Experimental assessment and prediction of design parameter influences on a specific vacuum-based granular gripper
Tóm tắt
Innovative soft robotic grippers, such as granular grippers, enable the automated handling of a wide spectrum of different geometries, increasing the flexibility and robustness of industrial production systems. Granular grippers vary in their design as well as in their configuration, which affects the specific characteristics and capabilities regarding grippable objects. Relevant aspects are the selection of granulates and membranes, as they affect the deformability. This influences the achievable gripping forces, which vary with the gripped objects geometry. On the basis of experimental studies, the modeling of interpolations as well as through experimental validations, the present research investigates the influences of different configurations on the achievable gripping forces for a specific concept of an innovative vacuum-based granular gripper. Specifically, the focus lies on design as well as configuration parameters, which could influence the achievable gripping force. Influencing parameters are determined based on a literature review of similar gripping concepts. Various adjustment possibilities are identified, such as materials of granulates or membranes. The possible configuration options are experimentally analyzed with a one-factor-at-a-time approach. The possibility of modelling the effects of their interrelations on the achievable gripping force is examined with approaches for linear models and compared to interpolations based on Machine Learning. Especially the granulate filling level and the membrane configuration exhibit the largest influences, which were best predicted with the approach based on artificial neural networks. A selection of an optimized gripper configuration for a specified object set as well as possible further developments such as a continuous expandability of the approaches and integrations with simulations are discussed. As a result of these analyses, this research provides methodologies for an optimized selection of a gripper configuration for an improved object-specific achievable gripping force and allows for more efficient handling processes with the examined type of vacuum-based granular gripper.
Tài liệu tham khảo
Birglen L, Schlicht T (2018) A statistical review of industrial robotic grippers. Robot Comp Integr Manuf 49:88
Pham D, Yeo S (1991) Strategies for gripper design and selection in robotic assembly. Int J Prod Res 29:303–16
Gabriel F, Römer M, Bobka P et al (2021) Model-based grasp planning for energy-efficient vacuum-based handling. CIRP Annals 70(1):1–4
Kehoe B, Berenson D, Goldberg K (2012) Estimating part tolerance bounds based on adaptive Cloud-based grasp planning with slip. In: IEEE International Conference, pp 1106–1113
Navarro-Guerrero N, Toprak S, Josifovski J, Jamone L (2023) Visuo-haptic object perception for robots: an overview. Auton Robots. 47(4):377–403
Azim MS, Lobov A, Pastukhov A (2019) Methodology for implementing universal gripping solution for robot application. Proc Estonian Acad Sci 68:4
Shintake J, Cacucciolo V, Floreano D et al (2018) Soft robotic grippers. Adv Mater. 30(29):1707035
Mykhailyshyn R, Savkiv V, Maruschak P et al (2022) A systematic review on pneumatic gripping devices for industrial robotics. Transport 37(3):201–31
Götz H, Santarossa A, Sack A et al (2022) Soft particles reinforce robotic grippers: robotic grippers based on granular jamming of soft particles. Granular Matter 24:1
Fitzgerald SG, Delaney GW, Howard D (2020) A review of jamming actuation in soft robotics. Actuators 9:1
Brown E, Rodenberg N, Amend J et al (2010) Universal robotic gripper based on the jamming of granular material. Proc Natl Acad Sci USA 107(44):18809–14
Santarossa A, D’Angelo O, Sack A, Pöschel T (2023) Effect of particle size on the suction mechanism in granular grippers. Granular Matter. 25(1):16
Dröder K, Dietrich F, Löchte C et al (2016) Model based design of process-specific handling tools for workpieces with many variants in shape and material. CIRP Annals 65(1):53–6
Löchte CW (2016) Formvariable Handhabung mittels granulatbasierter Niederdruckflächensauger. Dissertation, TU Braunschweig
Kunz H, Löchte C, Dietrich F, Raatz A, Fischer F, Dröder K, Dilger K (2015) Novel form-flexible handling and joining tool for automated preforming. Sci Eng Comp Mater 22(2):199–213
Wacker C, Dierks N, Illgen J et al. (2022) Empirically Adapted Model for the Handling of Variable Geometries with Vacuum-Based Granulate Grippers. Proceedings of the 7th MHI Colloquium 2022
Nishida T, Shigehisa D, Kawashima N et al. (2014) Development of universal jamming gripper with a force feedback mechanism. In: 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS), pp 242–246
Shanmugasundar G, Dharanidharan M, Vishwa D et al. (2022) A study on design of universal gripper for different part handling: Methods, mechanisms, and materials. In: RECENT TRENDS IN SCIENCE AND ENGINEERING, p 20115
Friedmann M, Fleischer J (2022) Automated configuration of modular gripper fingers. Procedia CIRP 106:70–75
Mishra R, Philips T, Delaney GW et al. (2021) Vibration Improves Performance in Granular Jamming Grippers
Schmalz J (2018) Rechnergestützte Auslegung und Auswahl von Greifersystemen. Dissertation, TU München
Amend JR, Brown E, Rodenberg N et al (2012) A positive pressure universal gripper based on the jamming of granular material. IEEE Trans Robot 28(2):341–50
Amend J, Cheng N, Fakhouri S et al (2016) Soft robotics commercialization: jamming grippers from research to product. Soft Robot 3(4):213–22
Meuleman S, Balt V, Jarray A et al. (2017) Investigation of Particle Properties on the Holding Force in a Granular Gripper. V International Conference on Particle-based Methods - Fundamentals and Applications
Alsakarneh A, Alnaqbi S, Alkaabi M et al. (2018) Experimental analysis of the holding-force of the jamming grippers. In: 2018 Advances in Science and Engineering Technology International Conferences (ASET), pp 1–3
Fujita M, Tadakuma K, Komatsu H et al (2018) Jamming layered membrane gripper mechanism for grasping differently shaped-objects without excessive pushing force for search and rescue missions. Adv Robot 32(11):590–604
Miettinen J, Frilund P, Vuorinen I et al (2019) Granular jamming based robotic gripper for heavy objects. Proc Estonian Acad Sci 68(4):421–8
Howard D, O'Connor J, Brett J et al. (2021) Shape, Size, and Fabrication Effects in 3D Printed Granular Jamming Grippers. In: 2021 IEEE 4th International Conference on Soft Robotics (RoboSoft), pp 458–464
Gómez-Paccapelo JM, Santarossa AA, Bustos HD et al (2021) Effect of the granular material on the maximum holding force of a granular gripper. Granular Matter 23:1–6
Aktaş B, Narang YS, Vasios N et al (2021) A modeling framework for jamming structures. Adv Funct Mater 31(16):2007554
Athanassiadis AG, Miskin MZ, Kaplan P et al (2014) Particle shape effects on the stress response of granular packings. Soft Matter 10(1):48–59
Putzu F, Konstantinova J, Althoefer K (2019) Soft particles for granular jamming. Towards Autonomous Robotic Systems: 20th Annual Conference Proceedings, Part II
Löchte C, Kunz H, Schnurr R et al (2014) Form-flexible handling and joining technology (FormHand) for the forming and assembly of limp materials. Procedia CIRP 23:206–11
Jiang A, Ranzani T, Gerboni G et al. (2014) Robotic Granular Jamming: Does the Membrane Matter. Soft Robot 1
Howard D, O'Connor J, Letchford J et al. (2022) Getting a Grip: in Materio Evolution of Membrane Morphology for Soft Robotic Jamming Grippers. In: 2022 IEEE 5th International Conference on Soft Robotics (RoboSoft), pp 531–538
Howard GD, Brett J, O’Connor J et al (2022) One-shot 3D-printed multimaterial soft robotic jamming grippers. Soft Robot 9:1
FORMHAND Automation GmbH FH-R150. formhand.de/de/produkte/produkt-FH-R150. Acc. 01 Dec 2021
Kaufman RL (2013) Heteroskedasticity in regression: Detection and correction. Sage Publications
Cao B, Adutwum LA, Oliynyk AO et al (2018) How to optimize materials and devices via design of experiments and machine learning: demonstration using organic photovoltaics. ACS Nano 12:1
Elbadawi M, McCoubrey LE, Gavins FKH et al (2021) Harnessing artificial intelligence for the next generation of 3D printed medicines. Adv Drug Deliv Rev 175:113805
Akiyoshi M, Ikemoto K, Isobe H (2023) Tier-grown expansion of design-of-experiments parameter spaces for synthesis of a nanometer-scale macrocycle. Chemistry 18:2