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Motivation
Having a complete picture of the ground interactions
of a legged robot is a necessity in enabling high-
speed and dynamic ground locomotion. Current
sensing methods are inadequate to address the
unique demands of robotic legged locomotion, and
are vulnerable to inertial noise upon high acceleration.
M=70 kg V=4.5 m/s
Using new design principles and methodologies, we
have developed a low cost, robust footpad sensor
designed for running robots. This approach maps the
local sampling of pressure inside a polymeric footpad
to forces in three axes using machine learning.
The foot sensor is a monolithic composite structure
that is composed of a piezoresistive sensor array PCB
completely embedded in a protective polyurethane
rubber layer. This composite architecture allows for
compliance and traction during ground contact, while
deformation alters the measured stress distribution.
Large normal forces of 424N are measured in the Z-
axis with a normalized RMSE of 1.2%, and for the
shear forces, the range is 233N with a normalized
RMSE of 10.1% in the Y-axis, and 219N with a
normalized RMSE of 8.3% in the X-axis.
Least Squares Artificial Neural Network (LSANN) is a
new approach to reduce the computational time for
convergence to obtain a useful estimator for normal
and shear forces by 29.2%. Another area of research
is material modelling and FEA simulation to better
inform sensor placement.
Design Principles Improvements
Approach Results
Currently force sensing shoes are being developed to
help assist the elderly and disabled for slip prediction,
fall prevention and mitigation. Athletes can also
benefit from the real-time in-situ force data collected to
better optimize their training workouts.
Future Directions
Stress Distribution
under Shear
Automated CNC
mill data collection
setup
2nd generation MIT
Cheetah footpad
MIT Cheetah jumping on
grass while untethered
MIT Biomimetic Robotics Lab website: biomimetics.mit.edu Michael Chuah: [email protected] [email protected] www.michaelchuah.me
