2 research outputs found
SLUGBOT, an Aplysia-inspired Robotic Grasper for Studying Control
Living systems can use a single periphery to perform a variety of tasks and
adapt to a dynamic environment. This multifunctionality is achieved through the
use of neural circuitry that adaptively controls the reconfigurable
musculature. Current robotic systems struggle to flexibly adapt to unstructured
environments. Through mimicry of the neuromechanical coupling seen in living
organisms, robotic systems could potentially achieve greater autonomy. The
tractable neuromechanics of the sea slug
feeding apparatus, or buccal mass, make it an ideal candidate for applying
neuromechanical principles to the control of a soft robot. In this work, a
robotic grasper was designed to mimic specific morphology of the
feeding apparatus. These include the use of soft actuators
akin to biological muscle, a deformable grasping surface, and a similar
muscular architecture. A previously developed Boolean neural controller was
then adapted for the control of this soft robotic system. The robot was capable
of qualitatively replicating swallowing behavior by cyclically ingesting a
plastic tube. The robot's normalized translational and rotational kinematics of
the odontophore followed profiles observed despite
morphological differences. This brings -inspired control
one step closer to multifunctional neural control schema
and . Future additions may improve
SLUGBOT's viability as a neuromechanical research platform.Comment: Submitted and accepted to Living Machines 2022 conferenc
A Bioinspired Synthetic Nervous System Controller for Pick-and-Place Manipulation
The Synthetic Nervous System (SNS) is a biologically inspired neural network
(NN). Due to its capability of capturing complex mechanisms underlying neural
computation, an SNS model is a candidate for building compact and interpretable
NN controllers for robots. Previous work on SNSs has focused on applying the
model to the control of legged robots and the design of functional subnetworks
(FSNs) to realize dynamical systems. However, the FSN approach has previously
relied on the analytical solution of the governing equations, which is
difficult for designing more complex NN controllers. Incorporating plasticity
into SNSs and using learning algorithms to tune the parameters offers a
promising solution for systematic design in this situation. In this paper, we
theoretically analyze the computational advantages of SNSs compared with other
classical artificial neural networks. We then use learning algorithms to
develop compact subnetworks for implementing addition, subtraction, division,
and multiplication. We also combine the learning-based methodology with a
bioinspired architecture to design an interpretable SNS for the pick-and-place
control of a simulated gantry system. Finally, we show that the SNS controller
is successfully transferred to a real-world robotic platform without further
tuning of the parameters, verifying the effectiveness of our approach.Comment: Accepted to ICRA 202