GOMP-FIT: Grasp-Optimized Motion Planning for Fast Inertial Transport
This addresses the need for cost-effective, fast robotic pick-and-place operations in automation settings like warehouses, but it is incremental as it builds on prior work with specific constraints.
The paper tackles the problem of high-speed robot motions causing spills when transporting open-top containers by proposing GOMP-FIT, a motion planner that incorporates acceleration constraints to use inertial forces advantageously, resulting in 0% spills with minimal speed reduction in obstacle-free scenarios.
High-speed motions in pick-and-place operations are critical to making robots cost-effective in many automation scenarios, from warehouses and manufacturing to hospitals and homes. However, motions can be too fast -- such as when the object being transported has an open-top, is fragile, or both. One way to avoid spills or damage, is to move the arm slowly. We propose an alternative: Grasp-Optimized Motion Planning for Fast Inertial Transport (GOMP-FIT), a time-optimizing motion planner based on our prior work, that includes constraints based on accelerations at the robot end-effector. With GOMP-FIT, a robot can perform high-speed motions that avoid obstacles and use inertial forces to its advantage. In experiments transporting open-top containers with varying tilt tolerances, whereas GOMP computes sub-second motions that spill up to 90% of the contents during transport, GOMP-FIT generates motions that spill 0% of contents while being slowed by as little as 0% when there are few obstacles, 30% when there are high obstacles and 45-degree tolerances, and 50% when there 15-degree tolerances and few obstacles. Videos and more at: https://berkeleyautomation.github.io/gomp-fit/.