83 research outputs found
Slime mould tactile sensor
Slime mould P. polycephalum is a single cells visible by unaided eye. The
cells shows a wide spectrum of intelligent behaviour. By interpreting the
behaviour in terms of computation one can make a slime mould based computing
device. The Physarum computers are capable to solve a range of tasks of
computational geometry, optimisation and logic. Physarum computers designed so
far lack of localised inputs. Commonly used inputs --- illumination and
chemo-attractants and -repellents --- usually act on extended domains of the
slime mould's body. Aiming to design massive-parallel tactile inputs for slime
mould computers we analyse a temporal dynamic of P. polycephalum's electrical
response to tactile stimulation. In experimental laboratory studies we discover
how the Physarum responds to application and removal of a local mechanical
pressure with electrical potential impulses and changes in its electrical
potential oscillation patterns
A Model that Predicts the Material Recognition Performance of Thermal Tactile Sensing
Tactile sensing can enable a robot to infer properties of its surroundings,
such as the material of an object. Heat transfer based sensing can be used for
material recognition due to differences in the thermal properties of materials.
While data-driven methods have shown promise for this recognition problem, many
factors can influence performance, including sensor noise, the initial
temperatures of the sensor and the object, the thermal effusivities of the
materials, and the duration of contact. We present a physics-based mathematical
model that predicts material recognition performance given these factors. Our
model uses semi-infinite solids and a statistical method to calculate an F1
score for the binary material recognition. We evaluated our method using
simulated contact with 69 materials and data collected by a real robot with 12
materials. Our model predicted the material recognition performance of support
vector machine (SVM) with 96% accuracy for the simulated data, with 92%
accuracy for real-world data with constant initial sensor temperatures, and
with 91% accuracy for real-world data with varied initial sensor temperatures.
Using our model, we also provide insight into the roles of various factors on
recognition performance, such as the temperature difference between the sensor
and the object. Overall, our results suggest that our model could be used to
help design better thermal sensors for robots and enable robots to use them
more effectively.Comment: This article is currently under review for possible publicatio
Learning Sensor Feedback Models from Demonstrations via Phase-Modulated Neural Networks
In order to robustly execute a task under environmental uncertainty, a robot
needs to be able to reactively adapt to changes arising in its environment. The
environment changes are usually reflected in deviation from expected sensory
traces. These deviations in sensory traces can be used to drive the motion
adaptation, and for this purpose, a feedback model is required. The feedback
model maps the deviations in sensory traces to the motion plan adaptation. In
this paper, we develop a general data-driven framework for learning a feedback
model from demonstrations. We utilize a variant of a radial basis function
network structure --with movement phases as kernel centers-- which can
generally be applied to represent any feedback models for movement primitives.
To demonstrate the effectiveness of our framework, we test it on the task of
scraping on a tilt board. In this task, we are learning a reactive policy in
the form of orientation adaptation, based on deviations of tactile sensor
traces. As a proof of concept of our method, we provide evaluations on an
anthropomorphic robot. A video demonstrating our approach and its results can
be seen in https://youtu.be/7Dx5imy1KcwComment: 8 pages, accepted to be published at the International Conference on
Robotics and Automation (ICRA) 201
Learning Latent Space Dynamics for Tactile Servoing
To achieve a dexterous robotic manipulation, we need to endow our robot with
tactile feedback capability, i.e. the ability to drive action based on tactile
sensing. In this paper, we specifically address the challenge of tactile
servoing, i.e. given the current tactile sensing and a target/goal tactile
sensing --memorized from a successful task execution in the past-- what is the
action that will bring the current tactile sensing to move closer towards the
target tactile sensing at the next time step. We develop a data-driven approach
to acquire a dynamics model for tactile servoing by learning from
demonstration. Moreover, our method represents the tactile sensing information
as to lie on a surface --or a 2D manifold-- and perform a manifold learning,
making it applicable to any tactile skin geometry. We evaluate our method on a
contact point tracking task using a robot equipped with a tactile finger. A
video demonstrating our approach can be seen in https://youtu.be/0QK0-Vx7WkIComment: Accepted to be published at the International Conference on Robotics
and Automation (ICRA) 2019. The final version for publication at ICRA 2019 is
7 pages (i.e. 6 pages of technical content (including text, figures, tables,
acknowledgement, etc.) and 1 page of the Bibliography/References), while this
arXiv version is 8 pages (added Appendix and some extra details
FLECTILE: 3D-printable soft actuators for wearable computing
Rapid prototyping and fast manufacturing processes are critical drivers for implementing wearable devices. This paper shows an exemplary method for building flexible, fully elastomeric, vibrotactile electromagnetic actuators based on the Lorentz force law. This paper also introduces the design parameters required for well-functioning actuators and studies the properties of such actuators. The crucial element of the actuator is a helical planer coil manufactured from "capillary" silver TPU (Thermoplastic polyurethane), an ultra-stretchable conductor. This paper leverages the novel material to manufacture soft vibration actuators in fewer and simpler steps than previous approaches. Best practices and procedures for building a wearable actuator are reported. We show that the dimension of the actuators are easily configurable and can be printed in batch-size-one using 3D printing. Actuators can be attached directly to the skin as all the components of FLECTILE are made from biocompatible polymers. Tests on the driving properties have confirmed that the actuator could reach a broad scope of frequency up to 200 Hz with a small voltage (5 V) required. A user study showed that vibrations of the actuator are well perceivable by six study participants under an observing, hovering, and resting condition
Design and Analysis of Mobile Locomation Approach
One of the most difficult tasks for a robotic system is to determine the best path through the workspace. The main purpose is to prevent obstructions and create an optimum path. As a result, a mobile robot's free configuration space must be managed very carefully for course planning and navigation. The path planning work will be easier, faster, and more efficient if the configuration space is partitioned. In addition, the data perceived by the sensor contains some noise. As a result, we construct an approach to produce an optimal prediction state in order to build a map that aids in the effective management of the environment in order to locate the most efficient paths to target. We use the modified Kalman Filter (MKF) to determine the most reliable sensor data prediction, and then the K-means clustering method to identify the subsequent landmarks while evading barriers
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