GOMP: Grasp-Optimized Motion Planning for Bin Picking

Rapid and reliable robot bin picking is a critical challenge in automating warehouses, often measured in picks-per-hour (PPH). We explore increasing PPH using faster motions based on optimizing over a set of candidate grasps. The source of this set of grasps is two-fold: (1) grasp-analysis tools such as Dex-Net generate multiple candidate grasps, and (2) each of these grasps has a degree of freedom about which a robot gripper can rotate. In this paper, we present Grasp-Optimized Motion Planning (GOMP), an algorithm that speeds up the execution of a bin-picking robot's operations by incorporating robot dynamics and a set of candidate grasps produced by a grasp planner into an optimizing motion planner. We compute motions by optimizing with sequential quadratic programming (SQP) and iteratively updating trust regions to account for the non-convex nature of the problem. In our formulation, we constrain the motion to remain within the mechanical limits of the robot while avoiding obstacles. We further convert the problem to a time-minimization by repeatedly shorting a time horizon of a trajectory until the SQP is infeasible. In experiments with a UR5, GOMP achieves a speedup of 9x over a baseline planner.

Authors

• Jeffrey Ichnowski
• Michael Danielczuk
• Jingyi Xu
• Vishal Satish
• Ken Goldberg

Keywords

• Trajectory
• Grippers
• Robot sensing systems
• Planning
• Manipulators
• Optimization
• Path Planning
• Bin-picking
• Degrees Of Freedom
• Quadratic Programming
• Fast Motion
• Trust Region
• Sequential Quadratic Programming
• Robotic Gripper
• Convolutional Neural Network
• Workspace
• Maximum Velocity
• Problem Definition
• Convex Optimization
• Robotic Arm
• Depth Camera
• Linear Constraints
• Coordinate Frame
• Obstacle Avoidance
• Additional Degrees Of Freedom
• Planning Algorithm
• Optimal Motion
• Warm Start
• Smooth Trajectory
• Fixed Time Interval
• Joint Velocity
• Suction Cup