Public Member Functions | |
def | __init__ |
def | debug |
def | output_voltage |
def | plot_fz_adc |
def | plot_pressure_z_adc |
def | plot_rtax_vout |
def | pressure2resistance |
def | pressure2resistance_2 |
def | pressure2resistance_3 |
def | rtax |
Public Attributes | |
adc_array | |
adc_bias | |
adc_bits | |
adc_plot | |
adc_range | |
contact_area | |
contact_area_percent | |
begin: parameters to be specified | |
fz_array | |
fz_max | |
fz_min | |
no_contact_area | |
pressure_array | |
pressure_max | |
r1 | |
r_min | |
r_min_percent_of_r_no_contact | |
r_no_contact | |
rtax_array | |
rtax_max | |
taxel_area | |
vdigi_array | |
vdigi_max | |
volts_per_adc_unit | |
vtot |
Attempts to model the digital signal that results from a normal force applied to a taxel. It assumes that the force is uniformly distributed over an area. The contact area is specified as a percentage of the taxel area.
Definition at line 25 of file tactile_sensor_model.py.
def hrl_fabric_based_tactile_sensor.tactile_sensor_model.TaxelModel.__init__ | ( | self, | |
contact_area_percent = 50.0 |
|||
) |
Definition at line 30 of file tactile_sensor_model.py.
print out many of the key member variables of the TaxelModel object
Definition at line 251 of file tactile_sensor_model.py.
given the resistance for the entire taxel, this returns the voltage across the taxel, which is what the analog to digital converter reads
Definition at line 88 of file tactile_sensor_model.py.
plot the curve relating applied normal force to the analog to digital converter output. this corresponds with the empirically generated scatter plots from a real taxel
Definition at line 225 of file tactile_sensor_model.py.
plot the curve relating applied normal force to the analog to digital converter output. this corresponds with the empirically generated scatter plots from a real taxel
Definition at line 233 of file tactile_sensor_model.py.
plot the curve relating the total taxel resistance and the voltage across the taxel, which corresponds to the voltage converted to a digital signal
Definition at line 242 of file tactile_sensor_model.py.
given an applied pressure, returns the resistivity of the contacted region of the taxel. this uses a simple linear model, where: 0 Pascals -> r_no_contact pressure max -> r_min Ohms
Definition at line 95 of file tactile_sensor_model.py.
def hrl_fabric_based_tactile_sensor.tactile_sensor_model.TaxelModel.pressure2resistance_2 | ( | self, | |
p | |||
) |
given an applied pressure, returns the resistivity of the contacted region of the taxel. this uses a logistic model, where: 0 Pascals -> r_no_contact pressure max -> r_min Ohms
Definition at line 112 of file tactile_sensor_model.py.
def hrl_fabric_based_tactile_sensor.tactile_sensor_model.TaxelModel.pressure2resistance_3 | ( | self, | |
p | |||
) |
given an applied pressure, returns the resistivity of the contacted region of the taxel. this was a quick attempt to use a model similar to "The Working Principle of Resistive Tactile Sensor Cells" by Karsten Weiss and Heinz Worn. It doesn't work, yet?
Definition at line 131 of file tactile_sensor_model.py.
given the pressure uniformly applied across the contact area this returns the resistance for the entire taxel. it essentially models the taxel as parallel resistors, where resistors in the contact area have a resistance dependent on the applied pressure, and resistors in the non-contact area have the maximum resistance. i started with a discrete model, and then made a continuous approximation, which appears to correspond with using two volumes in parallel with different resistivities. based on wikipedia, it looks like i've been using something called resistivity (rho). so, this model can use the equation r = rho*(length/area). if we assume length is constant, then this leads to r_contact = rho_contact/area_contact and the same for not_contact. assuming that they are in parallel. then R_total = 1/((area_contact/rho_contact) + (area_non_contact/rho_non_contact)). if we assume length changes, then we can make rho_contact = resistivity_contact * length_contact. this should be more carefully investigated, but it seems right... the biggest unknown is the function that converts pressure to resistivity. there are currently three models of this function in this code. fitting a parametric model to the data would be a good next step. probably use an optimizer like Nelder-Mead that only requires function evaluations and use a cost function that compares the fz->adc mapping to empirically collected data.
Definition at line 175 of file tactile_sensor_model.py.
Definition at line 33 of file tactile_sensor_model.py.
Definition at line 33 of file tactile_sensor_model.py.
Definition at line 32 of file tactile_sensor_model.py.
Definition at line 33 of file tactile_sensor_model.py.
Definition at line 33 of file tactile_sensor_model.py.
Definition at line 33 of file tactile_sensor_model.py.
begin: parameters to be specified
Definition at line 32 of file tactile_sensor_model.py.
Definition at line 33 of file tactile_sensor_model.py.
Definition at line 32 of file tactile_sensor_model.py.
Definition at line 32 of file tactile_sensor_model.py.
Definition at line 33 of file tactile_sensor_model.py.
Definition at line 33 of file tactile_sensor_model.py.
Definition at line 32 of file tactile_sensor_model.py.
Definition at line 32 of file tactile_sensor_model.py.
Definition at line 33 of file tactile_sensor_model.py.
Definition at line 32 of file tactile_sensor_model.py.
Definition at line 33 of file tactile_sensor_model.py.
Definition at line 33 of file tactile_sensor_model.py.
Definition at line 32 of file tactile_sensor_model.py.
Definition at line 32 of file tactile_sensor_model.py.
Definition at line 33 of file tactile_sensor_model.py.
Definition at line 33 of file tactile_sensor_model.py.
Definition at line 33 of file tactile_sensor_model.py.
Definition at line 32 of file tactile_sensor_model.py.