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BOMP: Bin-Optimized Motion Planning

Zachary Tam, Karthik Dharmarajan, Tianshuang Qiu, Yahav Avigal, Jeffrey Ichnowski, Ken Goldberg

TL;DR

BOMP is 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 that 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.

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.

BOMP: Bin-Optimized Motion Planning

TL;DR

BOMP is 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 that 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.

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.

Paper Structure

This paper contains 11 sections, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem Statement
  4. Method
  5. Grasped Box Shape Estimation
  6. Suction Grasp Selection
  7. Optimization Formulation
  8. Experiments
  9. Simulated Experiments
  10. Physical Experiments
  11. Discussion

Figures (2)

  • Figure 1: Bin-optimized motion planning. BOMP executing a time-optimized, jerk-limited, collision-free trajectory moving a box from a bin to a drop-off point. We use the long-nosed "bluction" tool from Huang, et al. huang2022bluction to enable the robot to reach all parts of the deep bin, and an overhead RGBD camera to detect obstacles and target boxes. BOMP uses an optimization-based motion planner to compute the pick-and-place trajectory. In order to speed up the computation, a neural network warm-starts the optimizer. It accepts the obstacle environment, grasped box, and pick and place poses as input, and outputs an initial trajectory.
  • Figure 2: Challenging grasp poses. In physical experiments, we observe that using the long suction tool to grasp arbitrarily oriented boxes sometimes results in challenging grasp poses such as the ones pictured here. While the industry-standard Up-Over-Down method fails in these cases, BOMP is able to generate fast, jerk-limited, collision-free trajectories.