ElectroVoxel: Electromagnetically Actuated Pivoting for Scalable Modular Self-Reconfigurable Robots

Nisser, Martin; Cheng, Leon; Makaram, Yashaswini; Suzuki, Ryo; Mueller, Stefanie

doi:10.1109/icra46639.2022.9811746

Cited by 22 publications

(9 citation statements)

References 19 publications

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“…This controller may take one action at each time step, i.e., one every 1 60 s, with a control frequency f c = 60 Hz. Human-based controllers would hardly be able to keep this pace.…”

Section: ) Ai-based Controllermentioning

confidence: 99%

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Human Control of Simulated Modular Soft Robots May Predict the Performance of Optimized AI-Based Controllers

Pietrosanti,

Nadizar,

Pigozzi

et al. 2023

IEEE Access

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Robots with a modular body permit a wide range of physical configurations, which can be obtained by arranging the composing modules differently. While this freedom makes modular robots capable of performing different tasks, finding the optimal physical configuration for a given task is not trivial. In fact, practitioners attempt to jointly optimize the body and the controller of the robot for a given task, but the result is not always satisfactory. More broadly, it is not clear what factors make a physical configuration more or less successful. In this paper, we aim to fill this gap and verify if humans can be predictive with respect to the performance of an optimized controller for a given robot body. We consider the case of Voxelbased Soft Robots (VSRs), whose rich dynamic induced by the softness of the modules makes the body particularly relevant for the robot ability to perform a task. We instantiate a number of (simulated) VSR bodies, differing in shape and actuation mechanism, and let a panel of humans control them, by means of online interaction with the simulator, while performing the task of locomotion. We use the same bodies with controllers obtained with evolutionary optimization, for the same task. We compare the ranking of human-and optimized AI-based VSRs, finding them very similar. We believe that our results strengthen the hypothesis that intrinsic factors in the body of modular robots determine their success.

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“…This controller may take one action at each time step, i.e., one every 1 60 s, with a control frequency f c = 60 Hz. Human-based controllers would hardly be able to keep this pace.…”

Section: ) Ai-based Controllermentioning

confidence: 99%

“…R ECONFIGURABILITY plays a key role in the road towards robots that are more adaptable in terms of task [1] and environment [2]. In this regard, modular robots [3] are a relevant family of robots.…”

Section: Introductionmentioning

confidence: 99%

Human Control of Simulated Modular Soft Robots May Predict the Performance of Optimized AI-Based Controllers

Pietrosanti,

Nadizar,

Pigozzi

et al. 2023

IEEE Access

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“…Active self-assembly by modular self-reconfigurable robots (MSRR) involves modules that modulate their behavior online in order to locate, position and bond themselves to their neighbors, for which each module requires embedding with computation, sensing and actuation. Because most systems choose cubic modules to facilitate tessellation, a key discriminator between systems is often the actuation, which include electropermanent magnets [6], electromagnets [7], [8], momentum wheels [9], [10], and COTs servos or motors [11]. While active assembly has been used to successfully reconfigure a variety of robotic systems, it is these actuators that are typically the most significant challenge to scaling systems up in number and down in size due to the cost and complexity of embedding them into individual modules [12].…”

Section: Introductionmentioning

confidence: 99%

Selective Self-Assembly using Re-Programmable Magnetic Pixels

Nisser¹,

Makaram²,

Faruqi³

et al. 2022

Preprint

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This paper introduces a method to generate highly selective encodings that can be magnetically "programmed" onto physical modules to enable them to self-assemble in chosen configurations. We generate these encodings based on Hadamard matrices, and show how to design the faces of modules to be maximally attractive to their intended mate, while remaining maximally agnostic to other faces. We derive guarantees on these bounds, and verify their attraction and agnosticism experimentally. Using cubic modules whose faces have been covered in soft magnetic material, we show how inexpensive, passive modules with planar faces can be used to selectively selfassemble into target shapes without geometric guides. We show that these modules can be easily re-programmed for new target shapes using a CNC-based magnetic plotter, and demonstrate self-assembly of 8 cubes in a water tank.

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“…Magnetic materials, however, still remain far behind this digital fabrication revolution [11] despite the fact that magnetic materials hold great promise for interactive applications. For example, researchers used magnets to create novel tangible interfaces (MechaMagnets [46]), to produce custom tactile sensations (MagneLayer [42]), to guide the assembly of 3D objects (DynaBlock [36], ElectroVoxel [21], Stochastic Self-assembly [20]), and to create actuated interfaces (Programmable Polarities [24]). However, all of these works either use off-the-shelf magnets (MechaMagnets [46], DynaBlock [36]) or require manual construction of the customized magnetic materials (FluxPaper [26], MagneLayer [42]).…”

Section: Introductionmentioning

confidence: 99%

Mixels: Fabricating Interfaces using Programmable Magnetic Pixels

Nisser¹,

Makaram²,

Covarrubias³

et al. 2022

Preprint

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Fabrication PipelineMagnetic Pixels Application Use Cases a b cFigure 1: Mixels are interfaces made from programmable magnetic pixels. Users can create Mixels using our custom design and fabrication pipeline: (a) an inexpensive hardware add-on consisting of an electromagnet and hall effect sensor clipped on to a 3-axis CNC can both write and read magnetic pixels, (b) the resulting magnetic interfaces can be programmed to attract and repel only in specific configurations and are otherwise agnostic, (c) this enables a variety of applications, ranging from selectively paired yet visually identical objects, to haptics and guided assembly.

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ElectroVoxel: Electromagnetically Actuated Pivoting for Scalable Modular Self-Reconfigurable Robots

Cited by 22 publications

References 19 publications

Human Control of Simulated Modular Soft Robots May Predict the Performance of Optimized AI-Based Controllers

Human Control of Simulated Modular Soft Robots May Predict the Performance of Optimized AI-Based Controllers

Selective Self-Assembly using Re-Programmable Magnetic Pixels

Mixels: Fabricating Interfaces using Programmable Magnetic Pixels

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