A New Soft Robot Control Method: Using Model Predictive Control for a Pneumatically Actuated Humanoid

Best, Charles M.; Gillespie, Morgan T.; Hyatt, Phillip; Rupert, Levi; Sherrod, Vallan; Killpack, Marc D.

doi:10.1109/mra.2016.2580591

Cited by 101 publications

(61 citation statements)

References 19 publications

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“…A wide range of robotic continuum manipulators have been developed ranging from tendon‐driven manipulators installed on moving vehicles and others that behave as an octopus arm, pneumatically and vacuum‐driven continuum manipulators, vacuum‐driven continuum manipulators composed of stackable modules, fluidic elastomer manipulators, continuum actuators combining tendons and PAM actuators for combined position and stiffness control, and sPAMs‐driven continuum actuators . Other types of robotic arms have used jointed motions using either inflatable joints with a series of single degree‐of‐freedom (DOF) joints or PAMs to produce multi‐DOF joints capable of 3D positioning . It remains that the capability of jointed or continuum‐based robotic arms is directly dependent on the actuation capabilities of the soft actuator driving them.…”

Section: Introductionmentioning

confidence: 99%

Design of Paired Pouch Motors for Robotic Applications

Park

Lee

et al. 2018

Adv Materials Technologies

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Although other soft actuators have been built using smart materials or phasechanging materials, [8][9][10][11] soft pneumatic actuators have generally been the most used type of soft actuator. Pneumatic artificial muscles (PAMs), also called McKibben muscles, consist of a tubular matrix that expands radially and contract longitudinally upon pressurization, and have been widely used due to their large forces and contraction. [12][13][14] Although they can produce forces up to thousands of Newtons at high pressure and are readily available in the market, their range of motion is limited to ≈36.3% of their initial length. Pleated pneumatic artificial muscles have used pleats or fibers to improve the performance of the actuator. [15][16][17] Embedding sheets or fibers allows the fabrication of structure with complex programmed deformations based on origami structures, [18] and contraction ratios of 50% at 100 kPa were achieved using origami-based chambers with an external fiber mesh. [19] Inverse pneumatic artificial muscles capable of contraction ratios of 75% use the opposite principle where a pressure input elongates the actuator and the contraction stroke is done by deflating the actuator. [20] Using a Buckling elastomer under negative pressure has been developed to produce linear actuators capable of contraction ratios in the 40% range, [21,22] and a film containing a structure with repeated zig-zag patterns under vacuum pressure has been able to produce extremely large contraction ratios over 90%. [23] Extremely lightweight actuators made from plastic films have been developed that can produce sufficient forces for a wide range of applications. The first of these is pouch motors where two films are bonded flat and where the lateral expansion of the films upon inflation causes a longitudinal contraction up to 36% of their original lengths. [24] Films have also been used to develop serial pneumatic artificial muscles (sPAMS) where a long tube is restricted at regular intervals and can produce a contraction ratio up to 40% upon inflation. [25] Both actuators function on the same principle as PAMs but made from much lighter and flexible material.A wide range of robotic continuum manipulators have been developed ranging from tendon-driven manipulators installed on moving vehicles and others that behave as an octopus arm, [26,27] pneumatically and vacuum-driven continuum manipulators, [28] vacuum-driven continuum manipulators composed of stackable modules, [29] fluidic elastomer manipulators, [30] continuum actuators combining tendons and PAM actuators for combined position and stiffness control, [31,32] and sPAMs-driven continuum actuators. [25] Other types of robotic arms have used jointed motions using either inflatable joints with a series of single The performance of soft linear actuators will determine the capabilities of future soft robots, and any actuator that can produce larger deformations and forces with a low weight and using lower pressures could potentially become ubiquitous in the field. In this work,...

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Section: Introductionmentioning

confidence: 99%

Design of Paired Pouch Motors for Robotic Applications

Park

Lee

et al. 2018

Adv Materials Technologies

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“…Due to the deformable nature of under-actuated soft robotics, they are dynamically formulated using a system of infinite dimensions [23,58,93,[118][119][120]. These designs are currently unable to be reduced to an exact model which often leads to rigid-body assumptions to create manageable dynamic equations [121]. Figure 10 below shows a McKibben muscle controlling a rigid body mechanism, allowing for the deformation of the actuator to be easily measured with rigid body dynamic equations [122].…”

Section: Modelingmentioning

confidence: 99%

Soft Robotics: A Review of Recent Developments of Pneumatic Soft Actuators

et al. 2020

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This paper focuses on the recent development of soft pneumatic actuators for soft robotics over the past few years, concentrating on the following four categories: control systems, material and construction, modeling, and sensors. This review work seeks to provide an accelerated entrance to new researchers in the field to encourage research and innovation. Advances in methods to accurately model soft robotic actuators have been researched, optimizing and making numerous soft robotic designs applicable to medical, manufacturing, and electronics applications. Multi-material 3D printed and fiber optic soft pneumatic actuators have been developed, which will allow for more accurate positioning and tactile feedback for soft robotic systems. Also, a variety of research teams have made improvements to soft robot control systems to utilize soft pneumatic actuators to allow for operations to move more effectively. This review work provides an accessible repository of recent information and comparisons between similar works. Future issues facing soft robotic actuators include portable and flexible power supplies, circuit boards, and drive components.

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“…Research that is most related to ours in terms of trying to control a robot to a specific configuration includes the use of inflatable links with cable tendons (Sanan et al, 2009(Sanan et al, , 2011, fluid drive elastomer (Marchese et al, 2014a,b;Marchese and Rus, 2015), or rotary elastic chamber actuators such as in Ivlev (2009) and Gaiser et al (2014). However, in addition to other differences with past soft robot control work that we outline more specifically in Best et al (2016), as far as we know they have not developed control for these robots based on learned empirical models like those that we present in this paper. This is true except for work in Thuruthel et al (2017) where they learn dynamic models similar to what we present.…”

Section: Soft Robot Controlmentioning

confidence: 99%

Model-Based Control of Soft Actuators Using Learned Non-linear Discrete-Time Models

2019

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Soft robots have the potential to significantly change the way that robots interact with the environment and with humans. However, accurately modeling soft robot and soft actuator dynamics in order to perform model-based control can be extremely difficult. Deep neural networks are a powerful tool for modeling systems with complex dynamics such as the pneumatic, continuum joint, six degree-of-freedom robot shown in this paper.Unfortunately it is also difficult to apply standard model-based control techniques using a neural net. In this work, we show that the gradients used within a neural net to relate system states and inputs to outputs can be used to formulate a linearized discrete state space representation of the system. Using the state space representation, model predictive control (MPC) was developed with a six degree of freedom pneumatic robot with compliant plastic joints and rigid links. Using this neural net model, we were able to achieve an average steady state error across all joints of approximately 1 and 2 • with and without integral control respectively. We also implemented a first-principles based model for MPC and the learned model performed better in terms of steady state error, rise time, and overshoot. Overall, our results show the potential of combining empirical modeling approaches with model-based control for soft robots and soft actuators.

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A New Soft Robot Control Method: Using Model Predictive Control for a Pneumatically Actuated Humanoid

Cited by 101 publications

References 19 publications

Design of Paired Pouch Motors for Robotic Applications

Design of Paired Pouch Motors for Robotic Applications

Soft Robotics: A Review of Recent Developments of Pneumatic Soft Actuators

Model-Based Control of Soft Actuators Using Learned Non-linear Discrete-Time Models

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