Acceleration and Magnetic Sensor Network for Shape Sensing

Hermanis, Atis; Cacurs, Ričards; Greitāns, Modris

doi:10.1109/jsen.2015.2496283

Cited by 34 publications

(36 citation statements)

References 19 publications

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“…The next step is to smooth the orientations and ensure the consistency of surface normals at intersections of curves. To this end we use a variant of smoothing splines on the manifold SO (3). Recall that a smoothing spline x : [t 0 , t N ] → R n for a set of points p i ∈ R n minimizes the energy…”

Section: C) Filtering By Regression On So(3)mentioning

confidence: 99%

“…Previous works on shape from sensors proposed devices with a fixed connectivity and known distances between sensor nodes: the sensors were organized uniformly on a 1D ribbon [1] or on a regular 2D grid [2,3]. Multiple ribbons were employed to acquire networks of curves on a surface, using a comb structure [4] or patches with boundary from geodesic curves [5].…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…Note that the proposed framework is not limited to the dynamic setup, and can also be used in combination with fixed-topology devices without modification. Possible applications are countless and include smart materials [5], shape digitization [6], medical scanning [3] and Structural Health Monitoring (SHM) [2].…”

Section: Introduction and Related Workmentioning

confidence: 99%

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Morphorider: Acquisition and reconstruction of 3D curves with mobile sensors

Stanko¹,

Saguin-Sprynski²,

Jouanet³

et al. 2017

2017 Ieee Sensors

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This paper introduces a new method for real-time shape sensing. Using a single inertial measurement unit (IMU), our method enables to scan physical objects and to reconstruct digital 3D models. By moving the IMU along the surface, a network of local orientation data is acquired together with traveled distances and network topology. We then reconstruct a consistent network of curves and fit these curves by a globally smooth surface. To demonstrate the feasibility of our approach, we have constructed a mobile device called the Morphorider, which is equipped with a 3A3M-sensor node and an odometer for distance tracking.

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Section: C) Filtering By Regression On So(3)mentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

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Morphorider: Acquisition and reconstruction of 3D curves with mobile sensors

Stanko¹,

Saguin-Sprynski²,

Jouanet³

et al. 2017

2017 Ieee Sensors

View full text Add to dashboard Cite

show abstract

“…We aim to reconstruct the galloping curves via tangential information with a length constraint using few attitude sensors, which do not need to know the spatial position of the conductor obtained using DGPS technology. There have been a few studies of shape sensing using attitude sensors, such as Morphosense (a flexible ribbon-like device) [25] and Human wearing vest (with integrated shape sensing sensor array) [26]. The author in [25] proposed a spatial interpolation with a Pythagorean-hodograph, which has better reconstruction performance than does the natural cubic spline method.…”

Section: Introductionmentioning

confidence: 99%

“…However, this method requires extensive iterative computations, which is not suitable for a real-time galloping monitoring system. Reference [26] presented a fast computation algorithm for the global shape reconstruction. Since the reconstruction precision is notably limited, many sensors must be used to ensure the reconstruction performance.…”

Section: Introductionmentioning

confidence: 99%

Full-Scale Reconstruction for Transmission Line Galloping Curves Based on Attitudes Sensors

Yang

et al. 2018

Mathematical Problems in Engineering

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Caused by strong winds or nonuniform icing, conductor galloping is one of the major hazards that should be monitored in a timely fashion. In this paper, we proposed a new full-scale reconstruction method for transmission line curves based on the attitude sensors that uses only the tangential information and arc-length constraint. Meanwhile, a comparatively low-complexity algorithm is presented: (1) quaternion rotation to generate the tangent vectors; (2) a modified B-spline global interpolation to evaluate the curvature information; and (3) a linear mapping method to correct reconstruction curves. Moreover, sensors placement method is put forward according to the galloping features. Finally, both simulation and experimental results demonstrate that the proposed method can accurately reconstruct the galloping curves in a relatively short time, which fulfills the requirement of the real-time galloping monitoring system.

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Shape Changing Robots: Bioinspiration, Simulation, and Physical Realization

et al. 2020

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are specialized for a single task, and cannot adapt their body to accomplish additional tasks after manufacture. Moreover, biological bodies are often highly regenerative, and able to repair and reconfigure their large-scale architecture in the face of significant damage or radical changes to their components. [3] For example, salamanders regenerate amputated limbs, [4] and fragments cut from arbitrary portions of planaria flatworms can rebuild (and rescale) their bodies to recover a full, correct anatomy. [3] Remarkably, many of these systems are able to retain information, such as learned memories, despite drastic reconfiguration or total replacement of their brains. [5] In these integrated living systems, intelligence, memory, learning, behavior, and body structure are all intertwined and emerge from the multiscale dynamics of the same robust and highly fault-tolerant medium.Evolution did not result in hard-coded body plans purely determined by genetic factors, but rather produced diverse examples of intelligent self-modifying systems which adapt to numerous extragenomic influences. [6] In this way, biology serves as an important proof-of-principle, and design challenge, for artificial intelligence and shape changing robots. Despite having access to this extensive set of model systems, the realization of general-purpose, adaptive robots has remained elusive. Researchers have proposed modular robots that can be attached to each other to expand functionality, [7] passively conforming universal grippers, [8] reconfigurable robotic skins, [9] self-assembling robot swarms, [10] gait-switching mechanisms [11] and controllers, [12,13] and algorithms that quickly re-adapt to multiple distinct tasks. [14] Such approaches succeed at adaptation but operate under the assumption that the robot's body is only reconfigured or reshaped due to external forces, and do not explore the possibility of synthetic machines that actively grow, regenerate, deform, or otherwise change the resting shape of their constituent components.With the introduction of a conformable gripper by Hirose in 1978, [15] followed by continuum robot arms, [16] silicone grippers, [17] and variable stiffness actuators, [18] robots that can adapt to real-world environments by changing their shape are becoming closer to reality. In particular, the idea of passively conforming around objects during grasping has been quite successful. [17,19,20] Soft robots have shown potential in other applications, including human-robot interaction and exploration, as reviewed by Kim et al., [21] Rus et al., [22] and others. [23,24] For a comprehensive review of the role of deformation in singlefunction soft robots, the reader is referred to Wang et al. [25] One of the key differentiators between biological and artificial systems is the dynamic plasticity of living tissues, enabling adaptation to different environmental conditions, tasks, or damage by reconfiguring physical structure and behavioral control policies. Lack of dynamic plasticity is a significant limitation for a...

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Acceleration and Magnetic Sensor Network for Shape Sensing

Cited by 34 publications

References 19 publications

Morphorider: Acquisition and reconstruction of 3D curves with mobile sensors

Morphorider: Acquisition and reconstruction of 3D curves with mobile sensors

Full-Scale Reconstruction for Transmission Line Galloping Curves Based on Attitudes Sensors

Shape Changing Robots: Bioinspiration, Simulation, and Physical Realization

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