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Soft robots have recently drawn extensive attention thanks to their unique ability of adapting to complicated environments. Soft robots are designed in a variety of shapes of aiming for many different applications. However, accurate modelling and control of soft robots is still an open problem due to the complex robot structure and uncertain interaction with the environment. In fact, there is no unified framework for the modeling and control of generic soft robots. In this paper, we present a novel data-driven machine learning method for modeling a cable-driven soft robot. This machine learning algorithm, named deterministic learning (DL), uses soft robot motion data to train a radial basis function neural network (RBFNN). The soft robot motion dynamics are then guaranteed to be accurately identified, represented, and stored as an RBFNN model with converged constant neural network weights. To validate our method, We have built a simulated soft robot almost identical to our real inchworm soft robot, and we have tested the DL algorithm in simulation. Furthermore, a neural network weight combining technique is used which can extract and combine useful dynamics information from multiple robot motion trajectories.
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Drosophibot: a fruit fly inspired bio-robot
We introduce Drosophibot, a hexapod robot with legs designed based on the Common fruit fly, Drosophila melanogaster, built as a test platform for neural control development. The robot models anatomical aspects not present in other, similar bio-robots such as a retractable abdominal segment, insect-like dynamic scaling, and compliant feet segments in the hopes that more similar biomechanics will lead to more similar neural control and resulting behaviors. In increasing biomechanical modeling accuracy, we aim to gain further insight into the insect’s nervous system to inform the current model and subsequent neural controllers for legged robots.
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- Award ID(s):
- 1704436
- PAR ID:
- 10119320
- Date Published:
- Journal Name:
- 8th International Conference, Living Machines 2019
- Page Range / eLocation ID:
- 146-157
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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