Dataset Information

Smart Braid Feedback for the Closed-Loop Control of Soft Robotic Systems.

ABSTRACT: This article experimentally investigates the potential of using flexible, inductance-based contraction sensors in the closed-loop motion control of soft robots. Accurate motion control remains a highly challenging task for soft robotic systems. Precise models of the actuation dynamics and environmental interactions are often unavailable. This renders open-loop control impossible, while closed-loop control suffers from a lack of suitable feedback. Conventional motion sensors, such as linear or rotary encoders, are difficult to adapt to robots that lack discrete mechanical joints. The rigid nature of these sensors runs contrary to the aspirational benefits of soft systems. As truly soft sensor solutions are still in their infancy, motion control of soft robots has so far relied on laboratory-based sensing systems such as motion capture, electromagnetic (EM) tracking, or Fiber Bragg Gratings. In this article, we used embedded flexible sensors known as Smart Braids to sense the contraction of McKibben muscles through changes in inductance. We evaluated closed-loop control on two systems: a revolute joint and a planar, one degree of freedom continuum manipulator. In the revolute joint, our proposed controller compensated for elasticity in the actuator connections. The Smart Braid feedback allowed motion control with a steady-state root-mean-square (RMS) error of [1.5]°. In the continuum manipulator, Smart Braid feedback enabled tracking of the desired tip angle with a steady-state RMS error of [1.25]°. This work demonstrates that Smart Braid sensors can provide accurate position feedback in closed-loop motion control suitable for field applications of soft robotic systems.

SUBMITTER: Felt W

PROVIDER: S-EPMC5649447 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Smart Braid Feedback for the Closed-Loop Control of Soft Robotic Systems.

Felt Wyatt W Chin Khai Yi KY Remy C David CD

Soft robotics 20170901 3

This article experimentally investigates the potential of using flexible, inductance-based contraction sensors in the closed-loop motion control of soft robots. Accurate motion control remains a highly challenging task for soft robotic systems. Precise models of the actuation dynamics and environmental interactions are often unavailable. This renders open-loop control impossible, while closed-loop control suffers from a lack of suitable feedback. Conventional motion sensors, such as linear or ro ...[more]

PMID: 29062629

Similar Datasets

Project description:BackgroundSegmented cine cardiac MRI combines data from multiple heartbeats to achieve high spatiotemporal resolution cardiac images, yet predefined k-space segmentation trajectories can lead to suboptimal k-space sampling. In this work, we developed and evaluated an autonomous and closed-loop control system for radial k-space sampling (ARKS) to increase sampling uniformity.MethodsThe closed-loop system autonomously selects radial k-space sampling trajectory during live segmented cine MRI and attempts to optimize angular sampling uniformity by selecting views in regions of k-space that were not previously well-sampled. Sampling uniformity and the ability to detect cardiac phase in vivo was assessed using ECG data acquired from 10 normal subjects in an MRI scanner. The approach was then implemented with a fast gradient echo sequence on a whole-body clinical MRI scanner and imaging was performed in 4 healthy volunteers. The closed-loop k-space trajectory was compared to random, uniformly distributed and golden angle view trajectories via measurement of k-space uniformity and the point spread function. Lastly, an arrhythmic dataset was used to evaluate a potential application of the approach.ResultsThe autonomous trajectory increased k-space sampling uniformity by 15±7%, main lobe point spread function (PSF) signal intensity by 6±4%, and reduced ringing relative to golden angle sampling. When implemented, the autonomous pulse sequence prescribed radial view angles faster than the scan TR (0.98 ± 0.01 ms, maximum = 1.38 ms) and increased k-space sampling mean uniformity by 10±11%, decreased uniformity variability by 44±12%, and increased PSF signal ratio by 6±6% relative to golden angle sampling.ConclusionThe closed-loop approach enables near-uniform radial sampling in a segmented acquisition approach which was higher than predetermined golden-angle radial sampling. This can be utilized to increase the sampling or decrease the temporal footprint of an acquisition and the closed-loop framework has the potential to be applied to patients with complex heart rhythms.

Project description:Adaptable gain regulation is at the core of the forward controller operation performed by the cerebro-cerebellar loops and it allows the intensity of motor acts to be finely tuned in a predictive manner. In order to learn and store information about body-object dynamics and to generate an internal model of movement, the cerebellum is thought to employ long-term synaptic plasticity. LTD at the PF-PC synapse has classically been assumed to subserve this function (Marr, 1969). However, this plasticity alone cannot account for the broad dynamic ranges and time scales of cerebellar adaptation. We therefore tested the role of plasticity distributed over multiple synaptic sites (Hansel et al., 2001; Gao et al., 2012) by generating an analog cerebellar model embedded into a control loop connected to a robotic simulator. The robot used a three-joint arm and performed repetitive fast manipulations with different masses along an 8-shape trajectory. In accordance with biological evidence, the cerebellum model was endowed with both LTD and LTP at the PF-PC, MF-DCN and PC-DCN synapses. This resulted in a network scheme whose effectiveness was extended considerably compared to one including just PF-PC synaptic plasticity. Indeed, the system including distributed plasticity reliably self-adapted to manipulate different masses and to learn the arm-object dynamics over a time course that included fast learning and consolidation, along the lines of what has been observed in behavioral tests. In particular, PF-PC plasticity operated as a time correlator between the actual input state and the system error, while MF-DCN and PC-DCN plasticity played a key role in generating the gain controller. This model suggests that distributed synaptic plasticity allows generation of the complex learning properties of the cerebellum. The incorporation of further plasticity mechanisms and of spiking signal processing will allow this concept to be extended in a more realistic computational scenario.

Dataset Information

Smart Braid Feedback for the Closed-Loop Control of Soft Robotic Systems.

Publications

Smart Braid Feedback for the Closed-Loop Control of Soft Robotic Systems.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets