CN115876374B - Flexible touch structure of nose of robot dog and method for identifying soft and hard attributes of contact - Google Patents
Flexible touch structure of nose of robot dog and method for identifying soft and hard attributes of contact Download PDFInfo
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- CN115876374B CN115876374B CN202211726927.4A CN202211726927A CN115876374B CN 115876374 B CN115876374 B CN 115876374B CN 202211726927 A CN202211726927 A CN 202211726927A CN 115876374 B CN115876374 B CN 115876374B
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- 238000000034 method Methods 0.000 title claims abstract description 36
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 claims abstract description 49
- 239000000741 silica gel Substances 0.000 claims abstract description 49
- 229910002027 silica gel Inorganic materials 0.000 claims abstract description 49
- 239000000463 material Substances 0.000 claims abstract description 17
- 238000004088 simulation Methods 0.000 claims abstract description 7
- 238000012549 training Methods 0.000 claims description 32
- 238000010801 machine learning Methods 0.000 claims description 22
- 230000006399 behavior Effects 0.000 claims description 13
- 238000004422 calculation algorithm Methods 0.000 claims description 13
- 230000008569 process Effects 0.000 claims description 9
- 238000006073 displacement reaction Methods 0.000 claims description 7
- 238000012795 verification Methods 0.000 claims description 7
- 238000012360 testing method Methods 0.000 claims description 6
- 238000010276 construction Methods 0.000 claims description 3
- 241000282472 Canis lupus familiaris Species 0.000 description 38
- 230000006870 function Effects 0.000 description 8
- 238000010586 diagram Methods 0.000 description 5
- 238000004590 computer program Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000006467 substitution reaction Methods 0.000 description 3
- 238000003491 array Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 238000007635 classification algorithm Methods 0.000 description 1
- 238000002790 cross-validation Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000010030 laminating Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
- 239000013598 vector Substances 0.000 description 1
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Abstract
The invention discloses a nose flexible touch structure of a machine dog and a method for identifying soft and hard attributes of contacts, wherein the structure comprises the following steps: flexible silica gel, a sensor chip, a groove, a magnet, a simulation nose and a fixing material; the magnet is arranged in the flexible silica gel, and the first side surface of the flexible silica gel is attached to the foremost end, close to the nostril, in the simulation nose; the sensor chip is fixed on the groove and is attached to the second side surface of the flexible silica gel, and the second side surface is the opposite surface of the first side surface; when the contact object deforms the flexible silica gel and the position of the magnet in the flexible silica gel is changed, the sensor chip acquires corresponding sensor information, so that the softness of the contact object is identified; one end of the fixing material is connected with the outer edge of one end of the simulated nose far away from the nostril, and the other end of the fixing material is used for being connected with a body part of the machine dog; the touch recognition method can distinguish the hardness of the contact object in a touch manner, so that more proper behavior can be performed, and the touch recognition method can be widely applied to the field of touch recognition.
Description
Technical Field
The invention relates to the technical field of touch recognition, in particular to a soft touch structure of a nose of a machine dog and a method for recognizing soft and hard attributes of a contact.
Background
In daily life, a robot dog is often used in a household as an electronic pet or other functions, interacts with humans and the environment, changes its own behavior by recognizing the characteristics of nearby objects, and is an indispensable skill for a robot dog applied to a plurality of fields such as accompanying entertainment.
However, the existing machine dog mostly judges the nearby environment and executes actions only through the visual unit, but the machine dog has the following disadvantages: the hardness of surrounding objects cannot be distinguished by touching or the like as in real dogs, so that different behaviors can be performed.
Disclosure of Invention
In view of the above, the embodiment of the invention provides a soft and hard touch structure of a nose of a robot dog capable of identifying soft and hard attributes of a contact object and a method for identifying the soft and hard attributes of the contact object.
An aspect of an embodiment of the invention provides a soft touch structure for a nose of a machine dog, comprising: flexible silica gel, a sensor chip, a groove, a magnet, a simulation nose and a fixing material;
the magnet is arranged in the flexible silica gel, and the first side surface of the flexible silica gel is attached to the foremost end, close to nostrils, in the simulation nose; the sensor chip is fixed on the groove, and is attached to a second side face of the flexible silica gel, wherein the second side face is an opposite face of the first side face; one end of the fixing material is connected with the outer edge of one end of the simulated nose part far away from the nostril, and the other end of the fixing material is used for being connected with a body part of the machine dog;
the sensor chip is used for obtaining corresponding sensor information when the flexible silica gel is deformed by the contact object of the flexible tactile structure of the nose part of the robot dog and the position of the magnet in the flexible silica gel is changed, and the sensor information is used for determining the soft and hard properties of the contact object.
Preferably, the sensor chip includes a plurality of sensor chips, each of which is fixed to the groove.
Another aspect of the embodiments of the present invention provides a method for identifying soft and hard properties of a contact object, which is used for determining soft and hard properties of a contact object of a nose flexible tactile structure of a machine dog according to sensor information acquired by the nose flexible tactile structure of the machine dog, and the method includes:
reading sensor information acquired by a sensor chip, wherein the sensor information comprises deformation of flexible silica gel and displacement of a magnet in the flexible silica gel;
and inputting the sensor information into a machine learning model based on a KNN algorithm to obtain the soft and hard properties of the contact.
Preferably, the method further comprises:
and controlling the machine dog to execute preset behaviors matched with the soft and hard attributes.
Preferably, the training process of the machine learning model includes:
and training the machine learning model based on a KNN algorithm by taking training sensor information as a training sample and soft and hard attributes of a training contact object as a training label.
Preferably, the validation set and the test set in the machine learning model training process are partitioned using K-fold cross validation.
Preferably, the soft and hard properties of the contact include soft, medium and hard properties.
Another aspect of the embodiment of the present invention further provides a device for identifying soft and hard properties of a contact, including:
the sensor information reading unit is used for reading the sensor information acquired by the sensor chip, wherein the sensor information comprises the deformation amount of the flexible silica gel and the displacement amount of the magnet in the flexible silica gel;
and the soft and hard attribute identification unit is used for inputting the sensor information into a machine learning model based on a KNN algorithm to obtain the soft and hard attribute of the contact object.
Another aspect of the embodiment of the invention also provides an electronic device, which includes a processor and a memory;
the memory is used for storing programs;
the processor executes the program to implement the method described above.
Another aspect of the embodiments of the present invention also provides a computer-readable storage medium storing a program that is executed by a processor to implement the above-described method.
Embodiments of the present invention also disclose a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions may be read from a computer-readable storage medium by a processor of a computer device, and executed by the processor, to cause the computer device to perform the foregoing method.
The nose flexible tactile structure of the invention comprises: flexible silica gel, a sensor chip, a groove, a magnet, a simulation nose and a fixing material; the magnet is arranged in the flexible silica gel, and the first side surface of the flexible silica gel is attached to the foremost end, close to the nostril, in the simulated nose; the sensor chip is fixed on the groove and is attached to the second side surface of the flexible silica gel, and the second side surface is the opposite surface of the first side surface; when the soft touch structure of the nose of the robot dog is contacted with the contact object to deform the soft silica gel and change the position of the magnet in the soft silica gel, the sensor chip can acquire corresponding sensor information, and then the softness degree of the contact object is identified according to the sensor information; one end of the fixing material is connected with the outer edge of one end of the simulated nose far away from the nostril, and the other end of the fixing material is used for being connected with a body part of the machine dog; according to the invention, the hardness of the contact can be distinguished in a touch manner, so that the robot dog can be controlled to execute different behaviors according to different hardness, and more proper behavior actions can be made compared with the existing control behavior of the robot dog based on visual identification.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a soft touch structure of a nose of a robot dog according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a method for identifying soft and hard attributes of a contact according to an embodiment of the present invention;
FIG. 3 is an exemplary block diagram of a soft touch construction for a nose of a machine dog provided in an embodiment of the present invention;
fig. 4 is a block diagram of a device for identifying soft and hard properties of a contact according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, an embodiment of the present invention provides a soft touch construction for a nose of a machine dog, which may include: flexible silica gel, sensor chip, recess, magnet, emulation nose and fixed material.
Next, the structure of this structure will be described.
Specifically, the magnet may be disposed inside a flexible silica gel, and a first side of the flexible silica gel may be attached to a foremost end of the simulated nose, which is adjacent to the nostril; the sensor chip can be fixed on the groove, and can be attached to the second side surface of the flexible silica gel, wherein the second side surface is the opposite surface of the first side surface; one end of the securing material may be attached to the outer edge of the simulated nose portion at an end remote from the nostril and the other end of the securing material may be used to attach to a body part of the robot dog.
When the soft touch structure of the nose of the robot dog is contacted with the contact object to enable the soft silica gel to deform, and the position of the magnet in the soft silica gel is changed, the sensor chip can acquire corresponding sensor information, and then the control module of the robot dog can determine the soft and hard properties of the contact object according to the sensor information.
Further, in order to obtain more accurate sensor information, the sensor chip of the soft touch structure of the nose of the robot dog of the invention can comprise a plurality of sensor chips, and all the sensor chips can be fixed on the grooves.
Referring to fig. 2, an embodiment of the present invention provides a method for identifying soft and hard properties of a contact object, which is used for determining soft and hard properties of a contact object of a soft and soft tactile structure of a nose of a robot dog according to sensor information acquired by the soft and soft tactile structure of the nose of the robot dog, and the method may include:
step S100: and reading sensor information acquired by the sensor chip, wherein the sensor information comprises deformation of the flexible silica gel and displacement of the magnet in the flexible silica gel.
Specifically, after the flexible touch structure of the nose of the machine dog contacts with the contact object, the contact object enables the flexible silica gel to deform, and the magnet in the flexible silica gel is displaced due to the deformation of the flexible silica gel, the sensor chip can acquire the deformation amount of the flexible silica gel and the displacement amount of the magnet as sensor information, and then the control module of the machine dog can read the sensor information.
Step S110: and inputting the sensor information into a machine learning model based on a KNN algorithm to obtain the soft and hard properties of the contact.
Specifically, the machine learning model is obtained based on training of the KNN algorithm, and the soft and hard attributes of the contact object can be identified according to the input sensor information, and in an alternative embodiment, the soft and hard attributes of the contact object in the invention can include: soft, medium, and hard attributes.
Next, a training process of the machine learning model is described, which may specifically include:
and training the machine learning model based on a KNN algorithm by taking training sensor information as a training sample and soft and hard attributes of a training contact object as a training label.
Specifically, KNN is k nearest algorithms, and is input as a training data set composed of feature vectors and instance categories, and in the embodiment of the present invention, is sensor information and hardness labels of corresponding contacts thereof. The training process comprises the following specific steps:
(1) An appropriate parameter k is selected.
(2) Distances between the test data and other data are selected, the relationship of the distances is calculated, and the sorting is performed.
(3) K points with the smallest distance are selected.
(4) The frequency of occurrence of the category to which the first k points belong is determined.
(5) The highest frequency class of the first k points is returned as the classification.
In the embodiment of the invention, a knn module in the sklearn packet can be called as a classification algorithm during training. In addition, the embodiment of the invention can divide the verification set and the test set in the training process of the machine learning model by using K-fold cross verification, and particularly, the most suitable K value can be selected by adopting a five-fold cross verification algorithm: dividing the whole data set into five parts, taking four parts of the data set as training sets each time, taking the rest part as test sets, and taking the k value corresponding to the highest average accuracy value.
Further, after the soft and hard properties of the contact are identified, the embodiment of the invention may further include the following processes:
and controlling the machine dog to execute preset behaviors matched with the soft and hard attributes.
Specifically, the robot dog may preset a plurality of different behaviors, and after the contact soft and hard attributes are identified, the control module of the robot dog may control the robot dog to execute the behavior matched with the soft and hard attributes.
Next, practical application of the present invention will be described with specific examples.
Referring to fig. 3, an exemplary block diagram of a soft touch structure for a nose of a machine dog is provided in an embodiment of the present invention.
A machine dog nose flexible tactile structure of the present invention may resemble a real dog nose shape, and the example structure of fig. 3 may include: a module 1 formed by a sensor chip and a groove, and a module 2 formed by flexible silica gel and a magnet. The behavior control system of the machine dog may include: the device comprises a data receiving module, a data input module, a machine learning module and a control module.
Specifically, the sensor chip is located emulation nose portion, is fixed in on the recess, and with flexible silica gel surface contact laminating, magnet is located flexible silica gel. When the example structure of the invention contacts with a contact object in the surrounding environment with a certain force, the data receiving module can read the sensor information acquired by the sensor chip and then transmit the sensor information into the data input module, the output end of the data input module is connected with the machine learning module so that the machine learning module can analyze the behavior by utilizing the sensor information, the machine learning module can further recognize the specific soft and hard properties of the contact object, the recognition result is input into the control module, and the control module controls the behavior of the machine dog according to the recognition result.
Referring to fig. 4, an embodiment of the present invention provides a device for identifying soft and hard properties of a contact, including:
the sensor information reading unit is used for reading the sensor information acquired by the sensor chip, wherein the sensor information comprises the deformation amount of the flexible silica gel and the displacement amount of the magnet in the flexible silica gel;
and the soft and hard attribute identification unit is used for inputting the sensor information into a machine learning model based on a KNN algorithm to obtain the soft and hard attribute of the contact object.
Embodiments of the present invention also disclose a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions may be read from a computer-readable storage medium by a processor of a computer device, and executed by the processor, to cause the computer device to perform the method shown in fig. 2.
In some alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flowcharts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed, and in which sub-operations described as part of a larger operation are performed independently.
Furthermore, while the invention is described in the context of functional modules, it should be appreciated that, unless otherwise indicated, one or more of the described functions and/or features may be integrated in a single physical device and/or software module or one or more functions and/or features may be implemented in separate physical devices or software modules. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary to an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be apparent to those skilled in the art from consideration of their attributes, functions and internal relationships. Accordingly, one of ordinary skill in the art can implement the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative and are not intended to be limiting upon the scope of the invention, which is to be defined in the appended claims and their full scope of equivalents.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiment of the present invention has been described in detail, the present invention is not limited to the embodiments described above, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and these equivalent modifications or substitutions are included in the scope of the present invention as defined in the appended claims.
Claims (7)
1. A soft touch construction for a nose of a machine dog, comprising: flexible silica gel, a sensor chip, a groove, a magnet, a simulation nose and a fixing material;
the magnet is arranged in the flexible silica gel, and the first side surface of the flexible silica gel is attached to the foremost end, close to nostrils, in the simulation nose; the sensor chip is fixed on the groove, and is attached to a second side face of the flexible silica gel, wherein the second side face is an opposite face of the first side face; one end of the fixing material is connected with the outer edge of one end of the simulated nose part far away from the nostril, and the other end of the fixing material is used for being connected with a body part of the machine dog;
the sensor chip is used for acquiring corresponding sensor information when the flexible silica gel is deformed by a contact object of the flexible tactile structure of the nose part of the robot dog and the position of a magnet in the flexible silica gel is changed, and the sensor information is used for determining the soft and hard properties of the contact object;
the sensor chip comprises a plurality of sensor chips, and each sensor chip is fixed on the groove.
2. A method for identifying soft and hard properties of a contact, characterized in that the method is used for determining the soft and hard properties of the contact of the soft and hard tactile structure of the nose of a machine dog according to sensor information acquired by the soft and soft tactile structure of the nose of the machine dog according to claim 1, and comprises the following steps:
reading sensor information acquired by a sensor chip, wherein the sensor information comprises deformation of flexible silica gel and displacement of a magnet in the flexible silica gel;
inputting the sensor information into a machine learning model based on a KNN algorithm to obtain the soft and hard properties of the contact object;
the training process of the machine learning model comprises the following steps:
training the machine learning model based on a KNN algorithm by taking training sensor information as a training sample and soft and hard attributes of a training contact as a training label;
and dividing a verification set and a test set in the training process of the machine learning model by using K-fold cross verification.
3. The method for identifying the soft and hard attributes of a contact according to claim 2, further comprising:
and controlling the machine dog to execute preset behaviors matched with the soft and hard attributes.
4. A method for identifying the soft and hard properties of a contact according to claim 2 or 3, wherein the soft and hard properties of the contact include soft, medium and hard properties.
5. A contact soft and hard attribute identification device for determining soft and hard attributes of a contact of a soft touch structure of a machine dog nose from sensor information acquired by the soft touch structure of the machine dog nose as claimed in claim 1, the device comprising:
the sensor information reading unit is used for reading the sensor information acquired by the sensor chip, wherein the sensor information comprises the deformation amount of the flexible silica gel and the displacement amount of the magnet in the flexible silica gel;
the soft and hard attribute identification unit is used for inputting the sensor information into a machine learning model based on a KNN algorithm to obtain soft and hard attributes of the contact object;
the device is also used for training the machine learning model based on a KNN algorithm by taking training sensor information as a training sample and taking the soft and hard attributes of a training contact as a training label; and dividing a verification set and a test set in the training process of the machine learning model by using K-fold cross verification.
6. An electronic device comprising a processor and a memory;
the memory is used for storing programs;
the processor executing the program implements the method of any one of claims 2 to 4.
7. A computer-readable storage medium, characterized in that the storage medium stores a program that is executed by a processor to implement the method of any one of claims 2 to 4.
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