the ability to quickly compute and execute pick-and-place motions from bins is critical to increasing productivity. We present Bin-Optimized Motion Planning (BOMP), a motion planning framework that plans arm motions for a six-axis industrial robot with a long-nosed suction tool to remo...

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Bibliographic Details
Published in:arXiv.org 2024-10
Main Authors: Tam, Zachary, Dharmarajan, Karthik, Qiu, Tianshuang, Yahav Avigal, Ichnowski, Jeffrey, Goldberg, Ken
Format: Article
Language:English
Subjects:
Actuation
Algorithms
Artificial neural networks
Axis movements
Bins
Collision avoidance
Industrial robots
Kinematics
Motion planning
Planning
Robot arms
Robot dynamics
Suction
Trajectory optimization
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Summary:In logistics, the ability to quickly compute and execute pick-and-place motions from bins is critical to increasing productivity. We present Bin-Optimized Motion Planning (BOMP), a motion planning framework that plans arm motions for a six-axis industrial robot with a long-nosed suction tool to remove boxes from deep bins. BOMP considers robot arm kinematics, actuation limits, the dimensions of a grasped box, and a varying height map of a bin environment to rapidly generate time-optimized, jerk-limited, and collision-free trajectories. The optimization is warm-started using a deep neural network trained offline in simulation with 25,000 scenes and corresponding trajectories. Experiments with 96 simulated and 15 physical environments suggest that BOMP generates collision-free trajectories that are up to 58 % faster than baseline sampling-based planners and up to 36 % faster than an industry-standard Up-Over-Down algorithm, which has an extremely low 15 % success rate in this context. BOMP also generates jerk-limited trajectories while baselines do not. Website: https://sites.google.com/berkeley.edu/bomp.
ISSN:2331-8422