A Boundary-Constrained Swarm Robot with Granular Jamming

Karimi, Mohammad; Alizadehyazdi, Vahid; Busque, Bruno-Pier; Jaeger, Heinrich M.; Spenko, Matthew

doi:10.1109/robosoft48309.2020.9115996

Cited by 15 publications

(9 citation statements)

References 26 publications

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“…The spread of membrane materials is listed in Table 5 below: [35,47] The most ubiquitous membrane materials are elastomers, specifically silicone and latex rubbers, which are also the most commonly used materials in other soft robotic grippers [2]. The only commercialised gripper used a custom polychloroprene to improve durability over more standard membrane materials.…”

Section: Membranesmentioning

confidence: 99%

“…Surgical tools tend towards long and slender tubular cylinders, utilizing jamming for stiffening and force generation. Recent developments have incorporated locomotive swarm robotics into tori membranes [34,35], or designed bioinspired human fingers through hybridizations of grains and layers or fibres, mimicking muscles, joints and bones [44,45].…”

Section: Membranesmentioning

confidence: 99%

“…They used their locomotive gripper to grasp cylinder objects and pulled them in a straight line. Karimi et al [35] also designed a locomotive swarm gripper with a torus-shaped membrane, however, they exploited vacuum pressure to enact jamming. Their grasping strategy was bioinspired from ants, whereby a sub-robot approached a target object and acted as a center of rotation for the aggregate swarm to engulf the object.…”

Section: Grippersmentioning

confidence: 99%

“…Figure 2.A diverse array of jamming actuated soft robots designed for unique environments and applications. From left to right, top to bottom: (a) a locomotive, humanoid, rigid robot using granular jamming paws to vertically ascend a rigid shaft via exploiting the jamming transition to increase compliance and friction against irregularly shaped walls [60], (b) a wormbot that crawls via bending moments created by combinations of McKibben actuation and jamming cells[5], (c) an ant-bioinspired granular jamming swarm gripper comprising of independently controllable sub-robots capable of surrounding a target object, jamming to grasp it and transporting it via wheeled leg locomotion[35], (d) a prostheses device incorporating a granular jamming bag gripper which was designed to assist amputees with daily living tasks including object grasping and manipulation[18], (e) a rigid four-legged locomotive robot utilizing granular paws with rubber cube packings with goals to increase damping and terrain compliance, and reduce slippage, during locomotion on smooth and rough surfaces[59], and (f) a remotely operated underwater vehicle capable of grasping and manipulating delicate deep-sea objects through a hydraulically actuated granular jamming gripper which engenders stronger grasps as environmental pressure increases[28].…”

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confidence: 99%

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A Review of Jamming Actuation in Soft Robotics

2020

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Jamming is a popular and versatile soft robotic mechanism, enabling new systems to be developed that can achieve high stiffness variation with minimal volume variation. Numerous applications have been reported, including deep-sea sampling, industrial gripping, and use as paws for legged locomotion. This review explores the state-of-the-art for the three classes of jamming actuator: granular, layer and fibre jamming. We highlight the strengths and weaknesses of these soft robotic systems and propose opportunities for further development. We describe a number of trends, promising avenues for innovative research, and several technology gaps that could push the field forwards if addressed, including the lack of standardization for evaluating the performance of jamming systems. We conclude with perspectives for future studies in soft jamming robotics research, particularly elucidating how emerging technologies, including multi-material 3D printing, can enable the design and creation of increasingly diverse and high-performance soft robotic mechanisms for a myriad of new application areas.

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Section: Membranesmentioning

confidence: 99%

Section: Membranesmentioning

confidence: 99%

Section: Grippersmentioning

confidence: 99%

mentioning

confidence: 99%

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A Review of Jamming Actuation in Soft Robotics

2020

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“…the change in nematic texture due to the motion of topological defects, also suggest the pos-sibility that more complex functional goals will require counter-intuitive solutions. Similar considerations might also apply to geometrical constraints introduced by confining active matter within deformable containers [39][40][41][42], or attempting to manipulate an active container filled with passive matter [43][44][45]. We therefore propose that reinforcement learning provides an appealing, model-free method for generating intuition and functionalizing the effects of localized activity in systems hosting topological excitations or otherwise complex dynamics.…”

mentioning

confidence: 99%

Learning to Control Active Matter

Falk,

Alizadehyazdi,

Jaeger

et al. 2021

Preprint

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The study of active matter has revealed novel non-equilibrium collective behaviors, illustrating their potential as a new materials platform. However, most works treat active matter as unregulated systems with uniform microscopic energy input, which we refer to as activity. In contrast, functionality in biological materials results from regulating and controlling activity locally over space and time, as has only recently become experimentally possible for engineered active matter. Designing functionality requires navigation of the high dimensional space of spatio-temporal activity patterns, but brute force approaches are unlikely to be successful without system-specific intuition. Here, we apply reinforcement learning to the task of inducing net transport in a specific direction for a simulated system of Vicsek-like self-propelled disks using a spotlight that increases activity locally. The resulting time-varying patterns of activity learned exploit the distinct physics of the strong and weak coupling regimes. Our work shows how reinforcement learning can reveal physically interpretable protocols for controlling collective behavior in non-equilibrium systems.

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