A robot quick-change load data interface intelligent identification and configuration method and system

Abstract

The embodiment of the application discloses a kind of robot quick-change load data interface intelligent identification and configuration method and system.It relates to robot interface identification and intelligent control technical field.The method comprises: after detecting that target load accesses robot quick-change interface, the type of external communication interface occupied by target load is identified;PUF challenge data is sent to target load, and corresponding PUF response data is received;When the identity trusted state is trusted, the response drift of the corresponding PUF response data is calculated, and the PUF drift level of target load is determined according to the response drift;And according to PUF drift level, interface communication response data, historical service log and current motion working condition data generate load aging risk parameter;Active compliance configuration parameter is generated;Robot motion control parameters and load data interface parameters are configured based on active compliance configuration parameter.The application improves the safety and reliability in the use process of robot quick-change load.

Description

Technical Field

[0001] This application relates to the field of robot interface recognition and intelligent control technology, and in particular to a method and system for intelligent recognition and configuration of robot quick-change load data interface. Background Technology

[0002] With the increasing application of industrial robots, mobile robots, and bionic quadruped robots in scenarios such as inspection, handling, testing, security, and special operations, robots typically need to quickly change external payloads such as cameras, LiDAR, mechanical grippers, sensor modules, communication modules, or tools depending on the task. To improve task switching efficiency, robot quick-change interfaces are increasingly being used to achieve mechanical, electrical, and data connections for payloads.

[0003] Current methods for identifying quick-change payloads in robots typically rely on preset payload numbers, communication protocol handshake information, or manually configured parameters. When the payload is an original manufacturer's device and the interface is in good condition, these methods can achieve basic identification and configuration. However, in practical use, robots may connect to counterfeit payloads, high-imitation payloads, unauthorized payloads, or payloads that have aged significantly after long-term service. Even if such payloads can pass the basic communication handshake, problems such as unverifiable identity, unstable interface contact, communication response drift, degraded electrical performance, or reduced structural connection reliability may exist.

[0004] Therefore, it is necessary to propose an intelligent identification and configuration method for robot quick-change load data interfaces that can combine multiple factors. Summary of the Invention

[0005] This application provides a method and system for intelligent identification and configuration of robot quick-change load data interface, which solves the problems in the prior art where robot quick-change load identification mainly relies on fixed numbering or communication protocols, making it difficult to identify unauthorized loads and aging loads, and unable to actively adjust robot operating parameters and interface configuration parameters according to load status.

[0006] This application provides the following solution: According to the first aspect, a method for intelligent identification and configuration of a robot quick-change payload data interface is provided. The method includes: after detecting that a target payload has been connected to the robot's quick-change interface, identifying the type of external communication interface occupied by the target payload, wherein the external communication interface type includes an Ethernet interface, a USB 2.0 interface, or an RS485 interface; sending PUF challenge data to the target payload, and receiving the corresponding PUF (Physical Unclonable) challenge data. The system retrieves PUF (Physically Unclonable Function) response data; determines the identity trust status based on the matching relationship between the PUF response data and the pre-stored trustworthy response template; when the identity trust status is trustworthy, calculates the response drift corresponding to the PUF response data, and determines the PUF drift level of the target load based on the response drift; acquires interface communication response data, historical service logs, and current robot motion condition data, and generates load aging risk parameters based on the PUF drift level, the interface communication response data, the historical service logs, and the current motion condition data; when the load aging risk parameters meet the preset derating configuration conditions, generates active compliance configuration parameters corresponding to the PUF drift level; and configures robot motion control parameters and load data interface parameters based on the active compliance configuration parameters.

[0007] According to one achievable method in an embodiment of this application, identifying the type of external communication interface occupied by the target payload includes: performing low-power link detection on the Ethernet interface, USB2.0 interface, and RS485 interface respectively, and obtaining the link establishment time, initial response frame, idle level state, and handshake retry count for each interface; and determining the type of external communication interface actually occupied by the target payload based on the link establishment time, the initial response frame, the idle level state, and the handshake retry count.

[0008] According to one achievable method in an embodiment of this application, sending PUF challenge data to the target payload includes: generating a corresponding PUF challenge sequence according to the type of the external communication interface, wherein the PUF challenge sequence includes basic identity challenge data and drift detection challenge data; wherein the basic identity challenge data is used to determine the identity trust status of the target payload, and the drift detection challenge data is used to obtain the PUF response stability of the target payload under different challenge conditions.

[0009] According to one achievable method in an embodiment of this application, calculating the response drift corresponding to the PUF response data includes: obtaining the bit difference position, response flip number, and response delay offset between the PUF response data and the pre-stored trusted response template; and generating the response drift based on the continuity of the bit difference position in the PUF response sequence, the response flip number, and the response delay offset.

[0010] According to one achievable method in an embodiment of this application, determining the PUF drift level of the target load based on the response drift includes: acquiring the current interface power supply voltage, interface temperature, and power-on stabilization time of the target load; performing environmental compensation on the response drift based on the interface power supply voltage, the interface temperature, and the power-on stabilization time to obtain the compensated response drift; and determining the corresponding PUF drift level based on the relationship between the compensated response drift and a preset drift range.

[0011] According to one achievable method in the embodiments of this application, the interface communication response data includes the number of link reconnection times, data frame loss rate, communication latency fluctuation, and number of interface error frames; the historical service log includes the cumulative number of plug-in / plug-out times, cumulative runtime, historical overload times, and historical abnormal disconnection times; the current motion condition data includes the robot's current gait type, movement speed, body posture change, and foot impact amplitude.

[0012] According to one achievable method in this application embodiment, generating load aging risk parameters based on the PUF drift level, the interface communication response data, the historical service log, and the current operating condition data includes: generating load-side degradation parameters based on the PUF drift level, the interface communication response data, and the historical service log; calculating the connection risk gain of the fast-switch interface under the current operating condition based on the load-side degradation parameters and the current operating condition data; and generating the load aging risk parameters based on the connection risk gain.

[0013] According to one achievable method in this application embodiment, generating the active compliance configuration parameters corresponding to the PUF drift level includes: determining the derating intensity based on the PUF drift level; calculating the maximum motion acceleration limit, the quick-switch interface axial virtual stiffness value, and the data interface bandwidth occupancy limit based on the derating intensity, and generating the active compliance configuration parameters.

[0014] According to one achievable method in this application embodiment, after configuring the robot motion control parameters and load data interface parameters based on the active compliance configuration parameters, the method further includes: collecting quick-switch interface vibration data, interface communication error data, and load power supply fluctuation data during robot operation; when the quick-switch interface vibration data, the interface communication error data, or the load power supply fluctuation data exceeds the corresponding feedback threshold, readjusting the active compliance configuration parameters, and writing the configuration parameters before and after adjustment and the operation feedback data into the abnormal compatibility sample library.

[0015] According to the second aspect, a robot quick-change payload data interface intelligent identification and configuration system is provided. The system includes: an interface identification module, used to identify the type of external communication interface occupied by the target payload after detecting that the target payload has accessed the robot quick-change interface; the external communication interface type includes an Ethernet interface, a USB 2.0 interface, or an RS485 interface; a PUF interaction module, used to send PUF challenge data to the target payload and receive corresponding PUF response data; an identity authentication module, used to determine the identity trust status based on the matching relationship between the PUF response data and a pre-stored trusted response template; and a drift analysis module, used to calculate the... The system comprises: a response drift quantity corresponding to the PUF response data, and a PUF drift level of the target load determined based on the response drift quantity; a risk assessment module, used to acquire interface communication response data, historical service logs, and current robot motion condition data, and generate load aging risk parameters based on the PUF drift level, the interface communication response data, the historical service logs, and the current motion condition data; a configuration generation module, used to generate active compliance configuration parameters corresponding to the PUF drift level when the load aging risk parameters meet preset derating configuration conditions; and a parameter configuration module, used to configure robot motion control parameters and load data interface parameters based on the active compliance configuration parameters.

[0016] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application not only identifies whether the payload can communicate, but also identifies the payload's trustworthiness through a PUF challenge response mechanism. Based on the payload's trustworthiness, it further analyzes the PUF response drift to identify subtle abnormalities in genuine payloads caused by connector wear, chip aging, or internal component degradation. Simultaneously, this application combines PUF drift levels, interface communication response data, historical service logs, and the robot's current motion condition data to generate payload aging risk parameters. This enables the robot to proactively enter derating configuration or proactively adapt to configuration based on the payload status and current motion risk, reducing the risks of quick-change interface shocks, communication interruptions, and payload damage, thereby improving the safety and reliability of the robot during quick-change payload use.

[0017] Of course, any product implementing this application does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart of the intelligent identification and configuration method for robot quick-change payload data interface provided in this application embodiment; Figure 2 A schematic diagram showing an embodiment and test results of the intelligent identification and configuration method for the robot quick-change payload data interface provided in this application. Figure 3 This is a structural block diagram of the intelligent identification and configuration system for the robot quick-change payload data interface provided in an embodiment of this application. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0021] Figure 1 This document presents a flowchart illustrating a method for intelligent identification and configuration of a robot's quick-change payload data interface, as provided in an embodiment of this application. Figure 1 As shown, the method may include the following steps: Step 101: After detecting that the target payload has been connected to the robot quick-switch interface, identify the type of external communication interface occupied by the target payload. The type of external communication interface includes Ethernet interface, USB 2.0 interface or RS485 interface.

[0022] Step 102: Send PUF challenge data to the target payload and receive the corresponding PUF response data.

[0023] Step 103: Determine the identity trust status based on the matching relationship between the PUF response data and the pre-stored trusted response template.

[0024] Step 104: When the identity trust status is trustworthy, calculate the response drift corresponding to the PUF response data, and determine the PUF drift level of the target payload based on the response drift.

[0025] Step 105: Obtain interface communication response data, historical service logs, and current robot motion condition data, and generate load aging risk parameters based on the PUF drift level, the interface communication response data, the historical service logs, and the current motion condition data.

[0026] Step 106: When the load aging risk parameter meets the preset derating configuration conditions, generate the active compliance configuration parameter corresponding to the PUF drift level.

[0027] Step 107: Configure robot motion control parameters and load data interface parameters based on the active compliance configuration parameters.

[0028] As can be seen from the above process, this application not only identifies whether the payload can communicate, but also identifies the payload's trustworthiness status through the PUF challenge response mechanism. Based on the trustworthiness of the payload's identity, it further analyzes the PUF response drift, thereby identifying subtle abnormal states of genuine payloads caused by connector wear, chip aging, or internal component degradation. Simultaneously, this application combines PUF drift levels, interface communication response data, historical service logs, and the robot's current motion condition data to generate payload aging risk parameters. This enables the robot to proactively enter derating configuration or proactively adapt to configuration based on the payload status and current motion risk, reducing the risks of quick-change interface impact, communication interruption, and payload damage, and improving the safety and reliability of the robot during quick-change payload use.

[0029] The following describes in detail each step of the above process and the effects that can be further produced, with reference to the embodiments.

[0030] Step 101 specifically involves: "After detecting that the target payload has been connected to the robot's quick-switch interface, identifying the type of external communication interface occupied by the target payload, wherein the type of external communication interface includes an Ethernet interface, a USB 2.0 interface, or an RS485 interface."

[0031] When the robot controller detects that a target payload has been connected to the robot's quick-switch interface, it does not immediately grant the target payload full power and complete data communication permissions. Instead, it first identifies the type of external communication interface that the target payload might use. This external communication interface type can include an Ethernet interface, a USB 2.0 interface, or an RS485 interface. By identifying the interface type before formal communication and power supply, the robot can avoid loading incorrect communication configuration parameters onto the target payload, and it can also prevent unknown or abnormal loads from impacting the robot controller, communication bus, or power supply circuit upon connection.

[0032] Specifically, the robot controller performs low-power link probing on the Ethernet, USB 2.0, and RS485 interfaces. Low-power link probing refers to sending basic probe signals or reading basic link status to each candidate interface under current-constrained, voltage-constrained, or communication-constrained conditions, rather than directly activating the target payload's full operating mode. This method can obtain the interface response characteristics of the target payload while ensuring safety, providing a basis for subsequently determining the type of external communication interface actually used by the target payload.

[0033] For Ethernet interfaces, the robot controller can detect the link establishment time, link negotiation status, and initial response frame during the physical link establishment process. The link establishment time reflects whether the Ethernet physical layer can establish a stable connection within a preset time. The initial response frame reflects whether the target payload returns handshake data, address negotiation data, or basic diagnostic data that conforms to Ethernet communication rules. If the Ethernet interface can establish a stable link in a short time and return an initial response frame in the expected format, the Ethernet interface can be considered a candidate for the target payload actually occupying the interface.

[0034] For USB 2.0 interfaces, the robot controller can detect the presence of USB pull-up characteristics, enumeration responses, device descriptor initial fields, and handshake retries of the target payload under limited power conditions. If the target payload can complete basic enumeration and return an initial response frame on the USB 2.0 interface, it indicates that the target payload may be connected to the robot's quick-switch interface via USB 2.0. The number of handshake retries can be used to determine the stability of the interface connection. A significant increase in the number of retries may indicate poor interface contact, abnormal response of the payload's internal control chip, or poor condition of the communication cable.

[0035] For RS485 interfaces, the robot controller can detect the bus idle level, initial response frame, inter-frame interval, and handshake retries. RS485 interfaces typically exhibit clear bus idle level characteristics; by detecting the idle level, it can determine whether the target payload is connected to this type of differential communication interface. The robot controller can also send basic probe frames according to a preset baud rate candidate set and determine whether RS485 communication has been successfully established based on the initial response frame returned by the target payload and the number of handshake retries. If the idle level is stable, the initial response frame format is valid, and the number of handshake retries is within the allowable range, the RS485 interface can be identified as the external communication interface type actually used by the target payload.

[0036] Link establishment time characterizes the time elapsed from the robot controller initiating low-power link detection to the formation of a valid communication connection between the candidate interface and the target interface. The initial response frame characterizes the first type of valid communication data returned by the target payload during the basic detection phase, which may include an interface identification field, a basic status field, a diagnostic field, or a handshake confirmation field. The idle level state characterizes the electrical stability of the candidate interface when no data is being transmitted. The number of handshake retries characterizes the number of times the robot controller repeatedly sends detection signals or handshake requests to establish a basic communication connection. These features reflect the degree of matching between the target payload and different external communication interfaces in terms of connection speed, communication format, electrical state, and connection stability, respectively.

[0037] When determining the type of external communication interface actually used by the target payload, the robot controller can comprehensively judge the link establishment time, initial response frame, idle level state, and handshake retries for each interface. For example, if the link establishment time of a certain interface is less than a preset time threshold, the initial response frame conforms to the communication format corresponding to that interface, the idle level state meets the electrical characteristics corresponding to that interface, and the number of handshake retries is less than a preset number threshold, it can be determined that the target payload actually uses that interface. If multiple interfaces have responses, the priority interface can be further determined based on the validity of the response frame, link stability, and the number of handshake retries to avoid misjudgment due to electrical crosstalk or residual signals.

[0038] Step 102 specifically involves: "Sending PUF challenge data to the target payload and receiving the corresponding PUF response data."

[0039] PUF, or Physically Unclonable Function, is a hardware identification technology that utilizes microscopic random differences naturally occurring during the manufacturing process of chips, circuits, or storage units to generate unique responses. Because these microscopic differences originate from uncontrollable physical deviations in the manufacturing process, even devices manufactured using the same design and process will rarely produce identical physical responses. Therefore, PUF can be considered an inherent identity characteristic of the payload hardware itself. In application, the robot sends challenge data to the target payload, which generates corresponding response data based on its internal PUF unit. The robot then compares this response data with a pre-stored trusted response template to determine if the target payload is trustworthy. Compared to traditional stored keys or device numbers, PUF does not require directly storing fixed keys, offering higher resistance to duplication and forgery, making it suitable for robot payload quick-change authentication, secure access, and status recognition.

[0040] When the target payload occupies an Ethernet interface, the robot controller can encapsulate the PUF challenge data into a data frame conforming to Ethernet communication rules and send it to the target payload along with the link negotiation status, address information, and frame check information. When the target payload occupies a USB 2.0 interface, the robot controller can send the PUF challenge data as a USB transfer request after device enumeration or basic diagnostic communication is completed, and receive the PUF response data according to the USB interface's response mechanism. When the target payload occupies an RS485 interface, the robot controller can encapsulate the PUF challenge data into a serial bus probe frame according to the target payload's corresponding communication address, baud rate, and frame check rules, and receive the PUF response data returned by the target payload within a preset response window. Through these methods, the PUF challenge process can be matched with the transmission characteristics of different external communication interfaces, avoiding authentication failures caused by inconsistent interface formats.

[0041] The PUF challenge sequence includes basic identity challenge data and drift detection challenge data. The basic identity challenge data is primarily used to verify the trustworthiness of the target payload's hardware identity. The robot controller can select highly stable and discriminative challenge data from a preset challenge set and send it to the target payload. The target payload generates corresponding PUF response data based on its internal PUF unit. Subsequently, the robot controller compares this PUF response data with a pre-stored trusted response template. If the matching degree between the two meets the preset authentication conditions, the target payload's identity trustworthiness status is determined to be trustworthy; if the matching degree does not meet the preset authentication conditions, the target payload is determined to be an unauthorized payload, a highly simulated payload, or an abnormal identity payload, and unauthorized access processing is triggered.

[0042] Drift detection challenge data differs from basic identity challenge data. Its purpose is not simply to determine whether the target payload is genuine, but rather to assess whether a trusted payload exhibits hardware response drift after long-term use. The robot controller can select challenge data that is more sensitive to the stability of the payload's internal PUF (Power Activated Functions) units, or repeatedly send the same challenge data multiple times, collecting the PUF response data returned by the target payload under different power-on phases, response wait times, or interface load conditions. By observing the consistency, bit flipping, response delay changes, and local response fluctuations of the PUF response data across multiple challenges, the PUF response stability of the target payload under different challenge conditions can be obtained.

[0043] Different challenge conditions can include different combinations of challenge data, different challenge sending orders, different challenge repetition counts, different response sampling times, and different interface communication load states. By setting different challenge conditions, the robot controller can obtain not only the response results of the target payload under standard certification conditions, but also the response change trend of the target payload under slight disturbance conditions. For example, when the internal chip of the target payload ages, the connector contact impedance increases, or the interface power supply stability decreases, the target payload may still pass the basic identity challenge, but under the drift detection challenge, it will exhibit phenomena such as slight bit flipping in the response, prolonged response time, or decreased consistency of repeated responses.

[0044] After obtaining the PUF response data corresponding to the drift detection challenge data, the robot controller can calculate the PUF response stability. The PUF response stability can be characterized by the consistency between multiple responses, the number of response flips, the response delay offset, and the concentration of abnormal responses. If the target payload can match a trustworthy response template in the basic identity challenge but shows a significant decrease in stability in the drift detection challenge, it indicates that although the target payload has a trustworthy identity, it may already have the risk of aging, wear, or poor interface contact.

[0045] Step 103 specifically involves: "Determining the identity trust status based on the matching relationship between the PUF response data and the pre-stored trusted response template."

[0046] The robot controller can extract information such as response bit sequence, response delay, response integrity, and verification results from the PUF response data and match it with a pre-stored trusted response template. The matching relationship can include bit consistency, the number of differing bits, the distribution of differing positions, the consistency of response data length, and whether the challenge-response correspondence is correct. If the bit consistency between the PUF response data and the pre-stored trusted response template reaches a preset authentication threshold, and the correspondence between the challenge data and the response data meets preset authentication rules, then the target payload's identity is determined to be trusted.

[0047] If the matching degree between the PUF response data and the pre-stored trusted response template is lower than the preset authentication threshold, or if the response data returned by the target payload exhibits abnormal length, verification failure, response timeout, or inconsistent challenge-response relationship, the robot controller can determine that the target payload's identity is untrusted. An untrusted status can indicate that the target payload is not an authorized payload, or that the target payload has a PUF unit anomaly, data link anomaly, or is at risk of tampering. In this case, the robot controller will not directly configure the open power supply and full data interface permissions as a normal payload, but will instead enter the unauthorized access processing procedure.

[0048] In actual judgment, since the PUF response may be slightly affected by temperature, supply voltage, power-on settling time, or interface transmission status, the matching relationship does not require the PUF response data to be completely consistent with the pre-stored trusted response template. Instead, a certain range of acceptable differences is allowed. The robot controller can set an authentication threshold to distinguish between normal physical fluctuations and identity anomalies. For example, when the number of differing bits is small and the difference locations are scattered, the difference can be considered a normal PUF fluctuation; when the number of differing bits is large, the difference locations are continuously concentrated, or the response delay is significantly abnormal, it may indicate that the target payload's identity is abnormal or that the hardware status has changed significantly.

[0049] Step 104 specifically involves: "When the identity trust status is trustworthy, calculate the response drift corresponding to the PUF response data, and determine the PUF drift level of the target payload based on the response drift."

[0050] Once the target load is identified as a reliable load, the robot controller does not directly assume that the load is in a healthy state. Instead, it further calculates the response drift corresponding to the PUF response data. This response drift characterizes the degree of deviation of the current PUF response data from a pre-stored reliable response template. Because the target load may be affected by factors such as device aging, connector wear, solder joint fatigue, power supply fluctuations, and environmental changes during long-term use, its internal PUF units, while still capable of authentication, may exhibit subtle changes in PUF response characteristics. Therefore, this application further evaluates the service status of the target load by calculating the response drift.

[0051] Specifically, the robot controller acquires the bit difference positions, response flip counts, and response delay offsets between the current PUF response data and the pre-stored trusted response template. The bit difference positions characterize the distribution of locations in the current PUF response sequence that are inconsistent with the trusted response template; the response flip count characterizes the number of times the PUF response bits undergo state changes under the same challenge conditions; and the response delay offset characterizes the degree of change in the time required for the target payload to return PUF response data relative to the standard response time. When the internal circuitry of the target payload ages or the interface contact condition deteriorates, it often leads to instability in local response bits, and the response time may also change. Therefore, the above parameters can reflect the state changes of the target payload from different dimensions.

[0052] Furthermore, the robot controller generates a response drift based on the continuity of the bit difference positions in the PUF response sequence, the number of response flips, and the response delay offset. The continuity of the bit difference positions is used to distinguish between random fluctuations and concentrated anomalies. When the difference positions are discretely distributed, it usually indicates normal physical fluctuations; when the difference positions are continuously concentrated in a specific area, it may indicate that the corresponding circuit area has degraded. The number of response flips reflects the stability of the PUF response; the more flips, the worse the stability of the PUF response. The response delay offset reflects the degree of change in the response capability of the target load's internal circuitry; the larger the delay offset, the higher the probability that the target load's internal state deviates from its initial state. The robot controller generates the response drift after comprehensively analyzing the above factors to characterize the degree of deviation between the target load's current state and its initial reliable state.

[0053] After obtaining the response drift, the robot controller further determines the PUF drift level of the target load. Since PUF response characteristics are affected not only by hardware aging but also by power supply and environmental conditions, this application first performs environmental compensation processing on the response drift before determining the drift level. Specifically, the robot controller acquires the current interface power supply voltage, interface temperature, and power-on stabilization time of the target load. The interface power supply voltage reflects the current power supply status of the target load, the interface temperature reflects the current thermal environment status, and the power-on stabilization time reflects the time it takes for the target load to reach a stable operating state after power-on.

[0054] Subsequently, the robot controller performs environmental compensation on the response drift based on the interface power supply voltage, interface temperature, and power-on settling time. When there are slight fluctuations in the interface power supply voltage, a high interface temperature, or the target load has not yet fully entered a stable operating state, the PUF response may exhibit some temporary changes. To avoid misinterpreting environmental factors as hardware aging, this application eliminates the influence of these factors on the response drift through an environmental compensation mechanism, thereby obtaining the compensated response drift. The compensated response drift more accurately reflects the changes in the target load's own hardware state.

[0055] After obtaining the compensated response drift, the robot controller determines the corresponding PUF drift level based on the relationship between the compensated response drift and the preset drift range. The preset drift range can be established based on statistical results from a large number of samples. For example, a smaller compensated response drift corresponds to a low drift level, indicating that the target load is in normal service condition; a medium-range compensated response drift corresponds to a medium drift level, indicating that the target load has experienced some degree of aging or wear; a larger compensated response drift corresponds to a high drift level, indicating that the target load may have significant risks of device degradation, abnormal interface contact, or performance degradation due to long-term service.

[0056] Step 105 specifically involves: "Acquiring interface communication response data, historical service logs, and current motion condition data of the robot, and generating load aging risk parameters based on the PUF drift level, the interface communication response data, the historical service logs, and the current motion condition data."

[0057] After determining the PUF drift level of the target load, the robot controller further combines interface communication response data, historical service logs, and current motion condition data to generate load aging risk parameters. This process does not simply judge whether the load is aging, but first evaluates the degree of degradation of the target load itself, and then combines the impact of the robot's current motion state on the additional load generated by the quick-change interface, ultimately obtaining an aging risk result that is more consistent with the actual operating scenario.

[0058] Interface communication response data includes the number of link reconnections, data frame loss rate, communication latency fluctuation, and number of interface error frames; historical service logs include the cumulative number of plug-in / plug-outs, cumulative runtime, historical overload counts, and historical abnormal disconnection counts; current motion condition data includes the robot's current gait type, movement speed, body posture changes, and foot impact amplitude.

[0059] Specifically, the robot controller first generates load-side degradation parameters based on the PUF drift level, interface communication response data, and historical service logs. The PUF drift level reflects the degree of deviation of the target load's internal hardware response characteristics relative to its reliable state; the interface communication response data reflects the communication stability of the target load under the current interface connection state, such as the number of link reconnections, data frame loss rate, communication latency fluctuations, and the number of interface error frames; the historical service logs reflect the cumulative wear and tear of the target load during long-term use, such as the cumulative number of plug-in / plug-out cycles, cumulative runtime, historical overload cycles, and historical abnormal disconnection cycles. By combining the above data, the one-sidedness of judging the load status based on a single indicator can be avoided.

[0060] The load-side degradation parameter is primarily used to characterize the degree of degradation that has occurred within the target load itself. A high PUF drift level indicates a decrease in the stability of the target load's internal hardware response; an abnormal increase in interface communication response data suggests potential contact problems, unstable transmission, or malfunctions in the current data interface control circuit; and a high number of cumulative plug-in / plug-out counts, cumulative runtime, or historical overload counts in the historical service log indicates that the load has undergone a prolonged or high-intensity service process. Based on this, the robot controller generates the load-side degradation parameter, enabling it to simultaneously reflect hardware identity response degradation, communication interface degradation, and historical service cumulative degradation.

[0061] After obtaining the load-side decay parameters, the robot controller further incorporates current motion condition data to determine whether the decay state will be amplified in the current robot motion scenario. Current motion condition data may include information such as the robot's current gait type, speed, body posture changes, foot impact amplitude, robotic arm acceleration, or load mounting direction. For quadruped robots, mobile robots, or high-speed robots, even if the same target load has the same load-side decay parameters, the risks to quick-change interfaces differ significantly under low-speed, stable walking conditions versus high-speed running, sharp turns, and climbing stairs. Therefore, evaluating only the load's own state is insufficient to accurately determine actual operational risks.

[0062] Connection risk gain characterizes the amplification of connection risk to the quick-switch interface under the current motion condition. When the robot is in a low-speed, stable, and low-impact operating state, even if the target load experiences slight degradation, the quick-switch interface experiences less dynamic impact, and the connection risk gain can be low. When the robot is in a state of high-speed motion, frequent posture changes, significant foot impact, or significant load inertia, the quick-switch interface experiences increased vibration, tension, axial impact, and data contact disturbance, and the connection risk gain increases accordingly. By calculating the connection risk gain, the impact of the robot's current motion state on the aging load can be incorporated into the risk assessment.

[0063] The robot controller calculates the connection risk gain based on the load-side degradation parameters and current motion condition data, and further generates load aging risk parameters. These parameters indicate not only whether the target load has aged, but also the actual level of risk posed by its continued operation under the current motion conditions. For example, the same load might correspond to a lower aging risk under static inspection conditions, but a higher aging risk under high-speed inspection or complex ground walking conditions. Therefore, the system can dynamically adjust the risk assessment results according to different motion scenarios.

[0064] In one implementation, the connection risk gain of the quick-switch interface under the current operating condition can be denoted as... The calculation formula is as follows:

[0065] in, To connect risk gain, it represents the degree to which the current motion condition amplifies the aging risk of the target load; This is the load-side degradation parameter, with a value ranging from 0 to 1. The larger the value, the more severe the degradation of the target load itself. This is the excitation parameter for motion conditions, with a value range of 0 to 1. The larger the value, the stronger the impact of the robot's current motion on the quick-change interface. This is the critical value for load decay; This is the critical value for motion excitation; Basic risk amplification factor; This is a high-risk coupling amplification factor.

[0066] Among them, the excitation parameters of the motion condition It can be further calculated using the following formula:

[0067] in, The gait impact coefficient is used to characterize the impact intensity corresponding to the robot's current gait type. This represents the robot's current speed. The preset maximum speed; This refers to the change in fuselage attitude. I represents the preset maximum attitude change; I represents the foot impact amplitude or the quick-change interface impact amplitude. The preset maximum impact amplitude; , , , Let be the weighting coefficients, and satisfy:

[0068] Furthermore, load aging risk parameters can be... Represented as:

[0069] Therefore, the system not only considers the decay state of the target load itself, but also the amplification effect of the current robot motion condition on the risk of quick-change interface, so that the subsequent active adaptive configuration is more in line with the actual operating state.

[0070] Step 106 specifically involves: "When the load aging risk parameter meets the preset derating configuration conditions, generating active compliance configuration parameters corresponding to the PUF drift level."

[0071] When the system generates load aging risk parameters, it does not immediately shut down or disable the target load. Instead, it implements proactive compliant configuration based on the risk level. Proactive compliant configuration refers to the robot dynamically adjusting its motion parameters, quick-change interface mechanical parameters, and data communication parameters based on the target load's current health status and operational risks. This allows the robot to maintain its operational capabilities while reducing the impact on the quick-change interface and the risk of further load degradation. This approach differs from traditional fault alarm mechanisms; its core idea is to extend the load's service life and improve system operational safety while the target load still meets usability requirements through dynamic derating.

[0072] Specifically, the robot controller first determines the trustworthiness status of the target payload. When the trustworthiness status is trustworthy and the payload aging risk parameter reaches the preset aging risk threshold, it indicates that the current target payload is an authorized payload, but it already exhibits a certain degree of aging, wear, or performance degradation. At this point, the system enters the proactive compliance configuration phase. If the trustworthiness status is untrustworthy, the unauthorized access processing procedure is executed, and the proactive compliance configuration phase is not entered.

[0073] After entering the active compliance configuration phase, the robot controller determines the derating intensity based on the PUF drift level. The derating intensity characterizes the level of operational capability that the target load needs to reduce relative to its normal state. Since the PUF drift level reflects the degree to which the internal hardware characteristics of the target load deviate from their initial state, it serves as an important basis for assessing the load's health status. Generally, a higher PUF drift level indicates more significant aging of the internal components of the target load, corresponding to a greater derating intensity; a lower PUF drift level indicates that the target load is still in a relatively stable state, corresponding to a smaller derating intensity.

[0074] In one implementation, the system can classify derating intensity into multiple levels. For example, low drift level corresponds to mild derating mode, medium drift level corresponds to moderate derating mode, and high drift level corresponds to severe derating mode. Different derating modes correspond to different motion restriction strategies, interface protection strategies, and communication restriction strategies. This hierarchical control approach avoids using the same processing scheme for loads with different aging levels, thereby improving the specificity of the configuration results.

[0075] After determining the derating strength, the robot controller calculates the maximum motion acceleration limit, the axial virtual stiffness value of the quick-change interface, and the upper limit of data interface bandwidth usage. The maximum motion acceleration limit is used to restrict the inertial impact generated during robot movement. When the target load ages, the mechanical connection stability of the quick-change interface may decrease. If the robot continues to operate at its original acceleration, it is prone to generating large inertial loads, thereby accelerating interface wear. Therefore, by reducing the maximum motion acceleration limit, the impact and vibration forces on the target load can be reduced.

[0076] The axial virtual stiffness value of the quick-change interface is used to adjust the dynamic response characteristics of the robot control system to the quick-change interface. Axial virtual stiffness refers to the interface compliance characteristic parameter constructed by the robot controller at the control level. When the virtual stiffness is high, the robot tends to maintain strict position tracking; when the virtual stiffness is low, the robot allows the interface to generate a certain degree of flexible buffering when subjected to impact. For target loads with aging risks, appropriately reducing the axial virtual stiffness value can reduce the instantaneous impact load on the quick-change interface under vibration, collision, or inertia, thereby reducing the risk of interface damage.

[0077] The data interface bandwidth occupancy limit is used to restrict the maximum data transmission resources that the target payload can occupy during communication. When the interface connection status begins to degrade, high-bandwidth data transmission can easily lead to data frame loss, increased communication retransmissions, and aggravated communication latency fluctuations. Therefore, the system adjusts the data interface bandwidth occupancy limit according to the derating intensity, prioritizing the transmission of critical control and status data by the target payload, while reducing the transmission of non-critical data, thereby improving communication stability.

[0078] Furthermore, the robot controller combines the calculated maximum motion acceleration limit, the axial virtual stiffness value of the quick-change interface, and the upper limit of data interface bandwidth usage to generate corresponding active compliance configuration parameters. These active compliance configuration parameters simultaneously reflect the adjustment results of the motion control layer, the interface protection layer, and the data communication layer, and serve as the control basis for subsequent robot operation.

[0079] For example, when the target load corresponds to a low drift level, the system only slightly reduces the maximum motion acceleration while maintaining high data bandwidth and interface stiffness; when the target load corresponds to a medium drift level, the system further reduces the motion acceleration, while appropriately reducing the axial virtual stiffness of the quick-switch interface and restricting some non-critical data transmission; when the target load corresponds to a high drift level, the system significantly reduces the motion acceleration, enables a high compliance mode, and strictly limits the data interface bandwidth usage, thereby minimizing interface impact and communication load.

[0080] Through the above methods, this application establishes a correlation mechanism from PUF drift level to derating intensity and then to active compliance configuration parameters, enabling the robot to dynamically adjust its operating strategy according to the actual health status of the target payload.

[0081] Step 107 specifically involves: "Configuring robot motion control parameters and load data interface parameters based on the active compliance configuration parameters."

[0082] After generating the active compliance configuration parameters, the robot controller further configures the robot motion control parameters and load data interface parameters based on the active compliance configuration parameters.

[0083] Specifically, the robot controller first parses the maximum motion acceleration limit, the quick-change interface axial virtual stiffness value, and the data interface bandwidth occupancy limit contained in the active compliance configuration parameters, and maps them to the corresponding control modules. The maximum motion acceleration limit corresponds to the robot motion control module, the quick-change interface axial virtual stiffness value corresponds to the robot compliance control module, and the data interface bandwidth occupancy limit corresponds to the communication management module. Each control module updates its corresponding control strategy based on the received configuration parameters, enabling the robot to enter an operating mode that matches the current load state.

[0084] Regarding the configuration of robot motion control parameters, the robot controller can adjust the allowable acceleration range during robot motion planning based on the maximum acceleration limit. When the target load is in a slightly aged state, the system only slightly limits the acceleration; when the target load is in a higher-risk state, the system further reduces the allowable acceleration range. This reduces the inertial impact generated during robot startup, braking, turning, and attitude adjustment, and reduces the dynamic load on the quick-change interface. Simultaneously, the robot controller can also synchronously adjust the maximum motion speed, rate of change of angular velocity, trajectory smoothing coefficient, and gait switching strategy based on the active compliance configuration parameters, making the overall motion process smoother.

[0085] For configuring the axial virtual stiffness parameter of the quick-change interface, the robot controller can adjust the force-position hybrid control strategy in the interface direction. When the target load is in good condition, the system adopts a higher stiffness control mode to ensure high position tracking accuracy and motion response speed. When the target load is at risk of aging, the system gradually reduces the axial virtual stiffness of the interface, enabling the robot to generate a certain degree of flexible buffering when subjected to impact or vibration. This reduces the direct transmission of impact loads to the quick-change interface connection, lowering the risk of connector wear and mechanical connection fatigue. Simultaneously, the lower virtual stiffness can also absorb some vibration energy during motion, improving the adaptability of aging loads in complex environments.

[0086] Regarding the configuration of payload data interface parameters, the robot controller reallocates communication resources based on the upper limit of data interface bandwidth usage. When the target payload is in normal condition, data transmission is allowed according to the standard bandwidth. When the target payload is at risk of aging, the system prioritizes the transmission of control commands, status feedback, and safety data, while limiting the bandwidth usage of non-critical data. For example, the frequency of image data uploads can be reduced, the upload cycle of non-critical diagnostic information can be shortened, and the continuous transmission time of large amounts of data can be limited, thereby reducing the burden on the communication link. In this way, data loss, communication retransmissions, and latency fluctuations caused by interface aging can be reduced.

[0087] Furthermore, the robot controller can establish a linkage between motion control parameters and data interface parameters. For example, when the robot enters a high-speed motion state, even if the current load risk level remains unchanged, the system can appropriately increase the communication priority of critical control data and further reduce the bandwidth occupied by non-critical data to ensure the real-time performance of motion control commands and status feedback data. When the robot enters a low-speed inspection or static operation state, some bandwidth resources can be appropriately released to improve data acquisition efficiency. Through this linkage mechanism, motion control and communication control can jointly adapt to the current state of the target load.

[0088] Preferably, after configuring the robot motion control parameters and load data interface parameters based on the active compliance configuration parameters, the method further includes: collecting quick-switch interface vibration data, interface communication error data, and load power supply fluctuation data during robot operation; when the quick-switch interface vibration data, the interface communication error data, or the load power supply fluctuation data exceeds the corresponding feedback threshold, readjusting the active compliance configuration parameters, and writing the configuration parameters before and after adjustment and the operation feedback data into the abnormal compatibility sample library.

[0089] Specifically, during robot operation, the system continuously collects vibration data from the quick-switch interface, interface communication error data, and load power supply fluctuation data. The quick-switch interface vibration data reflects the intensity and impact of mechanical vibration during robot movement and can be acquired using vibration sensors, accelerometers, or inertial measurement units installed near the quick-switch interface. The interface communication error data reflects the current data connection status, including the number of communication retransmissions, the number of erroneous frames, the data packet loss rate, the number of communication delay anomalies, and the number of link reconnections. The load power supply fluctuation data reflects the stability of the target load's power supply status, including parameters such as the amplitude of power supply voltage fluctuations, the amplitude of current fluctuations, the number of instantaneous drops, and the duration of power supply anomalies.

[0090] Vibration data from the quick-switch interface reflects the mechanical reliability of the connection between the target load and the robot. When the quick-switch interface wears, the locking mechanism loosens, or the load mounting stiffness decreases, the vibration generated by the robot during movement will increase significantly. Interface communication error data reflects changes in the quality of the data interface connection. When connectors age, contact resistance increases, or the stability of the data transmission link decreases, the number of communication errors usually gradually increases. Load power supply fluctuation data reflects the stability of the electrical connection. When the power supply connection terminals are worn, oxidized, or have poor contact, the amplitude of power supply fluctuations often increases significantly. Therefore, the above three types of feedback data monitor the operating status of the quick-switch interface from the mechanical connection layer, data communication layer, and electrical power supply layer, respectively.

[0091] To achieve proactive protection, the system sets corresponding feedback thresholds for vibration data from the quick-switch interface, interface communication error data, and load power supply fluctuation data. These feedback thresholds can be set based on the target load type, interface specifications, historical operating data, and safety requirements. When any feedback data exceeds its corresponding threshold, it indicates that the current proactive compliance configuration may no longer be fully adapted to the actual state of the target load, or that the target load state has changed further. In this case, the system triggers a readjustment process for the proactive compliance configuration parameters.

[0092] In one implementation, the system can reconfigure the active compliance parameters using a step-by-step adjustment approach. For example, when vibration data first exceeds the feedback threshold, only a slight adjustment is performed; if the data continues to exceed the limit after adjustment, further derating is implemented; when abnormalities occur in multiple consecutive detection cycles, an enhanced protection mode is entered. This gradual adjustment mechanism avoids a significant decrease in robot performance caused by excessive derating at once, while ensuring that the system has sufficient protection capabilities.

[0093] During the readjustment process, the robot controller first analyzes the sources of data exceeding the thresholds. If the vibration data of the quick-change interface exceeds the feedback threshold, it indicates an increased risk to the mechanical connection. The system can further reduce the maximum motion acceleration limit and the axial virtual stiffness value of the quick-change interface to improve the interface compliance. If the interface communication error data exceeds the feedback threshold, it indicates a decrease in data link stability. The system can further reduce the upper limit of data interface bandwidth usage and increase the transmission priority of critical control data. If the load power supply fluctuation data exceeds the feedback threshold, it indicates a decrease in power supply connection reliability. The system can reduce the target load workload to mitigate the impact of instantaneous power changes.

[0094] After completing the proactive adaptation configuration parameter adjustment, the system writes the configuration parameters before and after the adjustment, along with the corresponding operational feedback data, into the anomaly compatibility sample library. This library records abnormal behavior and corresponding handling results under different loads, aging states, and operating conditions. The operational feedback data may include vibration data, communication error data, power supply fluctuation data, current motion condition data, and the corresponding PUF drift level. The configuration parameters may include the maximum motion acceleration limits before and after adjustment, the axial virtual stiffness value of the quick-switch interface, and the upper limit of data interface bandwidth usage.

[0095] As the number of samples in the anomaly compatibility sample library continues to increase, the system can gradually accumulate operational patterns of different types of loads under different conditions. For example, a certain type of load may be prone to communication anomalies under moderate drift levels, while another type of load is more likely to experience power supply fluctuations. By recording these differential characteristics, the system can proactively adopt targeted adaptive configuration strategies when similar conditions are subsequently identified, thereby reducing the trial-and-error adjustment process and improving configuration efficiency and protection effectiveness.

[0096] To further illustrate the technical effects of the present invention, a specific implementation method and its test results are first given.

[0097] Figure 2This document presents an embodiment and test results of the intelligent identification and configuration method for the robot quick-swap payload data interface provided in this application. The example uses a quadruped inspection robot equipped with a replaceable high-definition visual inspection payload. The robot is equipped with a quick-swap interface that supports multiple external communication interfaces, including Ethernet, USB 2.0, and RS485. After the target payload is connected to the robot's quick-swap interface, the robot controller first performs low-power link detection, acquiring the link establishment time, initial response frame, idle level state, and handshake retries for each candidate interface. Analysis reveals that the target payload has the shortest link establishment time on the USB 2.0 interface, the initial response frame conforms to the preset USB device description rules, and the handshake retries are within the normal range. Therefore, it is determined that the target payload occupies the USB 2.0 interface.

[0098] Subsequently, the robot controller generates the corresponding PUF challenge sequence based on the USB 2.0 interface and sends basic identity challenge data and drift detection challenge data to the target payload. The SRAM PUF unit inside the target payload generates corresponding PUF response data based on the challenge data. The robot controller compares the PUF response data with the pre-stored trusted response template, and the result shows that the target payload's identity authentication is successful, and its identity trust status is trusted. At the same time, the system further analyzes the differences between the current PUF response data and the trusted response template, obtains the bit difference position, response flip number, and response delay offset, and generates the corresponding response drift. After environmental compensation of the response drift based on the current interface power supply voltage, interface temperature, and power-on stabilization time, the PUF drift level corresponding to the target payload is determined to be a medium drift level.

[0099] Next, the robot controller acquires the interface communication response data and historical service logs of the target payload. The cumulative runtime is 2800 hours, the cumulative number of plug-in / plug-out operations is 620, the number of historical abnormal connection drops is 17, and the current communication link exhibits slight latency fluctuations. The system generates payload-side degradation parameters based on the PUF drift level, interface communication response data, and historical service logs. Simultaneously, the robot is currently in outdoor inspection mode, with high movement speeds and frequent crossings of steps and slopes. The system calculates the connection risk gain based on the current motion conditions and ultimately generates payload aging risk parameters.

[0100] Upon assessment, the load aging risk parameters reached the preset aging risk threshold, therefore the system entered active compliance configuration mode. The robot controller determined the derating intensity based on the corresponding PUF drift level and generated active compliance configuration parameters. Specifically, the maximum motion acceleration limit was adjusted to 80% of the rated value, the axial virtual stiffness of the quick-change interface was adjusted to 75% of the standard value, and the upper limit of data interface bandwidth usage was adjusted to 85% of the normal bandwidth. Subsequently, the robot performed the inspection task based on the adjusted parameters.

[0101] During the inspection, the system continuously collects vibration data of the quick-switch interface, interface communication error data, and load power supply fluctuation data. When the vibration amplitude of the quick-switch interface in a complex terrain area exceeds the feedback threshold, the system automatically recalculates the active compliance configuration parameters, further reducing the maximum motion acceleration to 70% of the rated value, while reducing the axial virtual stiffness of the quick-switch interface. The system then writes the configuration parameters before and after the adjustment, along with the corresponding operational feedback data, into the abnormal compatibility sample library for rapid configuration optimization under similar load conditions in the future.

[0102] To verify the effectiveness of the proposed solution, 30 visual inspection payloads with different service cycles were selected for testing, including 10 new payloads, 10 moderately aged payloads, and 10 heavily aged payloads. During the testing process, a traditional fixed configuration scheme and the intelligent recognition and active compliance configuration scheme proposed in this application were compared and verified. The test environment included smooth road surface inspection, slope inspection, and complex obstacle crossing scenarios, with a total operating time of 500 hours.

[0103] Test results show that, in terms of identification, the proposed solution can accurately identify all authorized payloads and successfully intercept unauthorized payloads, achieving an identification accuracy rate of 100%. Regarding aging condition identification, the proposed solution utilizes a method combining PUF drift levels and historical service data for joint analysis, enabling accurate differentiation of payloads with different aging degrees, achieving an aging condition determination accuracy rate of 96.7%.

[0104] In operational reliability testing, compared to traditional fixed configuration solutions, the proposed solution reduced the number of abnormal vibration events at the quick-change interface by approximately 42%, the communication error rate by approximately 38%, and the number of abnormal load power supply fluctuations by approximately 35%. For moderately and heavily aged loads, no interface detachment, communication interruption, or abnormal power outages occurred during inspections in complex terrain. Simultaneously, the robot's task completion rate remained above 98%, demonstrating that this application improves system safety while maintaining high operational continuity.

[0105] The test results further demonstrate that this application, through a combination of PUF identity authentication, PUF drift level assessment, load aging risk analysis, and proactive compliant configuration, can effectively improve the safety, stability, and environmental adaptability of the robot's quick-change load system, and significantly extend the service life of the quick-change interface and the target load.

[0106] According to another embodiment, a robot quick-change payload data interface intelligent identification and configuration system is provided. Figure 3 A schematic block diagram of the robot's quick-change payload data interface intelligent identification and configuration system is shown according to one embodiment. Figure 3 As shown, the system includes: The interface identification module 301 is used to identify the type of external communication interface occupied by the target payload after detecting that the target payload has been connected to the robot quick-change interface. The type of external communication interface includes Ethernet interface, USB 2.0 interface or RS485 interface.

[0107] The PUF interaction module 302 is used to send PUF challenge data to the target payload and receive corresponding PUF response data.

[0108] The identity authentication module 303 is used to determine the identity trust status based on the matching relationship between the PUF response data and the pre-stored trusted response template.

[0109] The drift analysis module 304 is used to calculate the response drift amount corresponding to the PUF response data when the identity trust status is trustworthy, and to determine the PUF drift level of the target payload based on the response drift amount.

[0110] The risk assessment module 305 is used to acquire interface communication response data, historical service logs and current motion condition data of the robot, and generate load aging risk parameters based on the PUF drift level, the interface communication response data, the historical service logs and the current motion condition data.

[0111] The configuration generation module 306 is used to generate active compliance configuration parameters corresponding to the PUF drift level when the load aging risk parameters meet the preset derating configuration conditions.

[0112] The parameter configuration module 307 is used to configure robot motion control parameters and load data interface parameters based on the active compliance configuration parameters.

[0113] As one feasible approach, the interface identification module 301 identifies the type of external communication interface occupied by the target payload, including: performing low-power link detection on the Ethernet interface, USB 2.0 interface and RS485 interface respectively, and obtaining the link establishment time, initial response frame, idle level state and handshake retry count for each interface; and determining the type of external communication interface actually occupied by the target payload based on the link establishment time, the initial response frame, the idle level state and the handshake retry count.

[0114] As one feasible approach, the PUF interaction module 302 sends PUF challenge data to the target payload, including: generating a corresponding PUF challenge sequence according to the type of the external communication interface, wherein the PUF challenge sequence includes basic identity challenge data and drift detection challenge data; wherein the basic identity challenge data is used to determine the identity trust status of the target payload, and the drift detection challenge data is used to obtain the PUF response stability of the target payload under different challenge conditions.

[0115] As an implementable approach, the drift analysis module 304 calculates the response drift corresponding to the PUF response data, including: obtaining the bit difference position, response flip number, and response delay offset between the PUF response data and the pre-stored trusted response template; and generating the response drift based on the continuity of the bit difference position in the PUF response sequence, the response flip number, and the response delay offset.

[0116] As an implementable approach, the drift analysis module 304 determines the PUF drift level of the target load based on the response drift amount, including: acquiring the current interface power supply voltage, interface temperature, and power-on stabilization time of the target load; performing environmental compensation on the response drift amount based on the interface power supply voltage, the interface temperature, and the power-on stabilization time to obtain the compensated response drift amount; and determining the corresponding PUF drift level based on the relationship between the compensated response drift amount and a preset drift range.

[0117] As an implementable approach, the interface communication response data in the risk assessment module 305 includes the number of link reconnections, data frame loss rate, communication latency fluctuation, and number of interface error frames; the historical service log includes the cumulative number of plug-in / plug-outs, cumulative runtime, historical overload counts, and historical abnormal disconnection counts; the current motion condition data includes the robot's current gait type, movement speed, body posture change, and foot impact amplitude.

[0118] As an implementable approach, the risk assessment module 305 generates load aging risk parameters based on the PUF drift level, the interface communication response data, the historical service log, and the current operating condition data, including: generating load-side degradation parameters based on the PUF drift level, the interface communication response data, and the historical service log; calculating the connection risk gain of the fast-switch interface under the current operating condition based on the load-side degradation parameters and the current operating condition data; and generating the load aging risk parameters based on the connection risk gain.

[0119] As one feasible approach, the configuration generation module 306 generates the active compliance configuration parameters corresponding to the PUF drift level, including: determining the derating intensity based on the PUF drift level; calculating the maximum motion acceleration limit, the quick-switch interface axial virtual stiffness value, and the data interface bandwidth occupancy limit based on the derating intensity, and generating the active compliance configuration parameters.

[0120] As an implementable approach, after configuring the robot motion control parameters and load data interface parameters based on the active compliance configuration parameters, the parameter configuration module 307 further includes: collecting quick-switch interface vibration data, interface communication error data, and load power supply fluctuation data during robot operation; when the quick-switch interface vibration data, the interface communication error data, or the load power supply fluctuation data exceeds the corresponding feedback threshold, readjusting the active compliance configuration parameters, and writing the configuration parameters before and after adjustment and the operation feedback data into the abnormal compatibility sample library.

[0121] In addition, embodiments of this application also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method described in any of the foregoing method embodiments.

[0122] And an electronic device comprising: one or more processors; and a memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method described in any of the foregoing method embodiments.

[0123] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for intelligent identification and configuration of a robot's quick-change payload data interface, characterized in that, The method includes: After detecting that the target payload has been connected to the robot's quick-switch interface, the type of external communication interface occupied by the target payload is identified. The type of external communication interface includes an Ethernet interface, a USB 2.0 interface, or an RS485 interface. Send PUF challenge data to the target payload and receive the corresponding PUF response data; The identity trust status is determined based on the matching relationship between the PUF response data and the pre-stored trusted response template; When the identity trust status is trustworthy, calculate the response drift corresponding to the PUF response data, and determine the PUF drift level of the target payload based on the response drift. Acquire interface communication response data, historical service logs, and current robot motion condition data, and generate load aging risk parameters based on the PUF drift level, the interface communication response data, the historical service logs, and the current motion condition data; When the load aging risk parameters meet the preset derating configuration conditions, active compliance configuration parameters corresponding to the PUF drift level are generated. Configure robot motion control parameters and load data interface parameters based on the active compliance configuration parameters.

2. The method according to claim 1, characterized in that, The identification of the type of external communication interface occupied by the target payload includes: Low-power link detection is performed on the Ethernet interface, USB 2.0 interface and RS485 interface respectively to obtain the link establishment time, initial response frame, idle level status and handshake retry count for each interface; The type of external communication interface actually used by the target payload is determined based on the link establishment time, the initial response frame, the idle level state, and the number of handshake retries.

3. The method according to claim 1, characterized in that, Sending PUF challenge data to the target payload includes: A corresponding PUF challenge sequence is generated based on the external communication interface type. The PUF challenge sequence includes basic identity challenge data and drift detection challenge data. The basic identity challenge data is used to determine the identity credibility status of the target payload, and the drift detection challenge data is used to obtain the PUF response stability of the target payload under different challenge conditions.

4. The method according to claim 1, characterized in that, The calculation of the response drift corresponding to the PUF response data includes: Obtain the bit difference position, response flip number, and response delay offset between the PUF response data and the pre-stored trusted response template; The response drift is generated based on the continuity of the bit difference position in the PUF response sequence, the number of response flips, and the response delay offset.

5. The method according to claim 1, characterized in that, Determining the PUF drift level of the target load based on the response drift includes: Obtain the current interface power supply voltage, interface temperature, and power-on stabilization time of the target load; Environmental compensation is performed on the response drift based on the interface power supply voltage, the interface temperature and the power-on stabilization time to obtain the compensated response drift. Based on the relationship between the compensated response drift amount and the preset drift range, the corresponding PUF drift level is determined.

6. The method according to claim 1, characterized in that, The interface communication response data includes the number of link reconnection times, data frame loss rate, communication latency fluctuation, and number of interface error frames. The historical service log includes the cumulative number of plug-in / plug-outs, cumulative runtime, historical overload counts, and historical abnormal disconnection counts. The current motion data includes the robot's current gait type, movement speed, body posture change, and foot impact amplitude.

7. The method according to claim 6, characterized in that, The process of generating load aging risk parameters based on the PUF drift level, the interface communication response data, the historical service logs, and the current operating condition data includes: Based on the PUF drift level, the interface communication response data, and the historical service logs, load-side degradation parameters are generated. Based on the load-side degradation parameters and the current operating condition data, the connection risk gain of the quick-switch interface under the current operating condition is calculated, and the load aging risk parameters are generated based on the connection risk gain.

8. The method according to claim 1, characterized in that, The generation of active compliance configuration parameters corresponding to the PUF drift level includes: The derating intensity is determined based on the PUF drift level; The maximum motion acceleration limit, the axial virtual stiffness value of the quick-change interface, and the upper limit of data interface bandwidth usage are calculated based on the derating strength to generate the active compliance configuration parameters.

9. The method according to claim 1, characterized in that, After configuring the robot motion control parameters and load data interface parameters based on the active compliance configuration parameters, the process further includes: During robot operation, vibration data of quick-change interface, interface communication error data, and load power supply fluctuation data are collected. When the vibration data of the quick-switch interface, the communication error data of the interface, or the power supply fluctuation data of the load exceeds the corresponding feedback threshold, the active compliance configuration parameters are readjusted, and the configuration parameters before and after the adjustment and the operation feedback data are written into the abnormal compatibility sample library.

10. A robot quick-change payload data interface intelligent identification and configuration system, characterized in that, The system includes: The interface identification module is used to identify the type of external communication interface occupied by the target payload after detecting that the target payload has been connected to the robot quick-change interface. The type of external communication interface includes Ethernet interface, USB2.0 interface or RS485 interface. The PUF interaction module is used to send PUF challenge data to the target payload and receive corresponding PUF response data. The identity authentication module is used to determine the identity trust status based on the matching relationship between the PUF response data and the pre-stored trusted response template; The drift analysis module is used to calculate the response drift amount corresponding to the PUF response data when the identity trust status is trustworthy, and to determine the PUF drift level of the target payload based on the response drift amount; The risk assessment module is used to acquire interface communication response data, historical service logs and current robot motion condition data, and generate load aging risk parameters based on the PUF drift level, the interface communication response data, the historical service logs and the current motion condition data; The configuration generation module is used to generate active compliance configuration parameters corresponding to the PUF drift level when the load aging risk parameters meet the preset derating configuration conditions. The parameter configuration module is used to configure robot motion control parameters and load data interface parameters based on the active compliance configuration parameters.

Images