Shape from sensors: Curve networks on surfaces from 3D orientations

Stanko, Tibor; Hahmann, Stéfanie; Bonneau, Georges-Pierre; Saguin-Sprynski, Nathalie

doi:10.1016/j.cag.2017.05.013

Cited by 10 publications

(7 citation statements)

References 32 publications

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“…Solutions in this regard could involve applying techniques from differential geometry to fuse rotation and strain data to generate smooth surface estimates. [100][101][102][103] In the work by Stanko et al, [100] a single algorithm was used to estimate the shape of objects as dissimilar as a mushroom, a chair, and a guitar. The only required input was distance estimates between successive orientation measurements.…”

Section: Shape Sensingmentioning

confidence: 99%

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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Section: Shape Sensingmentioning

confidence: 99%

Shape Changing Robots: Bioinspiration, Simulation, and Physical Realization

et al. 2020

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“…The final energy is minimized using Riemannian trust region algorithm [12]. e) Surfacing: The reconstructed curve network is surfaced using one of the existing methods that take into account the available surface normals along the curves [6].…”

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

confidence: 99%

“…Our main insight is to perform the filtering in the space of orientations and only integrate afterwards to get the positions. We apply the framework introduced in [6] in order to integrate the orientation data. 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.…”

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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“…An array of accelerometers attached to the measured object can provide local inclination information for global shape reconstruction. A number of studies have used this technique for measurement of human spine postures and 3D shape sensing of flexible materials [11][12][13][14][15]. The developments of microelectromechanical-systems (MEMS) technology in the last decade have produced many low cost, small size, light weight and readily available accelerometers.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Shape Measurement of an Inflatable Rubber Dam Using an Array of Inertial Measurement Units

Yan

Efstratiou

et al. 2021

IEEE Trans. Instrum. Meas.

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Shape measurement plays an important role in the condition monitoring and operation control of inflatable rubber dams. This paper presents a method to measure the cross-sectional shape of a rubber dam using an array of inertial measurement units (IMUs) placed on the circumference of the dam. Accelerometer and gyroscope measurements are combined using an adaptive complementary filter to determine the tangent angles of the dam circumference. The adaptive complementary filter adjusts the weights of the accelerometer and gyroscope measurements dynamically in order to reduce the uncertainty in orientation estimation due to external acceleration under dynamic conditions. A natural cubic spline that interpolates the measured tangent angles at discrete locations is used to represent the tangent angles along the dam circumference as a continuous function of the arc length. Finally, the cross-sectional shape is reconstructed by integrating the continuous tangent angle function along the circumference of the dam. Experimental assessment of the measurement system was performed on a purpose-built test rig using a digital camera as a reference measuring device. Results under a typical static condition show that the measured and reference shapes agree well with each other, with a similarity index of 3.74%, mismatch distance of the last IMU node being 12.3 mm and relative error of height measurement being -2.44%. Under dynamic conditions, the measurement results deteriorate due to external acceleration, but considerable improvement is achieved in comparison with an accelerometer-only approach. In addition, elimination of faulty nodes from shape reconstruction has negligible influence on the results, suggesting that the measurement system enjoys a high degree of fault tolerance.

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Shape from sensors: Curve networks on surfaces from 3D orientations

Cited by 10 publications

References 32 publications

Shape Changing Robots: Bioinspiration, Simulation, and Physical Realization

Shape Changing Robots: Bioinspiration, Simulation, and Physical Realization

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

Quantitative Shape Measurement of an Inflatable Rubber Dam Using an Array of Inertial Measurement Units

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