Mixing State Estimation of Peristaltic Continuous Mixing Conveyor with Distributed Sensing System Based on Soft Intestine Motion

Wakamatsu, Kota; Inoue, Katsuma; Hagiwara, Daiki; Adachi, Haruka; Matsui, Daiki; Kurumaya, Shunichi; Nishihama, Rie; Okui, Manabu; Nakajima, Kohei; Kuniyoshi, Yasuo; Nakamura, Taro

doi:10.1109/robosoft48309.2020.9116058

Cited by 9 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning-based mixture estimation using compressed air sensing on the subject of mixing powders and liquids has been conducted previously [13]. A machine learning model was constructed using three different air pressure and flow rate sensor values attached to three units.…”

Section: B Problems With Previous Estimation Methodsmentioning

confidence: 99%

“…To obtain a stable output, the transition pattern of the state signal is focused, regression models are constructed according to the transition pattern before and after the state signal, and the final mixed estimation is calculated by the weighted average of the estimates of these models. This model is based on the mixture estimation model [13] proposed in the previous study with some improvements, such as the labeling method. The method used to obtain the training data is described in the next section.…”

Section: B Proposed Mixing Estimation Modelmentioning

confidence: 99%

“…Therefore, reproducing intestinal motility via the device functioning similar to the intestine can lead to a better understanding of intestinal function. Our system is designed to determine the mixing state in each unit from the sensor values installed in the device [12,13]. In the future, we aim to control the driving pattern of the device autonomously based on the estimated mixing degree to achieve efficient device driving.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, a sensing system using the pressure and flow rate sensors was developed, focusing on the air pressure applied to the device [12]. The system could estimate the degree of mixing via machine learning from the time-series information of the sensors [13]. On the other hand, the quantity of both training and testing data was small, so the results of the proposed estimation method were not evaluated sufficiently.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Generalization Capability of Mixture Estimation Model for Peristaltic Continuous Mixing Conveyor

et al. 2021

View full text Add to dashboard Cite

We propose herein a method for estimating the mixing state of the contents of a peristaltic continuous mixing conveyor simulating the intestine, developed for mixing and conveying powders and liquids. This study serves to improve a previously proposed method for estimating the mixing state using a logistic regression model with the pressure and flow rate sensors installed in the device as inputs. Moreover, the estimation accuracy of the proposed method is better than that of the previous method. The generalizability of the proposed method is evaluated for four conditions in which the feeding order of the contents, powder, and liquid are changed. The feeding order is as follows: powder first, liquid first, and powder and liquid alternately. As a result, a highly accurate estimation of mixing is achieved under the condition wherein the powder component is in the unit adjacent to the lid, but not under the condition wherein the liquid component is fed first. It is speculated that this is because the movement of the powder component inside the device is more easily reflected by the pressure and flow rate sensors installed in the device than in the liquid component.

show abstract

Section: B Problems With Previous Estimation Methodsmentioning

confidence: 99%

Section: B Proposed Mixing Estimation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Generalization Capability of Mixture Estimation Model for Peristaltic Continuous Mixing Conveyor

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Therefore, we partially developed a sensing function [11] and system with a distributed arrangement like that of the intestinal tract. The two issues of estimating the mixing state of the contents [12][13] and automatic switching of the drive pattern according to the state of the contents [14] were verified, respectively. For the estimation of the mixing state, machine learning was used to successfully estimate the mixing state of powder and liquid in the device [12] [13].…”

Section: Introductionmentioning

confidence: 99%

Construction of Motion Mode Switching System by Machine Learning for Peristaltic Mixing Conveyor Based on Intestinal Movement

Terayama,

Oshino,

Nishihama

et al. 2024

IEEE Access

View full text Add to dashboard Cite

The high frequency of rocket launches requires low-cost solid rocket fuel. Currently, the fuel manufacturing process faces increased launch costs caused by the risk of ignition from rotary mixers and increased equipment and labor costs from batch processes in which mixing and conveying are separated. Therefore, this paper proposes and verifies an automatic switching system between mixing and conveying modes for a peristaltic mixing conveyor that enables safe and continuous mixing and conveying of solid fuel. In a previous study, peristaltic mixing conveyor with low shear force was developed and successfully produced solid fuel. However, there was room for improvement for more efficient fuel production because the device was controlled by pre-determined driving pattern. The actual intestine generates movement autonomously by enteric nerves. Therefore, the development of a sensing function that imitates the enteric nervous system and generates movement patterns based on the acquired data is expected to improve manufacturing efficiency. In this study, the sensor data of a mixed solid fuel simulant packaged in a bag were acquired, and the degree of mixing (unmixed and mixed completely) was discriminated using supervised learning (the k-nearest neighbor method). Furthermore, a system was constructed to continue the mixing mode when unmixing and automatically switch the motion to the conveying mode when the mixing was complete. The experiment showed that the motion mode automatically switched to the conveying mode at almost the same time as the labeled training data, and mixing and conveying of the simulated material was successfully performed.

show abstract

Skeletonizing the Dynamics of Soft Continuum Body from Video

et al. 2022

View full text Add to dashboard Cite

Soft continuum bodies have demonstrated their effectiveness in generating flexible and adaptive functionalities by capitalizing on the rich deformability of soft material. Compared with a rigid-body robot, it is in general difficult to model and emulate the morphology dynamics of a soft continuum body. In addition, a soft continuum body potentially has an infinite degree of freedom, requiring considerable labor to manually annotate its dynamics from external sensory data such as video. In this study, we propose a novel noninvasive framework for automatically extracting the skeletal dynamics from video of a soft continuum body and show the applications and effectiveness of our framework. First, we demonstrate that our framework can extract skeletal dynamics from animal videos, which can be effectively utilized for the analysis of soft continuum body including animal motion. Next, we focus on a soft continuum arm, a commonly used platform in soft robotics, and evaluate the potential information-processing capability. Normally, to control such a high-dimensional system, it is necessary to introduce many sensors to completely capture the motion dynamics, causing the deterioration of the material's softness. We illustrate that the evaluation of the memory capacity and sensory reconstruction error enables us to verify the minimum number of sensors sufficient for fully grasping the state dynamics, which is highly useful in designing a sensor arrangement for a soft robot. Also, we release the software developed in this study as open source for biology and soft robotics communities, which contributes to automating the annotation process required for the motion analysis of soft continuum bodies.

show abstract

Mixing State Estimation of Peristaltic Continuous Mixing Conveyor with Distributed Sensing System Based on Soft Intestine Motion

Cited by 9 publications

References 13 publications

Generalization Capability of Mixture Estimation Model for Peristaltic Continuous Mixing Conveyor

Generalization Capability of Mixture Estimation Model for Peristaltic Continuous Mixing Conveyor

Construction of Motion Mode Switching System by Machine Learning for Peristaltic Mixing Conveyor Based on Intestinal Movement

Skeletonizing the Dynamics of Soft Continuum Body from Video

Contact Info

Product

Resources

About