Mechanical hysteria creation, robotic damage detection, and categorization system

Abstract

Robots or robotic limbs, unlike humans, cannot sense their environment through a body-wide system similar to the subcutaneous nervous systems. They do not possess the ability to respond sensitively to external factors. As a result, when their bodies come into contact with other objects or suffer damage, they cannot sense it through a nervous system and make decisions accordingly. Furthermore, they cannot feel the pressure they apply to objects or calculate the physical energy transmitted by objects to them. This leads to limited awareness of environmental effects and not control over the condition of their own bodies. Consequently, when they are damaged, they may encounter problems in their tasks because they are unable to measure the extent of the damage, potentially leading to failure in their duties or unexpected issues causing time delays. The invention relates to a mechanical hysteria creation, robotic damage detection, and categorization system that provides support for autonomous damage detection and replacement of damaged parts in such cases.

Description

[0001] DESCRIPTION

[0002] MECHANICAL HYSTERIA CREATION, ROBOTIC DAMAGE DETECTION, AND CATEGORIZATION SYSTEM

[0003] TECHNICAL FIELD

[0004] The system can be used in all industrial sectors where robotic technologies are employed, including Robotic Nerve Systems, Mechanical Neural Networks, Industrial and Humanoid Robot Systems, the Military Combat Robots Industry, and the Civil Autonomous Robot Industry. In this context, the integration of the system into industrial robots will enable the robot to detect damages that may occur during the manufacturing process and establish a system capable of repairing this damage according to its severity.

[0005] STATE OF THE ART

[0006] The system with publication number WO2016168117A2 (Source 1 ) titled “Wearable Electric Multi-Sensory Human / Machine Human / Human Interfaces” can detect objects that come into contact with the body by placing sensors on the robot and human body. Additionally, haptic sensors have been utilized in the study presented in this patent application. That is, a network of thousands of sensors is positioned in a mesh shape around the user’s body. In the network created by thousands of sensors, when a sensor bundle is touched, a warning occurs, and the system responds accordingly. In the system from Source 5, the data from visual and auditory systems work together by integrating with the haptic sensor system.

[0007] While in the technique described in Source 1 , thousands of sensors wrap around the body like a net, in the “Mechanical Hysteria Creation, Robotic Damage Detection and Categorization System,” only one sensor is placed at strategic locations, and the system operates in a simpler structure through mathematical formulas and calculations. In the system from Source 5, the sensors are more prone to malfunction or damage, while the newly designed system has a lower likelihood of damage or failure. Moreover, due to the complex network structure of existing systems, their costs are high and reliability is low.

[0008] Robots cannot sense their environment through a body distribution similar to subcutaneous nerve systems like humans do. They lack the ability to respond sensitively to external stimuli. Therefore, when their bodies come into contact with other objects or are damaged, they cannot sense this situation through their nerve systems and cannot make decisions accordingly. Additionally, they cannot feel the pressure they apply to objects and cannot calculate the physical energies transferred to them by the objects. This situation leads to limited awareness of environmental effects and a lack of control over the state of their own bodies. As a result, when they incur damage, they may face problems in their work because they cannot measure the extent of the damage, and unexpected issues can lead to time losses. In fact, the lack of awareness of their own bodies prevents robots from detecting hardware failures, affecting the efficiency of their tasks. This situation leads to a failure to maintain their structural integrity, increasing the risk of permanent damage. Thus, the likelihood of experiencing failure before completing their tasks increases.

[0009] In the subject matter of the application, a nerve system has been designed to autonomously detect the point of damage. This will allow for the detection of the damage point as if it were a human, enabling the damaged part to be replaced in a fully autonomous manner. This situation can solve issues related to cost, reliability, and time loss.

[0010] Moreover, while in previous techniques it was not expected for robots to detect the damage levels of their own body parts and replace the damaged parts accordingly, our system has been designed with an autonomous system not only for the detection of the damaged part but also for its replacement. While existing techniques do not fully adapt to the robot and operate in a human-assisted manner, our system enables the robot to perceive sensations and act autonomously in problem-solving. Essentially, the closest system planned to be implemented (Source-5) cannot perform damage detection and naturally does not offer a solution autonomously. This system operates entirely on human commands, whereas our system autonomously transmits repair commands after performing damage detection.

[0011] DESCRIPTION OF THE INVENTION

[0012] The subject matter of the application, “Mechanical Hysteria Creation, Robotic Damage Detection and Categorization System,” contains only a nerve system, unlike the document mentioned in the prior art. The closest techniques for damage detection and analysis of the body's state are as follows:

[0013] Cameras: Robots can monitor their environment using cameras. Cameras can detect changes in the environment and identify damage through image analysis techniques.

[0014] Data Analysis: Robots can detect damage by analyzing data related to their hardware and software. For instance, monitoring the performance of motors or measuring battery voltage are important for damage detection.

[0015] Human Intervention: If the damage occurring in the robot's body cannot be identified by the robot's own system, engineers must examine the robot and perform damage detection.

[0016] Unlike the system described in Source 1 by Daniels John James on 2016-10-20, Wearable Electric Multi-Sensory Human / Machine Human / Human Interfaces, the “Mechanical Hysteria Creation, Robotic Damage Detection and Categorization System” uses a nerve system consisting of one piezoelectric sensor placed at strategic points instead of thousands of haptic sensors. This results in a simpler, lower-cost, and more reliable structure. The application of a network composed of thousands of haptic sensors to the user's body requires complex engineering and carries a low reliability risk. Another difference between the systems is that, unlike the network of haptic sensors spread over the body in the system from Source 5, the piezoelectric sensors placed at strategic points account for the magnitude of the impact, the wear coefficient of the body, and the damage to body parts.

[0017] While the haptic sensor system in Source 1 is hardware-focused, the Mechanical Hysteria Creation, Robotic Damage Detection, and Categorization System is software-focused.

[0018] The system uses mathematical formulas to calculate hysterization, operating by generating averages of piezoelectric sensors and categorizing the produced signals.

[0019] Additionally, in the system from Source 5, visual and auditory systems work together and are integrated with the haptic sensor system, whereas in the Mechanical Hysteria Creation, Robotic Damage Detection and Categorization System, there exists a hysterization system that only contains piezoelectric sensors.

[0020] This system can be used to determine the conditions under which prosthetic body parts in humans sustain damage or undergo deformation. Similarly, it can be used to calculate the rates at which robotic limbs and parts are affected by deformations resulting from impacts or damages. This similar system could be an important tool for evaluating the durability and damage resistance of prosthetic parts and robotic limbs.

[0021] In this context, the greatest advantages our system will offer include the ability for robots to perform their own damage detection, the capability to categorize damage based on its severity, the ability to decide whether to continue a task based on the extent of the damage, and the capability to carry out repairs. These advantages will contribute to progress in the industry regarding cost, time loss, and reliability. Additionally, it will provide a significant contribution to Industry 4.0.

[0022] Description of Figures Figure 1 . Simple Mechanical Hysteria on the X Axis

[0023] Figure 2. Simple Mechanical Hysteria on the Y Axis

[0024] Figure 3. Simple Mechanical Hysteria Creation on the X and Y Axes

[0025] Figure 4. Application of the System on the Robot Limb on the X and Y Axes

[0026] Figure 5. General View of the System Adapted to the Limb with X and Y Axes

[0027] Figure 6. General Hardware Structure of the System

[0028] Figure 7. Use of the System in Feet

[0029] Figure 8. Body Parts Divided into Categories

[0030] Figure 9. Numbered Sensor System Example for the Left Hand

[0031] Reference List

[0032] 1 . Piezoelectric Sensor on the X Axis 1

[0033] 2. Piezoelectric Sensor on the X Axis 2

[0034] 3. Piezoelectric Sensor on the Y Axis 3

[0035] 4. Piezoelectric Sensor on the Y Axis 4

[0036] 5. Piezoelectric Sensor on the X Axis 5

[0037] 6. Piezoelectric Sensor on the X Axis 6

[0038] 7. Piezoelectric Sensor on the Y Axis 7

[0039] 8. Piezoelectric Sensor on the Y Axis 8

[0040] 9. Mechanical Hysteria Line on the X Axis

[0041] 10. Mechanical Hysteria Line on the Y Axis

[0042] 11 . Piezoelectric Sensor (General)

[0043] 12. Data T ransmission Cable

[0044] 13. Port Module

[0045] 14. Robot Limb

[0046] 15. Mainboard

[0047] 16. Robot Head

[0048] 17. Robot Chest Section

[0049] 18. Right Arm

[0050] 19. Left Arm

[0051] 20. Right Hand 21 . Left Hand

[0052] 22. Right Leg

[0053] 23. Left Leg

[0054] 24. Right Foot

[0055] 25. Left Foot

[0056] 26. Coordinate (Left Hand Outer / 1.1 )

[0057] 27. Coordinate (Left Hand Outer / 1.2)

[0058] 28. Coordinate (Left Hand Outer / 2.1 )

[0059] 29. Coordinate (Left Hand Outer I 2.2)

[0060] 30. Coordinate (Left Hand Outer 12.3)

[0061] 31 .Coordinate (Left Hand Outer / 3.1 )

[0062] 32. Coordinate (Left Hand Outer I 3.2)

[0063] 33. Coordinate (Left Hand Outer I 3.3)

[0064] 34. Coordinate (Left Hand Outer / 4.1 )

[0065] 35. Coordinate (Left Hand Outer 14.2)

[0066] 36. Coordinate (Left Hand Outer 14.3)

[0067] 37. Coordinate (Left Hand Outer / 5.1 )

[0068] 38. Coordinate (Left Hand Outer I 5.2)

[0069] 39. Coordinate (Left Hand Outer I 5.3)

[0070] 40. Coordinate (Left Hand Inner / 6.1 )

[0071] 41 .Coordinate (Left Hand Inner / 6.2)

[0072] 42. Coordinate (Left Hand Inner / 6.3)

[0073] 43. Coordinate (Left Hand Inner / 6.4)

[0074] DETAILED DESCRIPTION OF THE INVENTION

[0075] In this detailed description, the preferred alternatives of the subject matter invention, the Mechanical Hysteria Creation, Robotic Damage Detection and Categorization System, are explained solely for a better understanding of the topic and will not impose any limiting effects. The Mechanical Hysteria Creation, Robotic Damage Detection and Categorization System spreads throughout the robot's body, similar to the nervous system in humans, and enables the robot to physically perceive when an impact, contact, or holding occurs by simulating this event.

[0076] Creating Mechanical Hysteria along the X and Y axes is a critical technology for robots, allowing them to analyze their own conditions by calculating the environmental effects applied to their bodies. Consequently, they can take action to repair themselves, generate damage reports to identify damaged points, or adjust their precision in their tasks. By using the Mechanical Hysteria System placed in the hands, they can calculate the force applied to their hands.

[0077] If the system is supported by additional sensors, it can easily gain the ability to calculate temperature and various effects with temperature sensors.

[0078] The system consists of basic Piezoelectric Sensors (11 ), auxiliary sensors dependent on preferences (Pressure, Temperature, Humidity, etc.), Data Transmission Cable (12), Port Module (13), and Mainboard (15).

[0079] The system can be examined under 3 main headings:

[0080] 1 . Mechanical Hysteria on the X Axis

[0081] 2. Mechanical Hysteria on the Y Axis

[0082] 3. Mechanical Hysteria on the X and Y Axes

[0083] At least 2 sensors are required to create mechanical hysteria. The mechanical hysteria established with these 2 sensors is unidirectional and linear, as shown in Figure-1 with Numbered X Axis Mechanical Hysteria Lines (9). The mechanical hysteria established with 4 sensors is realized in two dimensions on the X and Y axes, as shown in Figure-4, and when any point on the body is touched, the robot perceives it and calculates the intensity of the touch and categorizes it. The sizes and shapes of the piezoelectric sensors (11 ) can vary. This allows them to be tailored and adapted according to different sloped surfaces, flat surfaces, and different shapes.

[0084] Establishing Hysteria Area and Formulas

[0085] To create mechanical hysteria for robots, precise piezoelectric sensors (11 ) and specific mathematical operations are required for these sensors. As a result of these operations, data is categorized, and the touch point and touch force on the robotic limb (14) are detected by behaving like a Nervous System based on the piezoelectric sensors (11 ).

[0086] Formula Explanation / Logic

[0087] 1. The Mainboard(15) receives unprocessed sensor data through Piezoelectric Sensors (11 ) and stores it (for example, an average of 100 or 50 data points is collected).

[0088] The number of data points collected is recorded, and the average sensor data for the piezoelectric sensors (11 ) is obtained. The Young's modulus values are calculated according to the structure of the plate where the sensors are located (Plate’s Elasticity Coefficient).

[0089] 2. To find the proximity of the contact point to the piezoelectric sensors (11 ) as a percentage, the averages of the piezoelectric sensors (11 ) on the same axis are summed, and the total of the two sensor averages is obtained. (For example, the averages of the sensors on the X axis in Figure-1 are summed.)

[0090] 3. To find the proximity of the contact point to the piezoelectric sensors (11 ), the total of the two sensors is divided by the sensor averages. Then, the proximity of the contact point to the piezoelectric sensors (11 ) as a percentage is found by multiplying by 100.

[0091] 4. To find the proximity to the piezoelectric sensors (11 ) in cm, the distance between the piezoelectric sensors (11 ) on the same axis is multiplied by their percentage (for example, the length of the X axis in Figure-1 is 50 cm, and 50 cm is written). This percentage is multiplied by 100 again, and the proximity of the object transferring physical energy to the sensors is obtained in cm.

[0092] The four steps described in this section are illustrated with the following formulas.

[0093] Formulas

[0094] X-Axis Formula Definitions

[0095] First Processed Sensor Data = FPSD

[0096] Second Processed Sensor Data = SPSD

[0097] Number of Measurements Taken = NMT

[0098] Sum of Two Sensor Averages = STSA First Average Sensor Data = FASD Second Average Sensor Data = SASD First Sensor Percentage = FSP Second Sensor Percentage = SSP

[0099] H = Coefficient for Error Margin Correction

[0100] X = Variable Coefficient for Error Margin Correction

[0101] Young’s Modulus = E = (o / E) First Average Sensor Data Sum of Two Sensor Averages:

[0102] STSA = FASD + SASD

[0103] First Sensor Percentage:

[0104] FSP = (FASD / STSA ) x 100

[0105] Second Sensor Percentage:

[0106] SSP = (SASD / STSA ) x 100

[0107] Distance to First Sensor:

[0108] Distance_First = FSP * Cm_Value * 100

[0109] Distance to Second Sensor:

[0110] Distance_Second = SSP * Cm_Value * 100

[0111] Y-Axis Formula Definitions:

[0112] Third Processed Sensor Data = TPSD

[0113] Fourth Processed Sensor Data = FPSD

[0114] Number of Measurements Taken = NMT

[0115] Sum of Two Sensor Averages = STSA

[0116] Third Average Sensor Data = TASD

[0117] Fourth Average Sensor Data = FASD

[0118] Third Sensor Percentage = TSP

[0119] Fourth Sensor Percentage = FSP

[0120] H = Coefficient for Error Margin Correction

[0121] X = Variable Coefficient for Error Margin Correction

[0122] Young's Modulus (Plate Elasticity Coefficient is Determined) E = o / £

[0123] Third Average Sensor Data: Fourth Average Sensor Data:

[0124] Sum of Two Sensor Averages:

[0125] STSA = TASD+ FASD

[0126] Third Sensor Percentage:

[0127] TSP = (TASD / STSA) x 100

[0128] Fourth Sensor Percentage:

[0129] FSP= (FASD / STSA) x 100

[0130] Distance to Third Sensor:

[0131] Distance_Third = TSP* Cm_Value* 100

[0132] Distance to Fourth Sensor:

[0133] Distance_Fourth = FSP* Cm_Value* 100

[0134] X and Y Axis Formula Definitions:

[0135] Fifth Processed Sensor Data = FPSD

[0136] Sixth Processed Sensor Data = SXPSD

[0137] Seventh Processed Sensor Data = SEPSD Eighth Processed Sensor Data = EPSD Number of Measurements Taken = NMT

[0138] Sum of Two Sensor Averages A = STSAA Sum of Two Sensor Averages B = STSAB Fifth Average Sensor Data = FASD Sixth Average Sensor Data = SXASD Seventh Average Sensor Data = SEASD Eighth Average Sensor Data = EASD

[0139] Fifth Sensor Percentage = FSP

[0140] Sixth Sensor Percentage = SXSP Seventh Sensor Percentage = SEESP

[0141] Eighth Sensor Percentage = ESP

[0142] H = Coefficient for Error Margin Correction

[0143] X = Variable Coefficient for Error Margin Correction

[0144] Young’s Modulus (Plate Elasticity Coefficient is Determined) E = o / s

[0145] Average Sensor Data:

[0146] Sum of Two Sensor Averages:

[0147] Sum of Two Sensor Averages A (STSAA) = FASD + SXASD

[0148] Sum of Two Sensor Averages B (STSAB) = SEASD + EASD

[0149] Sensor Percentages:

[0150] Fifth Sensor Percentage (FSP ) = (FASD I STSAA ) * 100

[0151] Sixth Sensor Percentage (SXSP ) = (SXASD I STSAA ) * 100

[0152] Seventh Sensor Percentage (SEESP ) = (SEASD I STSAB ) * 100

[0153] Eighth Sensor Percentage (ESP ) = (EASD / STSAB ) * 100

[0154] Distance to Sensors:

[0155] Distance to Fifth Sensor = FSP * Cm_Value * 100

[0156] Distance to Sixth Sensor = SXSP * Cm_Value * 100 Distance to Seventh Sensor = SEESP * Cm_Value * 100

[0157] Distance to Eighth Sensor = ESP * Cm_Value * 100

[0158] By doing so, when any point on the robot's body is touched with physical force, hysterization occurs, working like a nervous system. For example, in the system placed on a hand as in Figure-4, when points such as the middle point of the hand, the top left point, the bottom right of the hand, and the bottom left of the hand are touched, data will be obtained in such a way that the distance between the sensors will be at the upper limit.

[0159] When the X-axis Mechanical Hysteria Line (9) is 20 cm in length, and the Y-axis Mechanical Hysteria Line (10) is also 20 cm:

[0160] • X axis and Y axis (X, 10 / Y, 10)

[0161] • X axis and Y axis (X, 01 Y, 20)

[0162] • X axis and Y axis (X, 201 Y, 0)

[0163] • X axis and Y axis (X, 0 / Y, 0)

[0164] This system will work similarly in the robot's body, with the robotic nerve system and nerve networks spreading throughout the body, legs, and other limbs.

[0165] Application to Robot Body and Damage Feedback

[0166] With the formulas above, the system in Figure-4 can be installed both on the robot's limbs and, as in Figure-8, across the robot's entire body. Thus, when any point on the robot's body is subject to significant damage or physical force, the system will detect it and inform the robot about the extent of the damage.

[0167] In this way, the robot will be able to create conscious awareness of its own body and surroundings, providing environmental awareness.

[0168] The general hardware structure of the system can be seen in Figure-5, with components listed in order: Piezoelectric Sensors (11 ), Data Transmission Cable (12), Port Module (13), and Mainboard (15). The system has a simple structure. Optionally, resistance and temperature sensors can be added to the system, enabling it to detect the temperature values of objects in addition to physical forces, making it usable in various industrial applications.

[0169] Categorizing Damage

[0170] When the system is integrated into the entire body of a humanoid robot, as shown in Figure-8, and one of the categorized parts of the body is touched or damaged, the raw sensor data from the piezoelectric sensors is processed through formulas. The resulting average sensor data is then sent to the Mainboard (15), providing detailed information about the event.

[0171] By categorizing it, the robot can react under certain conditions and make additional decisions based on the situation.

[0172] For example, when damage is given to the robot limb (14):

[0173] • Fifth Processed Sensor Data = FPSD

[0174] • Sixth Processed Sensor Data = SXPSD

[0175] • Seventh Processed Sensor Data = SEPSD

[0176] • Eighth Processed Sensor Data = EPSD

[0177] • Number of Measurements Taken = NMT

[0178] • Sum of Two Sensor Averages A = STSAA

[0179] • Sum of Two Sensor Averages B = STSAB

[0180] Fifth Average Sensor Data (FASD):

[0181] Sixth Average Sensor Data (SXASD):

[0182] Seventh Average Sensor Data (SEASD): Eighth Average Sensor Data (EASD):

[0183] Sum of Two Sensor Averages A (STSAA) = FASD + SXASD

[0184] Sum of Two Sensor Averages B (STSAB) = SEASD + EASD

[0185] • Fifth Sensor Percentage (FSP) = (FASD I STSAA) * 100

[0186] • Sixth Sensor Percentage (SXSP) = (SXASD I STSAA) * 100

[0187] • Seventh Sensor Percentage (SEESP) = (SEASD I STSAB) * 100

[0188] • Eighth Sensor Percentage (ESP) = (EASD / STSAB) * 100

[0189] Distance to Sensors:

[0190] • Distance to Fifth Sensor = FSP * Cm_Value * 100

[0191] • Distance to Sixth Sensor = SXSP * Cm_Value * 100

[0192] • Distance to Seventh Sensor = SEESP * Cm_Value * 100

[0193] • Distance to Eighth Sensor = ESP * Cm_Value * 100

[0194] These formulas allow the robot to process the detected sensor data and evaluate the condition of the body parts in specific situations, categorizing the damage based on its location and intensity, enabling the robot to gain environmental awareness.

[0195] Fifth Average Sensor Data (FASD):

[0196] The sum of the Fifth Processed Sensor Data, divided by the number of measurements taken, is corrected with a variable error coefficient and multiplied by a function of Young's Modulus to obtain the result.

[0197] Sixth Average Sensor Data (SXASD): The sum of the Sixth Processed Sensor Data, divided by the number of measurements taken, is corrected with a variable error coefficient and multiplied by a function of Young's Modulus to obtain the result.

[0198] Seventh Average Sensor Data (SEASD):

[0199] The sum of the Seventh Processed Sensor Data, divided by the number of measurements taken, is corrected with a variable error coefficient and multiplied by a function of Young's Modulus to obtain the result.

[0200] Eighth Average Sensor Data (EASD):

[0201] The sum of the Eighth Processed Sensor Data, divided by the number of measurements taken, is corrected with a variable error coefficient and multiplied by a function of Young's Modulus to obtain the result.

[0202] With the average sensor data, the size of the damage on the robot limb is determined, and the damage levels are categorized as indicated below. Based on the level of damage, the robot can make decisions.

[0203] When a robot encounters physical force that could cause damage during a task, the processed sensor data is used to detect and evaluate values within the range of 0-100-1000-2000.

[0204] In the case of gentle contact, data at a value of 100 is obtained, which corresponds to the "Low" level. When contacted with moderate force, data at a value of 500 is received, reaching the "Medium" level. When contacted with strong force, data at a value of 1000 is obtained, defining the "High" level. Finally, if the contact is with crushing force, data at a value of 2000 is received, reaching the "Extreme" level. This classification provides the robot with simple definitions of how it should behave in various task conditions by giving it certain priorities. For example, a robot exposed to excessive force during a task can leave its task for repair and maintenance according to the damage classification. It can determine the damage to parts under the influence of crushing force and store this information in its memory by being aware of the components of the damaged limb. This allows it to develop the ability to replace its parts later on.

[0205] A robot performing delicate tasks can apply force within the range of 0-100 to the object it is holding or adjust the force within that range. It can perform its tasks with high accuracy by sensing the force applied to the object through its hardware. This helps robots to perform better in high-precision tasks in both the industrial and healthcare sectors.

[0206] Body Parts and Sensor Numbering System for Humans and Humanoid Robots

[0207] For a humanoid robot's body to be adapted, the robot's body is divided into 10 categories. The part categorization is as follows: Robot Head (16), Robot Chest Section (17), Right Arm (18), Left Arm (19), Right Hand (20), Left Hand (21 ), Right Leg (22), Left Leg (23), Right Foot (24), Left Foot (25). Each of these parts has 1 Port Module (13), making a total of 10 Port Modules (13), which are connected to the robot's Mainboard via Data Transmission Cable (12).

[0208] Each categorized section has piezoelectric sensors (11 ) with serial numbers. This allows the specific location of each sensor on the robot's body to be easily identified and recognized by the Mainboard (15).

[0209] The numbering system is simply composed of the name of the limb, the inner (inside the limb) and outer (fingers), and the sequence number. If there is a finger (outer), the sequence starts from the thumb, and if there isn’t (inner), the sensors are numbered accordingly. For example, the Sensor Numbers defined on the Mainboard (15), looking at Figure-9, starting from the thumb of the left hand are:

[0210] • Left Hand Thumb = (Left Hand Outer / 1 .1 ), (Left Hand Outer 1 1 .2)

[0211] • Left Hand Index Finger = (Left Hand Outer I 2.1 ), (Left Hand Outer I 2.2), (Left Hand Outer / 2.3)

[0212] • Left Hand Middle Finger = (Left Hand Outer I 3.1 ), (Left Hand Outer I 3.2), (Left Hand Outer / 3.3)

[0213] • Left Hand Ring Finger = (Left Hand Outer I 4.1 ), (Left Hand Outer I 4.2), (Left Hand Outer / 4.3)

[0214] • Left Hand Pinky Finger = (Left Hand Outer I 5.1 ), (Left Hand Outer I 5.2), (Left Hand Outer / 5.3)

[0215] • Left Hand Palm = (Left Hand Inner / 6.1 ), (Left Hand Inner 16.2), (Left Hand Inner 16.3), (Left Hand Inner I 6.4)

[0216] In this way, all limbs are categorized and numbered similarly.

[0217] Usage for Prosthetic Limbs Used by Disabled Individuals and Those Who Have Lost Sensory Perception

[0218] The Mechanical Hysteria Creation, Robotic Damage Detection, and Categorization System can be used to determine damage and deformations in mechanical or prosthetic limbs of individuals who have lost their limbs. Thus, broken or damaged parts can be easily detected. The force applied to objects held by prosthetic limbs can be calculated, and this force can be communicated to the user through warning lights, sound alerts, LED screens, or other assisting systems.

Claims

CLAIMS1. The invention is a mechanical hysteria creation, robotic damage detection, and categorization system that spreads throughout the robot's body and simulates the robot's physical awareness when a collision, contact, or grip by another hand occurs on the robot's body, characterized in that, it comprises the following; piezoelectric sensors (11 ), data transmission cable (12), port module (13), motherboard (14), and optional auxiliary sensors (such as pressure, heat, humidity, etc.).

2. According to claim 1 , the invention is a method for creating mechanical hysteria by acting like a nervous system when a physical force is applied to any point on the robot's body, characterized in that, it comprises the following;- First, the motherboard (14) receives raw sensor data through piezoelectric sensors (1 1 ) and stores it,- The number of measurements taken is divided by the raw sensor data to obtain the average sensor data from the piezoelectric sensors (11 ), and the Young’s modulus values of the sensors are calculated based on their plate structure,- The proximity of the contact point to the piezoelectric sensors (11 ) is determined by adding up the averages of the piezoelectric sensors (11 ) on the same axis and obtaining the total of two sensor averages,- To find the proximity of the piezoelectric sensors (11 ) to the contact point, the total of two sensors is divided by the sensor averages and then multiplied by 100 to calculate the percentage of proximity to the piezoelectric sensors (11 ),- To find the proximity in centimeters of the object transmitting physical energy to the piezoelectric sensors (11 ), the distance between the piezoelectric sensors (11 ) on the same axis is multiplied by the sensor percentage and then multiplied by 100 again to obtain the proximity to the sensors in centimeters.

3. According to claim 1 , the invention is a robotic damage detection and categorization method that processes the sensed sensor data, evaluates thecondition of body parts under certain circumstances, and categorizes the damage based on its location and severity, providing the robot with environmental awareness, characterized in that, it comprises the following;- The values of the piezoelectric sensors (11 ) are classified by sub-conditional algorithms using conditional algorithms applied to the robot's body limbs and regions,- The classified values are sent to the motherboard (15), allowing the robot to make logical decisions based on the extent of the damage, such as replacing the damaged part, terminating its current task, continuing its current task, notifying the human user about the damaged part, etc.,- Making autonomous decisions to perform self-body repair independently of human intervention.

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