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 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.

Authors

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

Keywords

• Productivity
• Service robots
• Kinematics
• Manipulators
• Industrial robots
• Trajectory
• Planning
• Optimization
• Intelligent robots
• Logistics
• Path Planning
• Neural Network
• Deep Neural Network
• Robotic Arm
• Height Map
• Collision-free Trajectory
• Computation Time
• Point Cloud
• Learning-based Methods
• Box Size
• Depth Images
• Quadratic Programming
• Depth Camera
• Joint Space
• End-effector
• Trajectory Optimization
• Physical Experiments
• Obstacle Avoidance
• Reduce Computation Time
• Sequential Quadratic Programming
• Collision Detection
• Compact Network
• Box Dimensions
• Trajectory Duration
• Closest Distance
• Neural Network Output
• Parallelization
• Ground-truth Box
• Reachable