Suction cup grasping is very common in industry, but moving too quickly can cause suction cups to detach, causing drops or damage. Maintaining a suction grasp throughout a high-speed motion requires balancing suction forces against inertial forces while the suction cups deform under strain. In this paper, we consider Grasp Optimized Motion Planning for Suction Transport (GOMP-ST), an algorithm that combines deep learning with optimization to decrease transport time while avoiding suction cup failure. GOMP-ST first repeatedly moves a physical robot, vacuum gripper, and a sample object, while measuring pressure with a solid-state sensor to learn critical failure conditions. Then, these are integrated as constraints on the accelerations at the end-effector into a time-optimizing motion planner. The resulting plans incorporate real-world effects such as suction cup deformation that are difficult to model analytically. In GOMP-ST, the learned constraint, modeled with a neural network, is linearized using Autograd and integrated into a sequential quadratic program optimization. In 420 experiments with a physical UR5 transporting objects ranging from 1.3 to 1.7 kg, we compare GOMP-ST to baseline optimizing motion planners. Results suggest that GOMP-ST can avoid suction cup failure while decreasing transport times from 16 to 58%. For code, video, and datasets, see https://sites.google.com/view/gomp-st


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    GOMP-ST: Grasp Optimized Motion Planning for Suction Transport


    Weitere Titelangaben:

    Springer Proceedings in Advanced Robotics


    Beteiligte:
    LaValle, Steven M. (Herausgeber:in) / O’Kane, Jason M. (Herausgeber:in) / Otte, Michael (Herausgeber:in) / Sadigh, Dorsa (Herausgeber:in) / Tokekar, Pratap (Herausgeber:in) / Avigal, Yahav (Autor:in) / Ichnowski, Jeffrey (Autor:in) / Cao, Max Yiye (Autor:in) / Goldberg, Ken (Autor:in)

    Kongress:

    International Workshop on the Algorithmic Foundations of Robotics ; 2022 ; , MD, USA June 22, 2022 - June 24, 2022



    Erscheinungsdatum :

    2022-12-15


    Format / Umfang :

    18 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Integrated Grasp and Motion Planning using Independent Contact Regions

    FONTANALS MARTÍNEZ, JOAN | BASE | 2015

    Freier Zugriff

    Grasp planning under uncertainty

    Erkmen, A. M. / Stephanou, H. E. | NTRS | 1989


    Optimal grasp of vacuum grippers with multiple suction cups

    Mantriota, Giacomo | Online Contents | 2007


    93ME105 Automatic Motion Planning Through an Environment Generated by GRASP

    Sanders, D. A. / Stott, I. / Liu, K. P. et al. | British Library Conference Proceedings | 1993


    Error-Tolerant Visual Planning of Planar Grasp

    Davidson, C. / Blake, A. / IEEE; Computer Society | British Library Conference Proceedings | 1998