Development of an Adaptive Force Control Strategy for Soft Robotic Gripping

被引:1
|
作者
MacDonald, Ian [1 ]
Dubay, Rickey [1 ]
机构
[1] Univ New Brunswick, Dept Mech Engn, Fredericton, NB E3B 5A3, Canada
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 16期
基金
加拿大自然科学与工程研究理事会;
关键词
soft robotics; adaptive control; computer vision;
D O I
10.3390/app14167354
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Using soft materials in robotic mechanisms has become a common solution to overcome many challenges associated with the rigid bodies frequently used in robotics. Compliant mechanisms allow the robot to adapt to objects and perform a broader range of tasks, unlike rigid bodies that are generally designed for specific applications. However, soft robotics presents its own set of challenges in both design and implementation, particularly in sensing and control. These challenges are abundant when dealing with the force control problem of a compliant gripping mechanism. The ability to effectively regulate the applied force of a gripper is a critical task in many control operations, as it allows the precise manipulation of objects, which drives the need for enhanced force control strategies for soft or flexible grippers. Standard sensing techniques, such as motor current monitoring and strain-based sensors, add complexities and uncertainties when establishing mathematical models of soft grippers to the required gripping forces. In addition, the soft gripper creates a complex non-linear system, compounded by adding an adhesive-type sensor. This work develops a unique visual force sensor trained on synthetic data generated using finite element analysis (FEA) and implemented by integrating a non-linear model reference adaptive controller (MRAC) to control gripping force on a fixed 6-DOF robot. The robot can be placed on a mobile platform to perform various tasks. The virtual FEA sensor and controller, combined, are termed virtual reference adaptive control (VRAC). The VRAC was compared to other methods and achieved comparable control sensing and control performance while reducing the complexity of the sensor requirements and its integration. The VRAC strategy effectively controlled the gripping force by driving the dynamics to match the desired performance after a limited amount of training cycles. The controller proposed in this work was designed to be generally applicable to most objects that the gripper will interact with and easily adaptable to a wide variety of soft grippers.
引用
收藏
页数:19
相关论文
共 50 条
  • [31] Force Feedback Control of a Robotic Needle Insertion into Layered Soft Tissues
    Yang, Tangwen
    Xiao, Lifang
    Chen, Panfei
    Zhu, Haifeng
    Zhao, Xingang
    Song, Guoli
    Han, Jianda
    Xu, Weiliang
    PROCEEDINGS OF THE 2018 25TH INTERNATIONAL CONFERENCE ON MECHATRONICS AND MACHINE VISION IN PRACTICE (M2VIP), 2018, : 43 - 47
  • [32] Fuzzy control strategy for an adaptive force control in end-milling
    Zuperl, U
    Cus, F
    Milfelner, A
    JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2005, 164 : 1472 - 1478
  • [33] The Development of a Gesture Controlled Soft Robot Gripping Mechanism
    Gunawardane, P. D. S. H.
    Medagedara, Nimali T.
    Madusanka, B. G. D. A.
    Wijesinghe, Singhe
    2016 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION FOR SUSTAINABILITY (ICIAFS): INTEROPERABLE SUSTAINABLE SMART SYSTEMS FOR NEXT GENERATION, 2016,
  • [34] Nanotweezers with Proximity Sensing and Gripping Force Control System
    Umemoto, Takeshi
    Ayano, Kenjiro
    Suzuki, Masato
    Yasutake, Masatoshi
    Konno, Takashi
    Hashiguchi, Gen
    JAPANESE JOURNAL OF APPLIED PHYSICS, 2009, 48 (08)
  • [35] Adaptive force control of robotic finger actuated by shape memory alloy
    Li X.-G.
    Zhang B.
    Zhang D.-H.
    Zhao X.-G.
    Han J.-D.
    Kongzhi Lilun Yu Yingyong/Control Theory and Applications, 2021, 38 (01): : 33 - 43
  • [36] Adaptive Robust Interaction Force Control of a Robotic Manipulator in Uncertain Environments
    Huang, Junsheng
    Yuan, Mingxing
    Huo, Zixuan
    Zhang, Shuaikang
    Zhang, Xuebo
    IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2025,
  • [37] A novel adaptive hybrid force-position control of a robotic manipulator
    Nganga-Kouya, Donatien
    Saad, Maarouf
    Okou, Francis A.
    INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL, 2011, 13 (1-2) : 97 - 107
  • [38] Soft-smart robotic end effectors with sensing, actuation, and gripping capabilities
    Xiang, Chaoqun
    Guo, Jianglong
    Rossiter, Jonathan
    SMART MATERIALS AND STRUCTURES, 2019, 28 (05)
  • [39] An Adaptive Control-Based Approach for 1-Click Gripping of Novel Objects Using a Robotic Manipulator
    Ding, Zhangchi
    Paperno, Nicholas
    Prakash, Kiran
    Behal, Aman
    IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2019, 27 (04) : 1805 - 1812
  • [40] ADAPTIVE BACKSTEPPING CONTROL OF THE GRIPPING PROCESS OF HEAVY MANIPULATORS
    Yang, Lixin
    Zhang, Xianmin
    PROCEEDINGS OF THE ASME INTERNATIONAL MECHANICAL ENGINEERING CONGRESS AND EXPOSITION, 2017, VOL 4B, 2018,