Soft robots actuate themselves and their world through induced pressure and strain, and can often sense these quantities as well. We hypothesize that coordination in a tightly coupled collective of soft robots can be achieved with purely proprioceptive sensing and no direct communication. In this paper, we target a platform of soft pneumatic modules capable of sensing strain on their perimeter, with the goal of using only the robots' own soft actuators and sensors as a medium for distributed coordination. However, methods for modelling, sensing, and controlling strain in such soft robot collectives are not well understood. To address this challenge, we introduce and validate a computationally efficient spring-based model for two-dimensional sheets of soft pneumatic robots. We then translate a classical consensus algorithm to use only proprioceptive data, test in simulation, and show that due to the physical coupling between robots we can achieve consensus-like coordination. We discuss the unique challenges of strain sensors and next steps to bringing these findings to hardware. These findings have promising potential for smart materials and large-scale collectives, because they omit the need for additional communication infrastructure to support coordination.
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Electronic skins and machine learning for intelligent soft robots
Soft robots have garnered interest for real-world applications because of their intrinsic safety embedded at the material level. These robots use deformable materials capable of shape and behavioral changes and allow conformable physical contact for manipulation. Yet, with the introduction of soft and stretchable materials to robotic systems comes a myriad of challenges for sensor integration, including multimodal sensing capable of stretching, embedment of high-resolution but large-area sensor arrays, and sensor fusion with an increasing volume of data. This Review explores the emerging confluence of e-skins and machine learning, with a focus on how roboticists can combine recent developments from the two fields to build autonomous, deployable soft robots, integrated with capabilities for informative touch and proprioception to stand up to the challenges of real-world environments.
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- Award ID(s):
- 1830870
- PAR ID:
- 10197967
- Date Published:
- Journal Name:
- Science Robotics
- Volume:
- 5
- Issue:
- 41
- ISSN:
- 2470-9476
- Page Range / eLocation ID:
- eaaz9239
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1126/scirobotics.aaz9239
