BOMP: Bin-Optimized Motion Planning

TL;DR

BOMP is a motion planning framework that uses deep learning and optimization to generate fast, collision-free, and smooth robot trajectories for bin picking tasks, outperforming traditional methods.

Contribution

It introduces a neural network warm-started optimization approach for rapid, collision-free, and jerk-limited motion planning in bin picking scenarios.

Findings

BOMP achieves up to 58% faster trajectories than baseline planners.

BOMP outperforms industry-standard algorithms with up to 36% speed improvement.

BOMP generates jerk-limited trajectories, unlike baseline methods.

Abstract

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.