BOMP: Bin-Optimized Motion Planning
This addresses productivity challenges in logistics automation by improving motion planning for bin-picking tasks, though it is incremental as it builds on existing optimization and learning techniques.
The paper tackles the problem of efficiently planning pick-and-place motions for robots in logistics by introducing BOMP, a framework that generates collision-free, jerk-limited trajectories up to 58% faster than baseline planners and 36% faster than an industry-standard method with a low 15% success rate.
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.