272 research outputs found
The cerebellum could solve the motor error problem through error increase prediction
We present a cerebellar architecture with two main characteristics. The first
one is that complex spikes respond to increases in sensory errors. The second
one is that cerebellar modules associate particular contexts where errors have
increased in the past with corrective commands that stop the increase in error.
We analyze our architecture formally and computationally for the case of
reaching in a 3D environment. In the case of motor control, we show that there
are synergies of this architecture with the Equilibrium-Point hypothesis,
leading to novel ways to solve the motor error problem. In particular, the
presence of desired equilibrium lengths for muscles provides a way to know when
the error is increasing, and which corrections to apply. In the context of
Threshold Control Theory and Perceptual Control Theory we show how to extend
our model so it implements anticipative corrections in cascade control systems
that span from muscle contractions to cognitive operations.Comment: 34 pages (without bibliography), 13 figure
Neuromorphic computing for attitude estimation onboard quadrotors
Compelling evidence has been given for the high energy efficiency and update
rates of neuromorphic processors, with performance beyond what standard Von
Neumann architectures can achieve. Such promising features could be
advantageous in critical embedded systems, especially in robotics. To date, the
constraints inherent in robots (e.g., size and weight, battery autonomy,
available sensors, computing resources, processing time, etc.), and
particularly in aerial vehicles, severely hamper the performance of
fully-autonomous on-board control, including sensor processing and state
estimation. In this work, we propose a spiking neural network (SNN) capable of
estimating the pitch and roll angles of a quadrotor in highly dynamic movements
from 6-degree of freedom Inertial Measurement Unit (IMU) data. With only 150
neurons and a limited training dataset obtained using a quadrotor in a real
world setup, the network shows competitive results as compared to
state-of-the-art, non-neuromorphic attitude estimators. The proposed
architecture was successfully tested on the Loihi neuromorphic processor
on-board a quadrotor to estimate the attitude when flying. Our results show the
robustness of neuromorphic attitude estimation and pave the way towards
energy-efficient, fully autonomous control of quadrotors with dedicated
neuromorphic computing systems
Using Distributed Wearable Sensors to Measure and Evaluate Human Lower Limb Motions
This paper presents a wearable sensor approach to motion measurements of human lower limbs, in which subjects perform specified walking trials at self-administered speeds so that their level walking and stair ascent capacity can be effectively evaluated. After an initial sensor alignment with the reduced error, quaternion is used to represent 3-D orientation and an optimized gradient descent algorithm is deployed to calculate the quaternion derivative. Sensors on the shank offer additional information to accurately determine the instances of both swing and stance phases. The Denavit-Hartenberg convention is used to set up the kinematic chains when the foot stays stationary on the ground, producing state constraints to minimize the estimation error of knee position. The reliability of this system, from the measurement point of view, has been validated by means of the results obtained from a commercial motion tracking system, namely, Vicon, on healthy subjects. The step size error and the position estimation accuracy change are studied. The experimental results demonstrated that the extensively existed sensor misplacement and sensor drift problems can be well solved. The proposed self-contained and environment-independent system is capable of providing consistent tracking of human lower limbs without significant drift
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