Multi-robot cooperative control method and system for industrial control

Abstract

The invention relates to the technical field of robot control, in particular to a multi-robot cooperative control method and system for industrial control. The method comprises the following steps: acquiring real-time working data of the sorting robot; determining a real-time working state according to the real-time working data; acquiring cargo monitoring data; determining cargo characteristics according to the cargo monitoring data; determining a cooperative beat deviation value according to the real-time working state and the cargo characteristics; determining a cargo compensation amount according to cargo characteristics; and according to the cooperative beat deviation value and the cargo compensation amount, the cooperative beat instruction set is adjusted. The deviation of the sorting robot is dynamically recognized, the superimposed effect of beat interference is relieved, it is ensured that the work beat disorder is responded in time, and goods accumulation or robot action conflicts are avoided. The action time sequence of the sorting robot is dynamically coordinated, the overall efficiency and accuracy are improved, time window conflicts and sorting sequence constraints are solved, beat disorder, goods deviation and equipment conflicts are reduced, and finally closed-loop self-adaptive control is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of robot control, in particular to a multi-robot collaborative control method and system for industrial control. BACKGROUND

[0002] In the field of industrial control, especially in the logistics sorting system, multi-robot collaborative control has become a core technology to improve efficiency and accuracy. Logistics sorting involves a large number of multi-category goods flowing continuously on the conveyor belt, and multiple sorting robots work collaboratively to complete the identification, grabbing and sorting tasks of the goods.

[0003] However, the existing multi-robot collaborative control method is usually based on fixed work pace or simple timing rules. This method often causes problems in the entire sorting process when facing dynamic logistics environment, and even causes equipment damage or goods loss, which seriously restricts the efficiency and stability in high-throughput sorting scenarios. SUMMARY

[0004] The present application provides a multi-robot collaborative control method and system for industrial control to solve the above problems.

[0005] In a first aspect, the present application provides a multi-robot collaborative control method for industrial control, the method comprising: obtaining real-time working data of a sorting robot; determining a real-time working state according to the real-time working data; obtaining goods monitoring data; determining goods characteristics according to the goods monitoring data; determining a collaborative pace deviation value according to the real-time working state and the goods characteristics; determining a goods compensation amount according to the goods characteristics; adjusting a collaborative pace instruction set according to the collaborative pace deviation value and the goods compensation amount.

[0006] This solution acquires real-time operational data from the sorting robot, ensuring the capture of its dynamic behavior. This supports real-time monitoring and deviation detection, preventing decreased motion accuracy due to equipment status uncertainties and providing an immediate and reliable source of raw information for the entire collaborative control process. Based on the real-time operational data, the solution determines the real-time operational status, dynamically identifies robot deviations, mitigates the cumulative effect of cycle time interference, ensures timely response to work cycle disorder, and avoids goods accumulation or robot motion conflicts. It also acquires goods monitoring data, ensuring coverage of the fluidity and variability of massive quantities of goods, thereby capturing the real-time status of goods on the conveyor belt. This effectively addresses the dynamic impact of goods characteristics, reducing the risk of sorting failures or goods damage due to goods offset or category differences. Based on the goods monitoring data, the solution determines goods characteristics, adapts to goods diversity, resolves time window conflicts, and avoids robot conflicts or goods omissions caused by mismatched goods characteristics. Finally, based on the real-time operational status and goods characteristics, the solution determines the collaborative cycle time deviation value, effectively compensating for the cumulative effect of cycle time interference, reducing overall throughput decline, and mitigating time window conflict issues. Based on the characteristics of the goods, the compensation amount is determined to address the dynamic impact of these characteristics, reduce the sorting failure rate and the risk of goods damage, and support adaptation to external disturbances. Based on the collaborative cycle time deviation and the compensation amount, the collaborative cycle time instruction set is adjusted to dynamically coordinate the timing of the sorting robot's actions, improving overall efficiency and accuracy, resolving time window conflicts and sorting sequence constraints, reducing cycle time disorder, goods deviation, and equipment conflicts, and ultimately achieving closed-loop adaptive control.

[0007] Optionally, determining the coordinated cycle time deviation value based on the real-time working status and the characteristics of the goods includes: Obtain the goods sorting task; analyze the goods sorting task to determine the sorting sequence; Based on the characteristics of the goods and the sorting sequence, determine the initial collaborative cycle instruction set; Based on the initial coordinated beat instruction set, determine the expected instruction timestamp; Based on the real-time working status, determine the actual arrival timestamp; The coordinated rhythm deviation value is determined based on the expected instruction timestamp and the actual arrival timestamp.

[0008] This solution acquires cargo sorting tasks, transforming dynamic logistics demands into processable data entities to ensure the initiation of collaborative control processes. It analyzes cargo sorting tasks, determines sorting sequences, avoids time window conflicts, and ensures the sorting process conforms to the flow characteristics of the logistics chain. Based on cargo characteristics and sorting sequences, it determines an initial collaborative cycle time instruction set, optimizes the robot collaboration plan based on the sorting sequence, and provides a baseline framework for timestamp calculation. Based on the initial collaborative cycle time instruction set, it determines the expected instruction timestamps and sets a baseline time reference for comparing actual execution, ensuring clear expected goals. Based on real-time working status, it determines the actual arrival timestamps, quantifies the real-time impact of equipment and cargo status, and ensures timestamps reflect true logistics dynamics. Based on the expected instruction timestamps and actual arrival timestamps, it determines the collaborative cycle time deviation value, quantifies the degree of cycle time disorder, and resolves the cumulative effect of cycle time interference.

[0009] Optionally, adjusting the coordinated cycle time instruction set based on the coordinated cycle time deviation value and the cargo compensation amount includes: The current transmission speed is determined based on the cargo monitoring data. Determine the maximum allowable sorting interval based on the aforementioned cargo sorting task; The arrival status of the goods is determined based on the compensation amount and the current transmission speed. The collaborative rhythm instruction set is adjusted based on the collaborative rhythm deviation value, the maximum allowable sorting interval, and the arrival status of the goods.

[0010] This solution determines the current conveyor speed based on cargo monitoring data, reflecting the conveyor belt's operating status and quantifying the impact of external disturbances. This reduces cargo position deviations caused by speed changes and lays a reliable data foundation for calculating cargo arrival status. Based on the cargo sorting task, the maximum permissible sorting interval is determined to avoid time window conflicts and ensure the maintenance of the sorting sequence's temporal safety boundaries. Based on cargo compensation and the current conveyor speed, the cargo arrival status is determined, correcting cargo position deviations caused by external disturbances, reducing sorting failure rates, and ensuring that collaborative cycle time adjustments are based on actual logistics conditions, not theoretical expectations. Based on the collaborative cycle time deviation value, the maximum permissible sorting interval, and the cargo arrival status, the collaborative cycle time instruction set is adjusted to optimize the robot's action timing, reduce cycle time interference and conflict risks, and ensure that sorting intervals meet constraints. This improves the synchronization of sorting actions, reduces cargo accumulation or missorting, and enhances overall efficiency and accuracy.

[0011] Optionally, determining the cargo compensation amount based on the cargo characteristics includes: Based on the characteristics of the goods, determine the quality and category of the goods; Based on the current transmission speed, the cargo weight, and the cargo type, determine the cargo offset during the cargo transmission process; The amount of cargo compensation is determined based on the cargo offset.

[0012] This solution determines the quality and category of goods based on their characteristics, ensuring that these characteristics are transformed into actionable variables for calculating cargo offset. Based on the current conveyor speed, cargo quality, and category, the solution determines the cargo offset during transport, quantifying the positional error and providing a direct basis for generating compensation amounts, ensuring accurate offset detection. Based on the cargo offset, the solution determines the compensation amount to correct the deviation between the actual and expected positions of the goods, thereby reducing sorting failures.

[0013] Optionally, adjusting the collaborative cycle time instruction set based on the collaborative cycle time deviation value, the maximum allowable sorting interval, and the goods arrival status includes: Determine the arrival time of the goods based on the arrival status of the goods; Based on the arrival time of the goods and the sorting sequence, potential conflict points are identified; Adjust the conveyor belt speed based on the potential conflict points; Based on the adjusted conveyor belt speed, the collaborative cycle time instruction set is adjusted according to the collaborative cycle time deviation value and the maximum allowable sorting interval.

[0014] This solution determines the arrival time of goods based on their arrival status, avoiding deviations in the overall adjustment process caused by inaccurate arrival status. Based on the arrival time and sorting sequence, potential conflict points are identified, ensuring speed adjustments are specifically designed to mitigate conflict risks. The conveyor belt speed is adjusted according to these potential conflict points to ensure more uniform goods flow and prevent conflict points from amplifying cycle time interference. Based on the adjusted conveyor belt speed, the collaborative cycle time instruction set is adjusted according to the collaborative cycle time deviation value and the maximum permissible sorting interval, achieving dynamic calibration of the action timing and reducing cycle time disorder and incorrect sorting.

[0015] Optionally, identifying potential conflict points based on the arrival time of the goods and the sorting sequence includes: Based on the sorting sequence, extract the interval between two consecutive items; Calculate the time interval between the arrival of two goods at the same sorting point based on the arrival time of the goods and the interval distance. The sorting robot's operation return time is determined based on the initial collaborative rhythm instruction set; If the interval is less than the operation reset time, it is identified as a potential conflict point.

[0016] This solution extracts the interval between two consecutive items based on the sorting sequence, ensuring an objective assessment of item density. Based on item arrival time and interval distance, the time interval between two items arriving at the same sorting point is calculated, quantifying the compactness of the time window and providing key time indicators for conflict detection. The sorting robot's return-to-position time is determined based on the initial cooperative cycle instruction set, ensuring conflict detection is based on consistent robot action timing parameters. If the interval is shorter than the return-to-position time, it is identified as a potential conflict point, identifying high-risk areas in the sorting sequence, thus facilitating optimization to improve reliability.

[0017] Optionally, adjusting the collaborative cycle time instruction set based on the adjusted conveyor belt speed, according to the collaborative cycle time deviation value and the maximum allowable sorting interval, includes: Based on the adjusted conveyor belt speed, and according to the initial cooperative beat instruction set, the expected working state of the rear sorting robot is determined. The beat superposition interference effect is determined based on the coordinated beat deviation value and the expected working state; The coordinated rhythm instruction set is adjusted based on the rhythm superposition interference effect and the maximum allowable sorting interval.

[0018] This solution, based on the adjusted conveyor belt speed and the initial collaborative cycle time instruction set, determines the expected working state of the rear sorting robot. This avoids robot movement misalignment caused by speed changes and ensures that the expected working state of the rear sorting robot reflects the current logistics status. This provides fundamental data for detecting and compensating for cycle time interference, reducing the risk of time window conflicts. Based on the collaborative cycle time deviation value and the expected working state, the solution determines the cycle time superposition interference effect, mitigating system-level disturbances caused by equipment response delays or external disturbances. Based on the cycle time superposition interference effect and the maximum permissible sorting interval, the collaborative cycle time instruction set is adjusted to achieve closed-loop control, real-time response to logistics dynamics, improved sorting accuracy and efficiency, and reduced risk of increased error rates or equipment damage.

[0019] Optionally, determining the coordinated cycle deviation value based on the expected instruction timestamp and the actual arrival timestamp includes: The theoretical start time of the sorting robot is determined based on the expected instruction timestamp; The actual arrival time of the sorting robot is determined based on the actual arrival timestamp. Calculate the difference between the theoretical start time and the actual placement time to obtain the original time deviation; Analyze the characteristics of the goods to determine their mass distribution; Based on the aforementioned cargo mass distribution, determine the inertia compensation coefficient for the sorting action; The coordinated beat deviation value is determined based on the inertia compensation coefficient and the original time deviation.

[0020] This solution determines the theoretical start time of the sorting robot based on the expected instruction timestamp, thereby reducing rhythm disorder caused by static rhythm adjustments in a dynamic logistics environment. Based on the actual arrival timestamp, the actual positioning time of the sorting robot is determined, capturing the actual execution time points and providing real-time feedback to reflect real-world uncertainties, thus accurately quantifying action delays or advances. The difference between the theoretical start time and the actual positioning time is calculated to obtain the original time deviation, quantifying the time difference between the planned and actual execution of sorting actions, identifying initial deviation values, and effectively detecting the accumulation of small deviations. By analyzing cargo characteristics and determining cargo mass distribution, the impact of cargo dynamic characteristics on sorting actions is identified, adapting to the diversity of massive quantities of goods and reducing sorting action mismatches caused by ignoring cargo characteristics. Based on the cargo mass distribution, the inertia compensation coefficient for sorting actions is determined to compensate for time and accuracy deviations, improving adaptability to cargo mass distribution. Based on the inertia compensation coefficient and the original time deviation, the collaborative rhythm deviation value is determined, making the collaborative rhythm deviation value more accurately reflect the true deviation.

[0021] Optionally, the method further includes: Obtain equipment operation data from the sorting robot; The health status of the sorting robot is determined based on the equipment operation data. Determine the joint wear compensation coefficient based on the health status of the equipment; The step of determining the coordinated beat deviation value based on the inertia compensation coefficient and the original time deviation includes: The coordinated beat deviation value is determined based on the joint wear compensation coefficient, the inertia compensation coefficient, and the original time deviation.

[0022] This solution acquires operational data from the sorting robot, capturing dynamic parameter changes during sorting operations. This ensures timely and usable data acquisition, preventing data loss that could lead to ineffective equipment status assessments and supporting the real-time nature of the entire compensation process. Based on the operational data, the robot's health status is determined, reflecting the degree of joint wear. This identifies potential problems and provides an accurate basis for determining the joint wear compensation coefficient, ensuring the compensation calculation is tailored to the robot's actual condition. The joint wear compensation coefficient is determined based on the equipment's health status, correcting for time deviations amplified by wear. This ensures the calculation of the collaborative cycle deviation integrates wear factors, improving the targeted nature of deviation compensation. Based on the joint wear compensation coefficient, inertia compensation coefficient, and original time deviation, the collaborative cycle deviation value is determined, reducing sorting errors and conflicts.

[0023] Secondly, this application provides a multi-robot collaborative control system for industrial control, the system comprising: The status determination module is used to acquire real-time working data of the sorting robot and determine the real-time working status based on the real-time working data. The monitoring and analysis module is used to acquire cargo monitoring data and determine cargo characteristics based on the cargo monitoring data. The deviation analysis module is used to determine the coordinated cycle deviation value based on the real-time working status and the characteristics of the goods. The compensation determination module is used to determine the amount of compensation for the goods based on the characteristics of the goods. The instruction adjustment module is used to adjust the collaborative rhythm instruction set according to the collaborative rhythm deviation value and the cargo compensation amount.

[0024] Optionally, when the deviation analysis module determines the coordinated cycle time deviation value based on the real-time working status and the characteristics of the goods, it is used for: Obtain the goods sorting task; analyze the goods sorting task to determine the sorting sequence; Based on the characteristics of the goods and the sorting sequence, determine the initial collaborative cycle instruction set; Based on the initial coordinated beat instruction set, determine the expected instruction timestamp; Based on the real-time working status, determine the actual arrival timestamp; The coordinated rhythm deviation value is determined based on the expected instruction timestamp and the actual arrival timestamp.

[0025] Optionally, when the instruction adjustment module adjusts the coordinated cycle time instruction set based on the coordinated cycle time deviation value and the cargo compensation amount, it is used for: The current transmission speed is determined based on the cargo monitoring data. Determine the maximum allowable sorting interval based on the aforementioned cargo sorting task; The arrival status of the goods is determined based on the compensation amount and the current transmission speed. The collaborative rhythm instruction set is adjusted based on the collaborative rhythm deviation value, the maximum allowable sorting interval, and the arrival status of the goods.

[0026] Optionally, when the compensation determination module determines the compensation amount for the goods based on the characteristics of the goods, it is used to: Based on the characteristics of the goods, determine the quality and category of the goods; Based on the current transmission speed, the cargo weight, and the cargo type, determine the cargo offset during the cargo transmission process; The amount of cargo compensation is determined based on the cargo offset.

[0027] Optionally, when the instruction adjustment module adjusts the collaborative cycle time instruction set based on the collaborative cycle time deviation value, the maximum allowable sorting interval, and the goods arrival status, it is used to: Determine the arrival time of the goods based on the arrival status of the goods; Based on the arrival time of the goods and the sorting sequence, potential conflict points are identified; Adjust the conveyor belt speed based on the potential conflict points; Based on the adjusted conveyor belt speed, the collaborative cycle time instruction set is adjusted according to the collaborative cycle time deviation value and the maximum allowable sorting interval.

[0028] Optionally, when the instruction adjustment module identifies potential conflict points based on the arrival time of the goods and the sorting sequence, it is used to: Based on the sorting sequence, extract the interval between two consecutive items; Calculate the time interval between the arrival of two goods at the same sorting point based on the arrival time of the goods and the interval distance. The sorting robot's operation return time is determined based on the initial collaborative rhythm instruction set; If the interval is less than the operation reset time, it is identified as a potential conflict point.

[0029] Optionally, when the instruction adjustment module adjusts the collaborative cycle time instruction set based on the adjusted conveyor belt speed, according to the collaborative cycle time deviation value and the maximum allowable sorting interval, it is used for: Based on the adjusted conveyor belt speed, and according to the initial cooperative beat instruction set, the expected working state of the rear sorting robot is determined. The beat superposition interference effect is determined based on the coordinated beat deviation value and the expected working state; The coordinated rhythm instruction set is adjusted based on the rhythm superposition interference effect and the maximum allowable sorting interval.

[0030] Optionally, when the deviation analysis module determines the coordinated cycle deviation value based on the expected instruction timestamp and the actual arrival timestamp, it is used to: The theoretical start time of the sorting robot is determined based on the expected instruction timestamp; The actual arrival time of the sorting robot is determined based on the actual arrival timestamp. Calculate the difference between the theoretical start time and the actual placement time to obtain the original time deviation; Analyze the characteristics of the goods to determine their mass distribution; Based on the aforementioned cargo mass distribution, determine the inertia compensation coefficient for the sorting action; The coordinated beat deviation value is determined based on the inertia compensation coefficient and the original time deviation.

[0031] Optionally, the multi-robot collaborative control system for industrial control further includes a deviation value determination module, used for: Obtain equipment operation data from the sorting robot; The health status of the sorting robot is determined based on the equipment operation data. Determine the joint wear compensation coefficient based on the health status of the equipment; When determining the coordinated beat deviation value based on the inertia compensation coefficient and the original time deviation, it is used for: The coordinated beat deviation value is determined based on the joint wear compensation coefficient, the inertia compensation coefficient, and the original time deviation. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a schematic diagram of an application scenario provided in an embodiment of this application.

[0034] Figure 2 This is a flowchart illustrating a multi-robot collaborative control method for industrial control, provided as an embodiment of this application.

[0035] Figure 3 This is a schematic diagram of a multi-robot collaborative control system for industrial control provided in one embodiment of this application. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, 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, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0037] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0038] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0039] Existing multi-robot collaborative control methods are usually based on fixed work cycles or simple timing rules. When facing dynamic logistics environments, due to the high-speed flow of goods on conveyor belts and the wide variety of products, this method often causes problems in the entire sorting process, and may even lead to equipment damage or loss of goods, which seriously restricts the efficiency and stability of high-throughput sorting scenarios.

[0040] Based on this, this application provides a multi-robot collaborative control method and system for industrial control. It acquires real-time working data of sorting robots, ensuring the capture of their dynamic behavior, thereby supporting real-time monitoring and deviation detection. This avoids decreased motion accuracy due to equipment status uncertainty, providing an immediate and reliable source of raw information for the entire collaborative control process. Based on real-time working data, it determines the real-time working status, dynamically identifies deviations in the sorting robots, mitigates the cumulative effect of cycle time interference, ensures timely response to work cycle disorder, and avoids goods accumulation or robot motion conflicts. It acquires goods monitoring data, ensuring coverage of the fluidity and variability of massive quantities of goods, thereby capturing the real-time status of goods on the conveyor belt, effectively addressing the dynamic impact of goods characteristics, and reducing the risk of sorting failures or goods damage due to goods offset or category differences. Based on goods monitoring data, it determines goods characteristics, adapts to goods diversity, resolves time window conflicts, and avoids robot conflicts or goods omissions caused by mismatched goods characteristics. Based on the real-time working status and goods characteristics, it determines the collaborative cycle time deviation value, effectively compensating for the cumulative effect of cycle time interference, reducing overall throughput decline, and mitigating time window conflict problems. Based on the characteristics of the goods, the compensation amount is determined to address the dynamic impact of these characteristics, reduce the sorting failure rate and the risk of goods damage, and support adaptation to external disturbances. Based on the collaborative cycle time deviation and the compensation amount, the collaborative cycle time instruction set is adjusted to dynamically coordinate the timing of the sorting robot's actions, improving overall efficiency and accuracy, resolving time window conflicts and sorting sequence constraints, reducing cycle time disorder, goods deviation, and equipment conflicts, and ultimately achieving closed-loop adaptive control.

[0041] Figure 1 This is a schematic diagram of an application scenario provided by this application, illustrating the application of the method provided in this application when performing multi-robot collaborative control.

[0042] Specifically, the method provided in this application is applied to any server, where the server interacts with the sorting robot and monitoring equipment to obtain real-time working data of the sorting robot. Based on the real-time working data, the real-time working status is determined. Cargo monitoring data is obtained through the monitoring equipment in the logistics sorting system. Based on the cargo monitoring data, the characteristics of the cargo are determined. Based on the real-time working status and cargo characteristics, the collaborative cycle time deviation value is determined. Based on the cargo characteristics, the cargo compensation amount is determined to address the dynamic impact of cargo characteristics, reduce sorting failure rate and cargo damage risk, and support adaptation to external disturbances. Based on the collaborative cycle time deviation value and cargo compensation amount, the collaborative cycle time instruction set is adjusted to dynamically coordinate the action timing of the sorting robot, improve overall efficiency and accuracy, resolve time window conflicts and sorting sequence constraints, reduce cycle time disorder, cargo offset, and equipment conflicts, and ultimately achieve closed-loop adaptive control.

[0043] For specific implementation details, please refer to the following examples.

[0044] Figure 2 This is a flowchart illustrating a multi-robot collaborative control method for industrial control, provided as an embodiment of this application. The method of this embodiment can be applied to the server in the above scenario. Figure 2 As shown, the method includes: S201. Obtain real-time working data of the sorting robot; determine the real-time working status based on the real-time working data; Real-time operational data can be raw operating parameters collected in real time from the sorting robot's multiple sensors, including action timestamps, position coordinate data, joint angle data, etc.

[0045] Real-time operating status can be the current dynamic operating parameters of the sorting robot.

[0046] Specifically, real-time working data is obtained through multiple sensors built into the sorting robot (such as position sensors, speed sensors, and joint angle sensors). Specifically, position coordinate data is obtained in real time from the position sensors; action timestamp data is obtained in real time from the speed sensors; and joint angle data is obtained from the joint angle sensors.

[0047] Based on real-time operational data, a rule engine (used to convert sensor data (position, timestamp, joint angle) into operable real-time operational data) is applied to analyze the real-time operational data. Specifically, the current position of the sorting robot is extracted from the position coordinate data; the start and end times of the actual actions are calculated from the action timestamp data; and the movement direction or return status of the sorting robot (such as whether the reset is complete) is inferred from the joint angle data.

[0048] S202. Obtain cargo monitoring data; determine cargo characteristics based on cargo monitoring data; Cargo monitoring data can be raw cargo information collected from monitoring equipment in the logistics sorting system, including cargo quality, cargo category, cargo location offset, and arrival time.

[0049] Cargo characteristics can be inherent attributes of the cargo, including quality grade, fragility, and offset.

[0050] Specifically, the monitoring equipment in the logistics sorting system continuously scans or photographs the surface of goods to obtain goods monitoring data (such as material, size, and shape). The goods monitoring data is analyzed using predefined characteristic extraction rules (a set of logical judgment conditions stored in the sorting system) to determine the characteristics of the goods (such as fragile items, oversized items). For example, if the goods monitoring data shows that the goods are made of glass, then the goods are determined to be fragile.

[0051] S203. Determine the coordination cycle deviation value based on the real-time working status and cargo characteristics; The collaborative cycle deviation value can be used to quantify the difference in timing deviations during collaborative robot work.

[0052] Specifically, based on the action timestamp difference in the real-time working state (the difference between the actual start / end time of the action and the theoretical planned time (the ideal start or end time of the action, such as the delay time) and the equipment status parameters (quantitative coefficients that characterize the degree of physical wear and tear of the sorting robot, such as the wear coefficient), the individual deviation value of the sorting robot (the timing offset caused by the action timestamp difference) is calculated.

[0053] Based on the cargo category (the category identifier of cargo according to physical characteristics) and offset value (the positional offset distance of cargo during the transmission process), the dynamic impact factor of cargo is evaluated (the operation time correction coefficient generated by the combined effect of cargo category and offset value, such as fragile items increasing operation time, and heavy objects causing inertial delay). Then, the individual deviation value and the dynamic impact factor are fused with a weighted average algorithm to determine the coordinated cycle deviation value.

[0054] S204. Determine the compensation amount for the goods based on their characteristics; The cargo compensation amount can be a compensation value used to correct motion parameters.

[0055] Specifically, based on the characteristics of the goods, a compensation amount is generated using a compensation mapping algorithm defined by industry standards (used to map the characteristics of the goods (quality characteristics, goods category) to the corresponding compensation amount (time compensation amount, force compensation amount)). For example, based on the quality characteristics of the goods, it is mapped to the motion time compensation amount (such as increasing the grasping motion time for heavy objects and decreasing the time for light objects); based on the characteristics of the goods category, it is mapped to the motion force compensation amount (such as reducing the grasping force of the robotic arm for fragile items).

[0056] S205. Adjust the coordinated cycle time instruction set based on the coordinated cycle time deviation value and the cargo compensation amount.

[0057] The coordinated cycle instruction set can be a set of preset control commands used to coordinate the timing of actions of multiple sorting robots.

[0058] Specifically, based on the collaborative cycle deviation value, the action timing is adjusted (the real-time time arrangement sequence of the sorting robot's collaborative actions; if the deviation value is positive, the sorting robot's start time is advanced to compensate for the delay); based on the cargo compensation amount, the action parameters are updated (the set of physical operation variables required for the sorting robot to perform a single action, such as extending or shortening the action duration by applying time compensation); and then, based on the adjusted action timing and the updated action parameters, the collaborative cycle instruction set is adjusted.

[0059] This solution acquires real-time operational data from the sorting robot, ensuring the capture of its dynamic behavior. This supports real-time monitoring and deviation detection, preventing decreased motion accuracy due to equipment status uncertainties and providing an immediate and reliable source of raw information for the entire collaborative control process. Based on the real-time operational data, the solution determines the real-time operational status, dynamically identifies robot deviations, mitigates the cumulative effect of cycle time interference, ensures timely response to work cycle disorder, and avoids goods accumulation or robot motion conflicts. It also acquires goods monitoring data, ensuring coverage of the fluidity and variability of massive quantities of goods, thereby capturing the real-time status of goods on the conveyor belt. This effectively addresses the dynamic impact of goods characteristics, reducing the risk of sorting failures or goods damage due to goods offset or category differences. Based on the goods monitoring data, the solution determines goods characteristics, adapts to goods diversity, resolves time window conflicts, and avoids robot conflicts or goods omissions caused by mismatched goods characteristics. Finally, based on the real-time operational status and goods characteristics, the solution determines the collaborative cycle time deviation value, effectively compensating for the cumulative effect of cycle time interference, reducing overall throughput decline, and mitigating time window conflict issues. Based on the characteristics of the goods, the compensation amount is determined to address the dynamic impact of these characteristics, reduce the sorting failure rate and the risk of goods damage, and support adaptation to external disturbances. Based on the collaborative cycle time deviation and the compensation amount, the collaborative cycle time instruction set is adjusted to dynamically coordinate the timing of the sorting robot's actions, improving overall efficiency and accuracy, resolving time window conflicts and sorting sequence constraints, reducing cycle time disorder, goods deviation, and equipment conflicts, and ultimately achieving closed-loop adaptive control.

[0060] In some embodiments, a goods sorting task is obtained; the goods sorting task is analyzed to determine the sorting sequence; an initial collaborative cycle time instruction set is determined based on the goods characteristics and the sorting sequence; the expected instruction timestamp is determined based on the initial collaborative cycle time instruction set; the actual arrival timestamp is determined based on the real-time working status; and the collaborative cycle time deviation value is determined based on the expected instruction timestamp and the actual arrival timestamp.

[0061] Goods sorting tasks can be the goods handling operations that need to be performed.

[0062] A sorting sequence can be a queue order in which goods are sorted sequentially.

[0063] The initial coordinated cycle instruction set can be the initial control commands used to coordinate the timing of actions of multiple sorting robots.

[0064] The expected instruction timestamp can be the theoretical time point at which the robot action occurs, calculated from the initial coordinated beat instruction set.

[0065] The actual arrival timestamp can be the time when the goods actually arrive at the sorting point, calculated based on the real-time working status.

[0066] Specifically, the sorting tasks are obtained through the logistics management system. The sorting tasks are analyzed to extract the arrival timestamp of each item; a first-in-first-out (FIFO) sorting algorithm is applied to sort the items according to their arrival timestamps to determine the sorting sequence.

[0067] Based on the sorting sequence, assign robot sorting actions to each item (the specific operations performed by the sorting robot during the sorting process, such as gripping, moving, or placing the item); then dynamically adjust the action parameters based on the item characteristics (such as mapping quality characteristics to action duration compensation, and mapping category characteristics to gripping force compensation) to determine the initial collaborative cycle instruction set.

[0068] The initial coordinated cycle instruction set is traversed, and the expected start and end timestamps for each action are calculated sequentially. These timestamps are then stored as expected instruction timestamps. Based on the conveyor belt speed (the conveyor belt's running speed parameter) in the real-time working state, the actual arrival time of the goods at the sorting point is determined, i.e., the actual arrival timestamp. If goods deviate (e.g., due to conveyor belt fluctuations causing positional deviations), the time calculation is adjusted. For example, the actual arrival timestamp is estimated based on the offset distance and conveyor speed.

[0069] The difference between the expected instruction timestamp and the actual arrival timestamp is calculated as the coordinated cycle deviation value.

[0070] This solution acquires cargo sorting tasks, transforming dynamic logistics demands into processable data entities to ensure the initiation of collaborative control processes. It analyzes cargo sorting tasks, determines sorting sequences, avoids time window conflicts, and ensures the sorting process conforms to the flow characteristics of the logistics chain. Based on cargo characteristics and sorting sequences, it determines an initial collaborative cycle time instruction set, optimizes the robot collaboration plan based on the sorting sequence, and provides a baseline framework for timestamp calculation. Based on the initial collaborative cycle time instruction set, it determines the expected instruction timestamps and sets a baseline time reference for comparing actual execution, ensuring clear expected goals. Based on real-time working status, it determines the actual arrival timestamps, quantifies the real-time impact of equipment and cargo status, and ensures timestamps reflect true logistics dynamics. Based on the expected instruction timestamps and actual arrival timestamps, it determines the collaborative cycle time deviation value, quantifies the degree of cycle time disorder, and resolves the cumulative effect of cycle time interference.

[0071] In some embodiments, the current conveying speed is determined based on cargo monitoring data; the maximum allowable sorting interval is determined based on cargo sorting tasks; the cargo arrival status is determined based on cargo compensation amount and current conveying speed; and the collaborative takt instruction set is adjusted based on collaborative takt deviation value, maximum allowable sorting interval, and cargo arrival status.

[0072] The current conveyor speed can be the conveyor belt operating speed value that is parsed in real time from cargo monitoring data.

[0073] The maximum permissible sorting interval can be a time threshold representing the minimum time interval to be tolerated, determined based on the goods sorting task.

[0074] The arrival status of goods can be calculated by combining the compensation amount of goods and the current transmission speed, resulting in a corrected actual arrival timestamp.

[0075] Specifically, the conveyor belt speed parameters are directly extracted from the cargo monitoring data as the current conveyor speed. The sorting sequence in the cargo sorting task is analyzed to calculate the theoretical interval between two consecutive cargo arriving at the same sorting port. Combined with the minimum return time of the sorting robot (the shortest time required for the sorting robot to return to its initial position after completing one sorting action) determined by the equipment performance parameters, the maximum allowable sorting interval is generated.

[0076] Based on the cargo compensation amount and the current conveyor speed, the real-time position of the cargo on the conveyor belt and the expected arrival time at the sorting gate are calculated as the cargo arrival status. Each sorting action instruction in the collaborative rhythm instruction set is iterated through, and the expected timestamp of the action is superimposed with the collaborative rhythm deviation value (if the deviation value is positive, the action is delayed; if negative, the action is advanced). Then, it is checked whether the sorting action interval between adjacent cargoes (two cargoes consecutively arranged in the sorting sequence) is less than the maximum allowable sorting interval: if the interval is insufficient, the action timestamp of the next cargo is delayed until the minimum interval requirement is met. Based on the cargo arrival status, the trigger timestamp of the corresponding sorting action (the time when the sorting action starts, ensuring that the action execution time (the start time of the sorting action) matches the actual arrival time of the cargo (the precise time when it actually arrives at the sorting gate)). The above action timestamps and trigger timestamps are integrated to update the collaborative rhythm instruction set.

[0077] This solution determines the current conveyor speed based on cargo monitoring data, reflecting the conveyor belt's operating status and quantifying the impact of external disturbances. This reduces cargo position deviations caused by speed changes and lays a reliable data foundation for calculating cargo arrival status. Based on the cargo sorting task, the maximum permissible sorting interval is determined to avoid time window conflicts and ensure the maintenance of the sorting sequence's temporal safety boundaries. Based on cargo compensation and the current conveyor speed, the cargo arrival status is determined, correcting cargo position deviations caused by external disturbances, reducing sorting failure rates, and ensuring that collaborative cycle time adjustments are based on actual logistics conditions, not theoretical expectations. Based on the collaborative cycle time deviation value, the maximum permissible sorting interval, and the cargo arrival status, the collaborative cycle time instruction set is adjusted to optimize the robot's action timing, reduce cycle time interference and conflict risks, and ensure that sorting intervals meet constraints. This improves the synchronization of sorting actions, reduces cargo accumulation or missorting, and enhances overall efficiency and accuracy.

[0078] In some embodiments, the cargo quality and cargo category are determined based on cargo characteristics; the cargo offset during the cargo transmission process is determined based on the current transmission speed, cargo quality, and cargo category; and the cargo compensation amount is determined based on the cargo offset.

[0079] Goods quality can be the weight attribute value of the goods.

[0080] Goods category can be a type classification identifier for goods.

[0081] Cargo offset can be the distance by which cargo deviates from its intended position during transport.

[0082] Specifically, the weight value in the cargo characteristics is analyzed to determine the cargo quality; at the same time, the classification label (a label used to distinguish the types of goods) in the cargo characteristics is analyzed to determine the cargo category (such as fragile items, heavy items, and lightweight items).

[0083] The tendency to shift position (the potential strength of the positional shift of goods during transport) is determined based on the type of goods. For example, lightweight goods are more susceptible to conveyor belt vibration and thus have a higher tendency to shift position; heavy or fragile goods are less susceptible. The degree of shift (the quantified distance of the shift) is then quantified by the weight of the goods. For instance, heavier goods (due to inertial stability) have a smaller shift, while lighter goods (due to susceptibility to external disturbances) have a larger shift. Furthermore, the current conveyor speed is used to adjust the shift. Higher conveyor speeds result in larger shifts (speed fluctuations amplify the drift effect), while lower speeds result in smaller shifts.

[0084] The cargo offset during the cargo transfer process is determined by linearly superimposing the offset tendency, offset degree, and adjusted offset amount. The cargo offset amount is then directly converted into a cargo compensation amount, which serves as a correction parameter whose value is inversely proportional to the cargo offset amount (i.e., used for reverse compensation of positional deviation).

[0085] This solution determines the quality and category of goods based on their characteristics, ensuring that these characteristics are transformed into actionable variables for calculating cargo offset. Based on the current conveyor speed, cargo quality, and category, the solution determines the cargo offset during transport, quantifying the positional error and providing a direct basis for generating compensation amounts, ensuring accurate offset detection. Based on the cargo offset, the solution determines the compensation amount to correct the deviation between the actual and expected positions of the goods, thereby reducing sorting failures.

[0086] In some embodiments, the arrival time of goods is determined based on the arrival status of the goods; potential conflict points are identified based on the arrival time of the goods and the sorting sequence; the conveyor belt speed is adjusted based on the potential conflict points; and the collaborative takt instruction set is adjusted based on the adjusted conveyor belt speed, according to the collaborative takt deviation value and the maximum allowable sorting interval.

[0087] The arrival time of goods can be the estimated time when the goods arrive at the sorting point.

[0088] Potential conflict points can be locations in the sorting sequence where robot motion conflicts or goods omissions may occur when the time interval between the arrival of goods in each adjacent goods pair is less than the maximum allowable sorting interval.

[0089] Conveyor belt speed refers to the operating speed of the conveyor belt, used to control the flow rate of goods on the conveyor belt.

[0090] Specifically, the arrival status of the goods is analyzed, and the current position of the goods on the conveyor belt and the current speed of the conveyor belt are extracted; the remaining distance between the current position of the goods and the sorting port is calculated; the quotient of the remaining distance and the current speed of the conveyor belt (remaining distance divided by the current speed of the conveyor belt) is taken as the arrival time of the goods.

[0091] Based on the arrival time of the goods, iterate through each adjacent pair of goods in the sorting sequence (such as the nth and (n+1)th goods in the sequence); calculate the arrival time interval of each adjacent pair of goods (i.e., the arrival time of the (n+1)th goods minus the arrival time of the nth goods); then compare the arrival time interval with the maximum allowable sorting interval, and mark it as a potential conflict point if the arrival time interval is less than the maximum allowable sorting interval.

[0092] Based on potential conflict points, the conveyor belt speed is dynamically adjusted. For example, if a potential conflict point exists (the time interval is too short), the conveyor belt speed is reduced to increase the interval between goods (making the time interval between goods arrivals longer); if there is no potential conflict point, the conveyor belt speed is maintained or slightly optimized to improve efficiency.

[0093] Based on the adjusted conveyor belt speed, the arrival time of the goods is recalculated. Then, using the recalculated arrival time, the action timestamps of each robot in the collaborative beat instruction set are updated (e.g., the time point of the grasping command is adjusted to the new arrival time of the goods). Furthermore, by combining the collaborative beat deviation value with the maximum allowable sorting interval, the time deviation of the collaborative beat instruction set is corrected. For example, if the collaborative beat deviation value is positive, it indicates that the action is delayed, so the instruction time is advanced; if the collaborative beat deviation value is negative, it indicates that the action is advanced, so the instruction time is delayed.

[0094] This solution determines the arrival time of goods based on their arrival status, avoiding deviations in the overall adjustment process caused by inaccurate arrival status. Based on the arrival time and sorting sequence, potential conflict points are identified, ensuring speed adjustments are specifically designed to mitigate conflict risks. The conveyor belt speed is adjusted according to these potential conflict points to ensure more uniform goods flow and prevent conflict points from amplifying cycle time interference. Based on the adjusted conveyor belt speed, the collaborative cycle time instruction set is adjusted according to the collaborative cycle time deviation value and the maximum permissible sorting interval, achieving dynamic calibration of the action timing and reducing cycle time disorder and incorrect sorting.

[0095] In some embodiments, the interval distance between two consecutive goods is extracted according to the sorting sequence; the interval time between the arrival time of the goods and the interval distance is calculated; the operation return time of the sorting robot is determined according to the initial cooperative rhythm instruction set; if the interval time is less than the operation return time, it is identified as a potential conflict point.

[0096] The interval distance can be the physical distance between two consecutive goods.

[0097] The sorting port can be a designated location where a sorting robot performs identification, grasping, and sorting actions.

[0098] The interval can be the time interval between two consecutive shipments arriving at the same sorting point.

[0099] Operation return time can be the minimum time required for a sorting robot to return to its initial position after completing a sorting action.

[0100] Specifically, each consecutive pair of goods is sequentially traversed from the sorting sequence. Then, for each consecutive pair of goods, the interval distance between the two consecutive goods is measured and extracted in real time using a laser rangefinder that must cover the conveyor belt area upstream of the sorting port. Based on each pair of consecutive goods, the time difference is calculated according to the arrival time of the goods and the interval distance, which is the time interval between the arrival of two goods at the same sorting port (the arrival time of goods n+1 - the arrival time of goods n).

[0101] The initial coordinated cycle instruction set is parsed, and the operation return field in the sorting robot's motion timing parameters (sorting robot motion time planning data, used to define the time thresholds for the robot to perform sorting, return, and other operations) is extracted to determine the sorting robot's operation return time. The interval duration is compared with the operation return time. If the interval duration is less than the operation return time, its position in the sorting sequence (e.g., the point between sequence n and n+1) is marked as a potential conflict point.

[0102] This solution extracts the interval between two consecutive items based on the sorting sequence, ensuring an objective assessment of item density. Based on item arrival time and interval distance, the time interval between two items arriving at the same sorting point is calculated, quantifying the compactness of the time window and providing key time indicators for conflict detection. The sorting robot's return-to-position time is determined based on the initial cooperative cycle instruction set, ensuring conflict detection is based on consistent robot action timing parameters. If the interval is shorter than the return-to-position time, it is identified as a potential conflict point, identifying high-risk areas in the sorting sequence, thus facilitating optimization to improve reliability.

[0103] In some embodiments, based on the adjusted conveyor belt speed and according to the initial cooperative rhythm instruction set, the expected working state of the rear sorting robot is determined; the rhythm superposition interference effect is determined based on the cooperative rhythm deviation value and the expected working state; and the cooperative rhythm instruction set is adjusted based on the rhythm superposition interference effect and the maximum allowable sorting interval.

[0104] A post-sorting robot can be a sorting robot located at the end of the sorting sequence.

[0105] The expected working state can be the expected state of the sorting robot's operation.

[0106] The beat superposition interference effect can be the delayed amplification caused by the propagation of the coordinated beat deviation value.

[0107] Specifically, the initial collaborative cycle instruction set is read, and the timing parameters of the actions for the rear sorting robot (including fields such as action start time and duration) are extracted. Then, using the adjusted conveyor belt speed, combined with the conveyor belt length (the physical distance from the start point of the conveyor belt (such as the goods entry point) to the corresponding sorting port of the rear sorting robot) and the sorting port position (the physical coordinates of the sorting point of the rear sorting robot on the conveyor belt), the expected time for the goods to arrive at the corresponding sorting port of the rear sorting robot is calculated. Based on the expected time and the timing parameters of the actions, the expected working state of the rear sorting robot is determined.

[0108] The collaborative cycle deviation value is superimposed on the expected working state of the rear sorting robot to form the adjusted expected working state; then the deviation value between the adjusted expected working state and the conveyor belt speed is calculated to evaluate the deviation transmission effect (the degree of influence of the collaborative cycle deviation value on the expected working state of the rear sorting robot) and determine the cycle superposition interference effect.

[0109] The clockwise interference effect is compared with the maximum allowable sorting interval. If the clockwise interference effect is less than or equal to the maximum allowable sorting interval, only the time parameters in the initial collaborative clockwise instruction set are finely adjusted (e.g., the sorting start time of the rear sorting robot is advanced by half the clockwise interference effect value). If the clockwise interference effect is greater than the maximum allowable sorting interval, conflict avoidance adjustment is performed, the duration of the rear sorting robot's action is reduced (e.g., the reset time is shortened), and the collaborative clockwise instruction set is reallocated to balance the time margin (e.g., the sorting start time of the rear robot is delayed).

[0110] This solution, based on the adjusted conveyor belt speed and the initial collaborative cycle time instruction set, determines the expected working state of the rear sorting robot. This avoids robot movement misalignment caused by speed changes and ensures that the expected working state of the rear sorting robot reflects the current logistics status. This provides fundamental data for detecting and compensating for cycle time interference, reducing the risk of time window conflicts. Based on the collaborative cycle time deviation value and the expected working state, the solution determines the cycle time superposition interference effect, mitigating system-level disturbances caused by equipment response delays or external disturbances. Based on the cycle time superposition interference effect and the maximum permissible sorting interval, the collaborative cycle time instruction set is adjusted to achieve closed-loop control, real-time response to logistics dynamics, improved sorting accuracy and efficiency, and reduced risk of increased error rates or equipment damage.

[0111] In some embodiments, the theoretical start time of the sorting robot is determined based on the expected instruction timestamp; the actual placement time of the sorting robot is determined based on the actual placement timestamp; the difference between the theoretical start time and the actual placement time is calculated to obtain the original time deviation; the characteristics of the goods are analyzed to determine the quality distribution of the goods; the inertia compensation coefficient of the sorting action is determined based on the quality distribution of the goods; and the cooperative cycle deviation value is determined based on the inertia compensation coefficient and the original time deviation.

[0112] The theoretical start time of a sorting robot can be the start time of the actions planned for the sorting robot.

[0113] The actual positioning time of the sorting robot can be the actual time it takes for the sorting robot to complete its actions.

[0114] The original time deviation can be the amount of time deviation between the expected start time of the action and the actual start time of the action.

[0115] Cargo mass distribution can be a characteristic representing the spatial distribution of cargo mass.

[0116] Sorting can be an operation performed by a sorting robot, including tasks such as identifying, grasping, and sorting goods.

[0117] The inertia compensation coefficient can be a parameter used to compensate for the impact of cargo inertia on sorting operations.

[0118] Specifically, the expected instruction timestamp is directly used as the theoretical start time of the sorting robot (i.e., the planned start time of the action). The actual arrival timestamp is directly used as the actual arrival time of the sorting robot (i.e., the actual completion time of the action).

[0119] A subtraction algorithm is used to calculate the time difference between the theoretical start time and the actual placement time; then, based on the calculation results, the original time deviation is determined (positive values ​​indicate delay, negative values ​​indicate advancement). Cargo characteristics are analyzed to extract the cargo's mass distribution (such as size and center of gravity).

[0120] Based on the cargo mass distribution, the pre-set compensation coefficient table (which stores coefficient values ​​corresponding to different cargo mass distributions and is used to determine the inertia compensation coefficient) established based on experimental calibration is consulted to determine the inertia compensation coefficient for the sorting action. The inertia compensation coefficient is then multiplied by the original time deviation to obtain the adjusted time deviation value, which is the coordinated cycle time deviation value.

[0121] This solution determines the theoretical start time of the sorting robot based on the expected instruction timestamp, thereby reducing rhythm disorder caused by static rhythm adjustments in a dynamic logistics environment. Based on the actual arrival timestamp, the actual positioning time of the sorting robot is determined, capturing the actual execution time points and providing real-time feedback to reflect real-world uncertainties, thus accurately quantifying action delays or advances. The difference between the theoretical start time and the actual positioning time is calculated to obtain the original time deviation, quantifying the time difference between the planned and actual execution of sorting actions, identifying initial deviation values, and effectively detecting the accumulation of small deviations. By analyzing cargo characteristics and determining cargo mass distribution, the impact of cargo dynamic characteristics on sorting actions is identified, adapting to the diversity of massive quantities of goods and reducing sorting action mismatches caused by ignoring cargo characteristics. Based on the cargo mass distribution, the inertia compensation coefficient for sorting actions is determined to compensate for time and accuracy deviations, improving adaptability to cargo mass distribution. Based on the inertia compensation coefficient and the original time deviation, the collaborative rhythm deviation value is determined, making the collaborative rhythm deviation value more accurately reflect the true deviation.

[0122] In some embodiments, the equipment operation data of the sorting robot is acquired; the equipment health status of the sorting robot is determined based on the equipment operation data; the joint wear compensation coefficient is determined based on the equipment health status; and the cooperative cycle deviation value is determined based on the joint wear compensation coefficient, the inertia compensation coefficient, and the original time deviation.

[0123] Equipment operation data can be used to characterize the real-time operating status of the sorting robot during the sorting process. Equipment health status can be a quantitative result of the wear and tear on the joints of the sorting robot. The joint wear compensation coefficient can be a numerical parameter used to quantify the impact of joint wear on the sorting robot's movements.

[0124] Specifically, the sorting robot collects real-time equipment operation data (such as the degree of joint wear (e.g., percentage of wear)) through joint wear sensors installed on it. The sorting robot's health status (e.g., normal, light wear, moderate wear, severe wear) is determined by comparing the equipment operation data with a preset health status threshold set based on experimental data (used to compare equipment operation data to determine the sorting robot's health status).

[0125] Based on the equipment's health status, a preset joint wear compensation coefficient table (which stores the mapping relationship between different health statuses and joint wear compensation coefficients, used to determine the joint wear compensation coefficients) is consulted to determine the joint wear compensation coefficients. The original time deviation is then multiplied by the joint wear compensation coefficient and the inertia compensation coefficient; based on the calculation result, the coordinated beat deviation value is determined.

[0126] This solution acquires operational data from the sorting robot, capturing dynamic parameter changes during sorting operations. This ensures timely and usable data acquisition, preventing data loss that could lead to ineffective equipment status assessments and supporting the real-time nature of the entire compensation process. Based on the operational data, the robot's health status is determined, reflecting the degree of joint wear. This identifies potential problems and provides an accurate basis for determining the joint wear compensation coefficient, ensuring the compensation calculation is tailored to the robot's actual condition. The joint wear compensation coefficient is determined based on the equipment's health status, correcting for time deviations amplified by wear. This ensures the calculation of the collaborative cycle deviation integrates wear factors, improving the targeted nature of deviation compensation. Based on the joint wear compensation coefficient, inertia compensation coefficient, and original time deviation, the collaborative cycle deviation value is determined, reducing sorting errors and conflicts.

[0127] Figure 3 A schematic diagram of a multi-robot collaborative control system for industrial control is provided as an embodiment of this application, as shown below. Figure 3 As shown, the multi-robot collaborative control system 300 for industrial control in this embodiment includes: a state determination module 301, a monitoring and analysis module 302, a deviation analysis module 303, a compensation determination module 304, and an instruction adjustment module 305.

[0128] The status determination module 301 is used to acquire real-time working data of the sorting robot and determine the real-time working status based on the real-time working data. The monitoring and analysis module 302 is used to acquire cargo monitoring data and determine cargo characteristics based on the cargo monitoring data. Deviation analysis module 303 is used to determine the coordinated cycle deviation value based on the real-time working status and the characteristics of the goods; The compensation determination module 304 is used to determine the compensation amount for the goods based on the characteristics of the goods. The instruction adjustment module 305 is used to adjust the collaborative rhythm instruction set according to the collaborative rhythm deviation value and the cargo compensation amount.

[0129] Optionally, when the deviation analysis module 303 determines the collaborative cycle time deviation value based on the real-time working status and the characteristics of the goods, it is used to: acquire the goods sorting task; analyze the goods sorting task and determine the sorting sequence; determine the initial collaborative cycle time instruction set based on the characteristics of the goods and the sorting sequence; determine the expected instruction timestamp based on the initial collaborative cycle time instruction set; determine the actual arrival timestamp based on the real-time working status; and determine the collaborative cycle time deviation value based on the expected instruction timestamp and the actual arrival timestamp.

[0130] Optionally, when the instruction adjustment module 305 adjusts the collaborative rhythm instruction set according to the collaborative rhythm deviation value and the cargo compensation amount, it is used to: determine the current conveying speed according to the cargo monitoring data; determine the maximum allowable sorting interval according to the cargo sorting task; determine the cargo arrival status according to the cargo compensation amount and the current conveying speed; and adjust the collaborative rhythm instruction set according to the collaborative rhythm deviation value, the maximum allowable sorting interval, and the cargo arrival status.

[0131] Optionally, when the compensation determination module 304 determines the cargo compensation amount based on the cargo characteristics, it is used to: determine the cargo quality and cargo category based on the cargo characteristics; determine the cargo offset during the cargo transmission process based on the current transmission speed, the cargo quality and cargo category; and determine the cargo compensation amount based on the cargo offset.

[0132] Optionally, when the instruction adjustment module 305 adjusts the collaborative rhythm instruction set according to the collaborative rhythm deviation value, the maximum allowable sorting interval, and the goods arrival status, it is used to: determine the goods arrival time according to the goods arrival status; identify potential conflict points according to the goods arrival time and the sorting sequence; adjust the conveyor belt speed according to the potential conflict points; and adjust the collaborative rhythm instruction set based on the adjusted conveyor belt speed, according to the collaborative rhythm deviation value and the maximum allowable sorting interval.

[0133] Optionally, when the instruction adjustment module 305 identifies potential conflict points based on the arrival time of the goods and the sorting sequence, it is used to: extract the interval distance between two consecutive goods based on the sorting sequence; calculate the interval duration between the arrival time of the goods and the interval distance; determine the operation return time of the sorting robot based on the initial collaborative rhythm instruction set; if the interval duration is less than the operation return time, it is determined as a potential conflict point.

[0134] Optionally, when the instruction adjustment module 305 adjusts the collaborative rhythm instruction set based on the adjusted conveyor belt speed, according to the collaborative rhythm deviation value and the maximum allowable sorting interval, it is used to: determine the expected working state of the rear sorting robot based on the adjusted conveyor belt speed and the initial collaborative rhythm instruction set; determine the rhythm superposition interference effect based on the collaborative rhythm deviation value and the expected working state; and adjust the collaborative rhythm instruction set based on the rhythm superposition interference effect and the maximum allowable sorting interval.

[0135] Optionally, when the deviation analysis module 303 determines the collaborative cycle deviation value based on the expected instruction timestamp and the actual arrival timestamp, it is used to: determine the theoretical start time of the sorting robot based on the expected instruction timestamp; determine the actual arrival time of the sorting robot based on the actual arrival timestamp; calculate the difference between the theoretical start time and the actual arrival time to obtain the original time deviation; analyze the characteristics of the goods to determine the quality distribution of the goods; determine the inertia compensation coefficient of the sorting action based on the quality distribution of the goods; and determine the collaborative cycle deviation value based on the inertia compensation coefficient and the original time deviation.

[0136] Optionally, the multi-robot collaborative control system for industrial control further includes a deviation value determination module 306, used for: acquiring equipment operation data of the sorting robot; determining the equipment health status of the sorting robot based on the equipment operation data; determining the joint wear compensation coefficient based on the equipment health status; and when determining the collaborative cycle deviation value based on the inertia compensation coefficient and the original time deviation, the module is used for: determining the collaborative cycle deviation value based on the joint wear compensation coefficient, the inertia compensation coefficient, and the original time deviation.

[0137] The system in this embodiment can be used to execute the methods of any of the above embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

Claims

1. A multi-robot cooperative control method for industrial control, characterized in that, include: Obtain real-time working data of the sorting robot; The real-time working status is determined based on the real-time working data; Obtain cargo monitoring data; Based on the cargo monitoring data, the characteristics of the cargo are determined; Based on the real-time working status and the characteristics of the goods, determine the cooperative cycle deviation value; The compensation amount for the goods shall be determined based on the characteristics of the goods. The coordinated rhythm instruction set is adjusted based on the coordinated rhythm deviation value and the cargo compensation amount.

2. The method according to claim 1, characterized in that, The step of determining the coordinated cycle deviation value based on the real-time working status and the characteristics of the goods includes: Obtain the goods sorting task; analyze the goods sorting task to determine the sorting sequence; Based on the characteristics of the goods and the sorting sequence, determine the initial collaborative cycle instruction set; Based on the initial coordinated beat instruction set, determine the expected instruction timestamp; Based on the real-time working status, determine the actual arrival timestamp; The coordinated rhythm deviation value is determined based on the expected instruction timestamp and the actual arrival timestamp.

3. The method according to claim 2, characterized in that, The step of adjusting the coordinated rhythm instruction set based on the coordinated rhythm deviation value and the cargo compensation amount includes: The current transmission speed is determined based on the cargo monitoring data. Determine the maximum allowable sorting interval based on the aforementioned cargo sorting task; The arrival status of the goods is determined based on the compensation amount and the current transmission speed. The collaborative rhythm instruction set is adjusted based on the collaborative rhythm deviation value, the maximum allowable sorting interval, and the arrival status of the goods.

4. The method according to claim 3, characterized in that, Determining the compensation amount based on the characteristics of the goods includes: Based on the characteristics of the goods, determine the quality and category of the goods; Based on the current transmission speed, the cargo weight, and the cargo type, determine the cargo offset during the cargo transmission process; The amount of cargo compensation is determined based on the cargo offset.

5. The method according to claim 3, characterized in that, The step of adjusting the collaborative cycle time instruction set based on the collaborative cycle time deviation value, the maximum allowable sorting interval, and the arrival status of the goods includes: Determine the arrival time of the goods based on the arrival status of the goods; Based on the arrival time of the goods and the sorting sequence, potential conflict points are identified; Adjust the conveyor belt speed based on the potential conflict points; Based on the adjusted conveyor belt speed, the collaborative cycle time instruction set is adjusted according to the collaborative cycle time deviation value and the maximum allowable sorting interval.

6. The method according to claim 5, characterized in that, The step of identifying potential conflict points based on the arrival time of the goods and the sorting sequence includes: Based on the sorting sequence, extract the interval between two consecutive items; Calculate the time interval between the arrival of two goods at the same sorting point based on the arrival time of the goods and the interval distance. The sorting robot's operation return time is determined based on the initial collaborative rhythm instruction set; If the interval is less than the operation reset time, it is identified as a potential conflict point.

7. The method according to claim 5, characterized in that, The adjustment of the collaborative cycle time instruction set based on the adjusted conveyor belt speed, according to the collaborative cycle time deviation value and the maximum allowable sorting interval, includes: Based on the adjusted conveyor belt speed, and according to the initial cooperative beat instruction set, the expected working state of the rear sorting robot is determined. The beat superposition interference effect is determined based on the coordinated beat deviation value and the expected working state; The coordinated rhythm instruction set is adjusted based on the rhythm superposition interference effect and the maximum allowable sorting interval.

8. The method according to claim 2, characterized in that, The step of determining the coordinated cycle deviation value based on the expected instruction timestamp and the actual arrival timestamp includes: The theoretical start time of the sorting robot is determined based on the expected instruction timestamp; The actual arrival time of the sorting robot is determined based on the actual arrival timestamp. Calculate the difference between the theoretical start time and the actual placement time to obtain the original time deviation; Analyze the characteristics of the goods to determine their mass distribution; Based on the aforementioned cargo mass distribution, determine the inertia compensation coefficient for the sorting action; The coordinated beat deviation value is determined based on the inertia compensation coefficient and the original time deviation.

9. The method according to claim 8, characterized in that, The method further includes: Obtain equipment operation data from the sorting robot; The health status of the sorting robot is determined based on the equipment operation data. Determine the joint wear compensation coefficient based on the health status of the equipment; The step of determining the coordinated beat deviation value based on the inertia compensation coefficient and the original time deviation includes: The coordinated beat deviation value is determined based on the joint wear compensation coefficient, the inertia compensation coefficient, and the original time deviation.

10. A multi-robot collaborative control system for industrial control, characterized in that, The method applied to any one of claims 1-9 includes: The status determination module is used to acquire real-time working data of the sorting robot and determine the real-time working status based on the real-time working data. The monitoring and analysis module is used to acquire cargo monitoring data and determine cargo characteristics based on the cargo monitoring data. The deviation analysis module is used to determine the coordinated cycle deviation value based on the real-time working status and the characteristics of the goods. The compensation determination module is used to determine the amount of compensation for the goods based on the characteristics of the goods. The instruction adjustment module is used to adjust the collaborative rhythm instruction set according to the collaborative rhythm deviation value and the cargo compensation amount.

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