An Energetically-Autonomous Robotic Tadpole with Single Membrane Stomach and Tail

Philamore, Hemma; Rossiter, Jonathan; Ieropoulos, Ioannis

doi:10.1007/978-3-319-22979-9_37

Cited by 5 publications

(5 citation statements)

References 15 publications

(14 reference statements)

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“…Other examples of MFCs as power sources for soft robots include the use of ionic polymer metal composites (IPMCs) as both low-voltage soft robotic actuators and, in a novel application, as the ion exchange membrane of an MFC in an MFC-powered tadpole-inspired soft robot ( Philamore et al, 2015a ). This work demonstrated the potential to build miniature bio-powered soft robots by using multi-functional smart materials for power and actuation.…”

Section: Previous Workmentioning

confidence: 99%

Neural Networks Predicting Microbial Fuel Cells Output for Soft Robotics Applications

et al. 2021

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The development of biodegradable soft robotics requires an appropriate eco-friendly source of energy. The use of Microbial Fuel Cells (MFCs) is suggested as they can be designed completely from soft materials with little or no negative effects to the environment. Nonetheless, their responsiveness and functionality is not strictly defined as in other conventional technologies, i.e. lithium batteries. Consequently, the use of artificial intelligence methods in their control techniques is highly recommended. The use of neural networks, namely a nonlinear autoregressive network with exogenous inputs was employed to predict the electrical output of an MFC, given its previous outputs and feeding volumes. Thus, predicting MFC outputs as a time series, enables accurate determination of feeding intervals and quantities required for sustenance that can be incorporated in the behavioural repertoire of a soft robot.

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Section: Previous Workmentioning

confidence: 99%

Neural Networks Predicting Microbial Fuel Cells Output for Soft Robotics Applications

et al. 2021

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“…Figure 1A presents our concept for such a soft robotic system. While this level of integration is not yet possible, we demonstrate the fundamental components ( Figure 1B-D) that could be combined with developments in energy storage (55) and soft sensing, to create such a robot. The SMC is the mechanism which enables all of these components, and we believe a significant step towards the kind of integrated, autonomous, soft robot shown in Figure 1A.…”

Section: Introductionmentioning

confidence: 99%

“…Although this level of integration is not yet possible, we demonstrate the fundamental components (Fig. 1, B to D) that could be combined with developments in energy storage (68) and soft sensing to create such a robot. The SMC is the Fig.…”

Section: Introductionmentioning

confidence: 99%

A soft matter computer for soft robots

et al. 2019

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Despite the growing interest in soft robotics, little attention has been paid to the development of soft matter computational mechanisms. Embedding computation directly into soft materials is not only necessary for the next generation of fully soft robots but also for smart materials to move beyond stimulus-response relationships and toward the intelligent behaviors seen in biological systems. This article describes soft matter computers (SMCs), low-cost, and easily fabricated computational mechanisms for soft robots. The building block of an SMC is a conductive fluid receptor (CFR), which maps a fluidic input signal to an electrical output signal via electrodes embedded into a soft tube. SMCs could perform both analog and digital computation. The potential of SMCs is demonstrated by integrating them into three soft robots: (i) a Softworm robot was controlled by an SMC that generated the control signals necessary for three distinct gaits; (ii) a soft gripper was given a set of reflexes that could be programmed by adjusting the parameters of the CFR; and (iii) a two–degree of freedom bending actuator was switched between three distinct behaviors by varying only one input parameter. SMCs are a low-cost way to integrate computation directly into soft materials and an important step toward entirely soft autonomous robots.

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“…In evolutionary robotics, locomotion and object manipulation are among the most prominent objectives for robots (Vargas et al, 2014). Though the same principles in evolutionary robotics can be implemented for energy autonomy in robotic systems, energy autonomy is usually implemented on different robotic systems where the robot is able to utilize energy from light (Noth et al, 2006;Afarulrazi et al, 2011) or microbial fuel cells (Ieropoulos et al, 2003;Philamore et al, 2015). Being able to automatically design robotic systems that are geared toward energy autonomy could give us unintuitive solutions that might be more effective than traditional solutions.…”

Section: Introductionmentioning

confidence: 99%

“…Many evolutionary robotics experiments have focused on acquiring behavior typical of consumers (Sims, 1994a,b;Pfeifer and Bongard, 2006;Vargas et al, 2014), we instead look at how primary energy producers can evolve in artificial systems. Some robotic platforms have been designed to cope with energy autonomy Ieropoulos et al, 2003;Philamore et al, 2015) although research in this area is still limited. Our evolutionary system, instead of modeling nature, aims at implementing feasible evolved designs into real modular robots.…”

Section: Introductionmentioning

confidence: 99%

Toward Energy Autonomy in Heterogeneous Modular Plant-Inspired Robots through Artificial Evolution

et al. 2017

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An Energetically-Autonomous Robotic Tadpole with Single Membrane Stomach and Tail

Cited by 5 publications

References 15 publications

Neural Networks Predicting Microbial Fuel Cells Output for Soft Robotics Applications

Neural Networks Predicting Microbial Fuel Cells Output for Soft Robotics Applications

A soft matter computer for soft robots

Toward Energy Autonomy in Heterogeneous Modular Plant-Inspired Robots through Artificial Evolution

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