Ultrasonic piezoelectric transceiver sensor for full surface contact localization

An apparatus for classifying a region and an intensity of a contact, comprising:

• a plurality of piezoelectric transceivers provided on an inner surface of a bumper of the apparatus, the plurality of piezoelectric transceivers being attached apart from each other;
• a memory storing instructions;
• and
• one or more processors communicatively coupled to the memory;
• wherein the one or more processors are configured to execute the instructions to:
• determining an excitation frequency of an input signal that corresponds to an operating mode of the apparatus;
• generate mechanical vibrations on a portion of the apparatus by providing the input signal to each of the plurality of piezoelectric transceivers;
• obtain, from each of the plurality of piezoelectric transceivers, at least one acoustic signal, the at least one acoustic signal being based on an input the input signal provided to each of the plurality of piezoelectric transceivers;
• extract signal magnitudes corresponding to the at least one acoustic signal based on comparing each acoustic signal of the at least one acoustic signal from a corresponding piezoelectric transceiver of the plurality of piezoelectric transceivers with a reference acoustic signal, wherein the reference acoustic signal is generated by providing the input signal to a reference piezoelectric transceiver;
• determine, using a trained machine learning model that has been trained with acoustic signals and corresponding position and intensity information of impacts on a plurality of regions of an outer surface of the bumper, the region of the contact on the bumper with respect to the plurality of regions and the intensity of the contact, based on the signal magnitudes;
• and
• determine a motion of the apparatus based on the region of the contact, the intensity of the contact, and the operating mode of the apparatus.

A method for classifying a region and an intensity of a contact by an apparatus, comprising:

• determining an excitation frequency of an excitation signal that corresponds to an operating mode of the apparatus;
• generating mechanical vibrations on a portion of the apparatus by providing the excitation signal to each of a plurality of acoustic transceivers and to a reference acoustic transceiver, the plurality of acoustic transceivers being provided on an inner surface of a bumper of the apparatus, the plurality of acoustic transceivers attached apart from each other;
• obtaining, from each of the plurality of acoustic transceivers, at least one acoustic signal, the at least one acoustic signal being received by the plurality of acoustic transceivers while each of the plurality of acoustic transceivers transmit the excitation signal provided to each of the plurality of acoustic transceivers;
• extracting signal magnitudes corresponding to the at least one acoustic signal based on comparing each of the at least one acoustic signal from a corresponding acoustic transceiver of the plurality of acoustic transceivers based on a reference signal from the reference acoustic transceiver, wherein the reference signal is generated by the reference acoustic transceiver using the excitation signal;
• and
• estimating, using a trained machine learning model based on the signal magnitudes, the region of the contact from among a plurality of contact regions on an outer surface of the bumper and the intensity of the contact from among a plurality of force levels, the trained machine learning model having been trained with acoustic signals and corresponding position and intensity information of impacts on the plurality of contact regions on the outer surface of the bumper.

A method for classifying a region and an intensity of a contact by an apparatus, comprising:

• determining an excitation frequency of an input signal that corresponds to an operating mode of the apparatus;
• generating mechanical vibrations on a portion of the apparatus by providing the input signal to a first acoustic sensor provided on an inner surface of a bumper of the apparatus;
• receiving, from the first acoustic sensor, a combined acoustic signal comprising a first acoustic signal based on the input signal provided to the first acoustic sensor and one or more other acoustic signals based on the input signal provided to one or more other acoustic sensors adjacent to the first acoustic sensor;
• extracting signal magnitudes corresponding to the combined acoustic signal based on comparing the combined acoustic signal with a reference acoustic signal from a reference acoustic sensor, wherein the reference acoustic signal is generated by the reference acoustic sensor using the input signal;
• and
• determining, based on the signal magnitudes using a trained machine learning model that has been trained with acoustic signals and corresponding position and intensity information of impacts on a plurality of regions of an outer surface of the bumper, the region of the contact on the bumper with respect to the plurality of regions and the intensity of the contact.

A method for classifying a region and an intensity of a contact by an apparatus, comprising:

• determining an excitation frequency of an input signal that corresponds to an operating mode of the apparatus;
• generating mechanical vibrations on a portion of the apparatus by providing the input signal to each of a plurality of acoustic sensors, the plurality of acoustic sensors provided on an inner surface of a bumper of the apparatus, the plurality of acoustic sensors attached apart from each other;
• obtaining, from the plurality of acoustic sensors, at least one acoustic signal, the at least one acoustic signal being based on the input signal provided to each of the plurality of acoustic sensors;
• detecting a change in signal magnitudes corresponding to the at least one acoustic signal based on comparing each acoustic signal of the at least one acoustic signal from a corresponding acoustic sensor of the plurality of acoustic sensors with a reference acoustic signal from a reference acoustic sensor, wherein the reference acoustic signal is generated by the reference acoustic sensor using the input signal;
• and
• determining, based on the change exceeding a change threshold, and using a trained machine learning model that has been trained with acoustic signals and corresponding position and intensity information of impacts on a plurality of regions of an outer surface of the bumper, the region of the contact on the bumper with respect to the plurality of regions and the intensity of the contact, based on the signal magnitudes.