Kinematic-Model-Free Tip Position Control of Reconfigurable and Growing Soft Continuum Robots

Hmida, Ikhlas Mohamed Ben; Renda, Federico; Kormushev, Petar

doi:10.1109/robosoft55895.2023.10121994

Cited by 4 publications

(1 citation statement)

References 25 publications

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“…Model-based controllers depend on analytical models for deriving the controller, while model-free controllers circumvent the need to use kinematic and dynamic models [9]. Model-free controllers focus on using learningbased techniques to perform control without geometric models [10][11][12][13][14][15][16]. However, for most applications, model-based controls have been applicable since simplified models provide enough information to improve control performance significantly compared to the model-free baseline.…”

Section: Introductionmentioning

confidence: 99%

Toward precise force control of soft hand grasping: The viscoelastic parameter estimation of pseudo-soft-rigid-model by applying logarithmic decrement method

Zhang,

Heung,

Deshpande

et al. 2024

Preprint

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The rapid advancement in physical human-robot interaction (HRI) has propelled the growth of soft robot design and, in parallel, soft robot controllers. Controlling soft robots is complex due to their wide range of movements, prompting the use of simplified model-based controllers to provide sufficient information for real-time high dynamic response control performance. However, most modeling techniques face computational efficiency and complexity of parameter identification issues and are hard to apply to real-time controls. To alleviate this, we employ a coupled analytical modeling approach based on Pseudo-Rigid Body Modeling and the Logarithmic Decrement Method for parameter estimation (PRBM+LDM). Using a soft robot hand test bed, we demonstrate the accuracy of PRBM+LDM to model position and force output as a function of pressure input and benchmark its performance. We, then, apply the PRBM+LDM model as a basis for a closed-loop position controller and compare its performance against a simple PID controller. Furthermore, we apply the PRBM+LDM model as a closed-loop force controller and compare its performance with simple constant pressure grasping control by performing small contact areas pinching tasks on low-weight, small objects - a screwdriver, a potato chip, and a brass coin. The PRBM+LDM-based position controller (Average Max. Error across all fingers: 4.37°) outperformed the simple PID position controller (Average Max. Error across all fingers: 20.38°). Furthermore, the PRBM+LDM-based force controller (Potato chip: 86%, Screwdriver: 74.42%, Brass coin: 64.75%) achieved a higher success rate than the constant pressure grasping control (Potato chip: 82.5%, Screwdriver: 70%, Brass coin: 35%) in the pinching tasks. We conclude that the PRBM+LDM modeling technique proves to be a convenient and efficient way to model the dynamic behavior of soft actuators closely and can be used to build high-precision position and force controllers. In application, it realizes stable, flexible grasping of small objects by exerting precise contact force on contact areas.

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Section: Introductionmentioning

confidence: 99%

Toward precise force control of soft hand grasping: The viscoelastic parameter estimation of pseudo-soft-rigid-model by applying logarithmic decrement method

Zhang,

Heung,

Deshpande

et al. 2024

Preprint

View full text Add to dashboard Cite

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A Novel Parameter Estimation Method for Pneumatic Soft Hand Control Applying Logarithmic Decrement for Pseudo‐Rigid Body Modeling

Zhang,

Heung,

Naquila

et al. 2024

Advanced Intelligent Systems

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Controlling soft robots, especially soft hand grasping, is complex due to their ubiquitous deformation, prompting the use of reduced model‐based controllers to provide sufficient state information for high dynamic response control performance. However, most modeling techniques face computational efficiency and complexity of parameter identification issues. To alleviate this, a paradigm coupling an analytical modeling approach based on pseudo‐rigid body modeling and the logarithmic decrement method (PRBM + LDM) for parameter estimation is proposed. Using a soft robot hand test bed, the PRBM + LDM model for a closed‐loop position controller is applied and is compared with a simple proportional–integral–derivative controller (PID controller) static shape control of soft continuum robots using deep visual inverse kinematic models. Furthermore, the PRBM + LDM model‐based force controller is compared with simple constant pressure grasping control by pinching tasks on low‐weight, small objects—a screwdriver, a potato chip, and a brass coin. The PRBM + LDM‐based position controller outperforms the simple PID position controller, and the PRBM + LDM‐based force controller achieves a higher success rate than the constant pressure grasping control in the pinching tasks. In conclusion, the PRBM + LDM modeling technique proves to be a convenient and efficient way to model the dynamic behavior of soft actuators closely and can be applied to build high‐precision position and force controllers.

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A Soft Supernumerary Robotic Limb with Fiber-Reinforced Actuators

Xu,

Zhang,

Huang

et al. 2024

J Intell Robot Syst

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Supernumerary robotic limbs (SRLs) have great potentials to assist human in daily activities and industrial manufacturing by providing extra limbs. However, current SRLs have heavy and rigid structures that may threaten the operator safety; moreover, their limited degrees of freedom and movement modes are not suitable for complicated tasks. Although soft SRLs have exhibited advantages in structure compliance and flexible manipulation to address these problems, it remains challenging to accurately design the geometrical parameters to adapt to specific tasks, and accurate control is also required to realize the expected movement. Inspired by the biological characteristics of the octopus arm muscle fibers, fiber-reinforced actuators (FRAs) are employed to realize various motions, including extension, expansion, bending, and twisting; multiple FRAs are assembled to implement the SRL to achieve complex movement trajectories. The analytic model of the FRA is established to reveal the relationship between its deformation and geometrical parameters as well as input air pressures, which is validated with finite element simulation. Trajectory and payload optimization algorithms are proposed to optimally design the SRL and its control strategy with meeting the prescribed requirement of movement trajectory and payload capacity. Finally, experiments are conducted to validate the proposed robotic system.

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Kinematic-Model-Free Tip Position Control of Reconfigurable and Growing Soft Continuum Robots

Cited by 4 publications

References 25 publications

Toward precise force control of soft hand grasping: The viscoelastic parameter estimation of pseudo-soft-rigid-model by applying logarithmic decrement method

Toward precise force control of soft hand grasping: The viscoelastic parameter estimation of pseudo-soft-rigid-model by applying logarithmic decrement method

A Novel Parameter Estimation Method for Pneumatic Soft Hand Control Applying Logarithmic Decrement for Pseudo‐Rigid Body Modeling

A Soft Supernumerary Robotic Limb with Fiber-Reinforced Actuators

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