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

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

doi:10.1109/icsens.2017.8234266

Cited by 1 publication

(1 citation statement)

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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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