GOMP-FIT: Grasp-Optimized Motion Planning for Fast Inertial Transport (2110.15326v2)

Abstract: 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/.

• The paper introduces a novel motion planning framework that integrates end-effector acceleration constraints for rapid and safe inertial transport.
• It formulates the Fast Inertial Transport (FIT) problem, achieving significant improvements over traditional planners with minimal spillage and constraint violations.
• Empirical tests on a UR5 robot validate GOMP-FIT's robustness and efficiency across varying object fragility and operational constraints.

Insightful Review of "GOMP-FIT: Grasp-Optimized Motion Planning for Fast Inertial Transport"

The paper "GOMP-FIT: Grasp-Optimized Motion Planning for Fast Inertial Transport" presents a significant advance in the domain of robotic motion planning, specifically targeting the challenge of high-speed inertial transport. The authors, led by Jeffrey Ichnowski from AUTOLab, University of California, Berkeley, introduce an innovative approach known as Grasp-Optimized Motion Planning for Fast Inertial Transport (GOMP-FIT). This methodology addresses the inherent trade-off between speed and safety in automated pick-and-place operations prevalent in industrial automation.

The crux of the research lies in developing a motion planner that leverages inertial forces beneficially, allowing robotic arms to perform rapid transit motions while minimizing risks such as spills or damage associated with fragile and open-top objects. Unlike traditional methods that necessitate reducing operational speed to avoid these risks, GOMP-FIT employs constraints rooted in inertial dynamics, applied at the robot's end-effector, thereby facilitating swift motion planning without compromising on safety.

Key Contributions

• The introduction of time-optimizing motion planning that incorporates end-effector acceleration constraints, enabling robots to handle objects with varying levels of fragility and coverage efficiently.
• Formulation of the Fast Inertial Transport (FIT) problem, an algorithmic approach to address high-speed transport requirements while maintaining object integrity.
• A structure that supports various constraints depending on the object properties, such as keeping contents inside open-top containers and avoiding overloading fragile objects.

Numerical Experiments and Results

GOMP-FIT was empirically validated using a UR5 robot, displaying its capability in a variety of scenarios. Noteworthy results include:

• Successful transport of open-top containers without spillage over multiple trials, illustrating substantial reliability over traditional planners like GOMP, which reported up to 90% spillage.
• Robust performance in transporting fragile objects, keeping acceleration norms well within stipulated limits, demonstrated by a negligible integrated violation of acceleration constraints.
• Notably, while maintaining high reliability, GOMP-FIT exhibits a minimal slowdown even when height and tilt constraints are intensified.

Implications and Future Directions

The implications of this work are profound in both academic and practical contexts. The GOMP-FIT framework provides a versatile tool for improving throughput in environments where the rapid, safe transport of a diverse range of objects is critical. The introduction of acceleration constraints in robot end-effector motion planning is a noteworthy conceptual advancement, potentially impacting future methodologies in robotic manipulation.

Looking forward, the scope for developing this research includes optimizing the computational speed of GOMP-FIT. Enhancements could be realized through employing machine learning techniques to aid in trajectory prediction, thus reducing computation times and allowing broader applicability in real-time systems. Additionally, transitioning from conservative approximations to torque-based limits could endow the system with greater adaptability and further enhance the speed and safety balance.

In summary, GOMP-FIT represents a comprehensive approach to solving a sophisticated problem in robotic motion planning, with potential applications extending across logistical and industrial automation domains, among others. The methodology and results provide a firm foundation for future research endeavors aimed at exploring dynamic, efficient, and safe robotic operations.