Distribution recovery control method and device during distribution fault of conveying robot, and computer equipment

Abstract

The invention relates to a delivery recovery control method and device during delivery fault of a delivery robot and computer equipment, and the method comprises the steps: obtaining fault sensing information collected by a plurality of types of sensors, the fault sensing information comprising mechanical state information, power system operation information, navigation information and communication state information; when it is determined that a distribution fault exists according to the fault sensing information, fault classification is determined based on the fault sensing information; based on the fault sensing information, fault classification, a fault classification decision mechanism, a dynamic emergency response mechanism and a preset intelligent recovery protocol, distribution recovery control is carried out; the dynamic emergency response mechanism comprises the step of opening an anti-falling air bag of the conveying robot to support the goods under the condition that the goods falling risk is identified according to the mechanical state information. Through fault detection, cargo anti-falling emergency processing and a recovery mechanism, smooth completion of a distribution task can be ensured.

Description

Technical Field

[0001] The present application relates to the technical field of delivery control of delivery robots, and in particular to a delivery recovery control method, device, and computer equipment in the event of a delivery failure of a delivery robot. Background Art

[0002] With the continuous development and popularization of automation technology, the market demand and development prospects of delivery robots are becoming increasingly broad. At the same time, the rise of new fields such as human-robot collaboration and multi-agent collaboration is also driving the development of delivery robots towards greater efficiency, flexibility, and diversity.

[0003] The delivery robot can deliver the goods from a certain location to a designated location through a path planning system. However, the applicant found that during the delivery process, the delivery task may fail. Summary of the Invention

[0004] Based on this, it is necessary to provide a delivery recovery control method, device, and computer equipment in response to the above technical problems, which can ensure the smooth completion of delivery tasks when a delivery robot fails to deliver.

[0005] In a first aspect, the present application provides a delivery recovery control method for a delivery robot in the event of a delivery failure, the method comprising:

[0006] Acquiring fault sensing information collected by multiple types of sensors, wherein the fault sensing information includes mechanical status information, power system operation information, navigation information, and communication status information;

[0007] In a case where a delivery fault is determined to exist according to the fault sensing information, determining a fault classification based on the fault sensing information;

[0008] Performing distribution recovery control based on the fault sensing information, the fault classification, the fault classification decision mechanism, the dynamic emergency response mechanism, and a preset intelligent recovery protocol;

[0009] The dynamic emergency response mechanism includes:

[0010] When it is identified based on the mechanical state information that there is a risk of cargo falling, the anti-fall airbag of the transport robot is opened to support the cargo.

[0011] In one embodiment, the dynamic emergency response mechanism further includes:

[0012] When the machine status information identifies that there is a risk of cargo falling, the warning light operates in the first state to indicate that there is a risk of cargo falling.

[0013] In one exemplary technique, the preset intelligent recovery protocol includes:

[0014] In the case where it is determined that there is a delivery failure based on the fault sensing information, adjacent transport robots are summoned based on a Mesh network composed of multiple transport robots to construct a temporary conveyor belt for goods.

[0015] In an exemplary technique, the fault classification includes first-level faults, second-level faults, and third-level faults. Determining the fault classification based on the fault sensing information includes:

[0016] If it is determined according to the fault sensing information that the transport robot has lost power or there is a risk of fire, it is determined as a first-level fault;

[0017] If it is determined according to the fault sensing information that the local function of the transport robot is abnormal, it is determined as a second-level fault;

[0018] If it is determined according to the fault sensing information that the performance of the transport robot has deteriorated, it is determined as a third-level fault.

[0019] In an exemplary technique, the fault classification decision mechanism includes:

[0020] If a first-level fault occurs, the transport robot immediately shuts down;

[0021] If a second-level fault occurs, the transport robot operates in a degraded mode;

[0022] If a third-level fault occurs, the transport robot continues to operate and sends an alarm message to the monitoring terminal.

[0023] In an exemplary technique, the dynamic emergency response mechanism further includes at least one of the following:

[0024] When it is determined according to the fault sensing information that the wheel set of the transport robot is jammed, enable the redundant Mecanum wheel independent drive mode;

[0025] When it is determined according to the fault sensing information that the motor of the transport robot is overheated, switch on the standby cooling system to cool the motor;

[0026] When it is determined according to the fault sensing information that the transport robot loses power, pop out the emergency roller set for inertial sliding braking;

[0027] When it is determined according to the fault sensing information that the lidar of the transport robot is abnormal, switch to binocular vision SLAM to scan the QR code installed on the ceiling for positioning;

[0028] When it is determined according to the fault sensing information that the map of the transport robot is lost, call the building BIM model to generate a temporary navigation grid;

[0029] When determining that the path of the transport robot is blocked based on the fault sensing information, start the acoustic wave matrix scanning;

[0030] When it is identified that there is a misalignment of goods based on the mechanical state information, drive the six-axis robotic arm of the transport robot to assist in correcting the goods to the position before misalignment, and drive the vacuum suction cup of the transport robot to fix the goods;

[0031] When it is identified that there is a liquid leakage of the carried goods based on the mechanical state information, deploy the nano waterproof barrier layer of the transport robot and send a chemical substance detection instruction to the monitoring terminal.

[0032] In an exemplary technique, the intelligent recovery protocol includes progressive self-repair of the transport robot in the order of isolating the faulty module, firmware hot swapping, and actuator calibration from front to back.

[0033] In an exemplary technique, the above method further includes:

[0034] Construct a digital twin prediction model to predict the motor life, battery health, and bearing wear of the transport robot.

[0035] In a second aspect, there is also provided a distribution recovery control device for a transport robot during distribution failures, and the device includes:

[0036] A fault sensing information acquisition module, configured to acquire fault sensing information collected by multiple types of sensors, where the fault sensing information includes mechanical state information, power system operation information, navigation information, and communication status information;

[0037] A fault classification module, configured to determine a fault classification based on the fault sensing information when it is determined that there is a distribution failure according to the fault sensing information;

[0038] A distribution recovery control module, configured to perform distribution recovery control based on the fault sensing information, the fault classification, a fault classification decision mechanism, a dynamic emergency response mechanism, and a preset intelligent recovery protocol;

[0039] Wherein, the dynamic emergency response mechanism includes:

[0040] When it is identified that there is a risk of goods falling based on the mechanical state information, open the anti-fall airbag of the transport robot to support the goods.

[0041] In a third aspect, there is provided a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the above method are implemented.

[0042] The above-mentioned distribution recovery control method, device, and computer equipment for the delivery robot during distribution failures obtain failure sensing information collected by multiple types of sensors for failure detection. When it is determined that a distribution failure has occurred, based on the failure sensing information, the failure classification is determined, and distribution recovery control is performed in combination with the failure sensing information, failure classification, failure classification decision mechanism, dynamic emergency response mechanism, and a preset intelligent recovery protocol. Among them, in the case of the risk of goods falling, the anti-drop airbag of the delivery robot is opened to support the goods. It can effectively reduce the risk of failure of the distribution task due to encountering failures during the distribution of goods such as office supplies, thus ensuring the smooth completion of the distribution task. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] To more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0044] Figure 1 It is an application environment diagram of the distribution recovery control method for the delivery robot during distribution failures in an embodiment;

[0045] Figure 2 It is a flowchart of the distribution recovery control method for the delivery robot during distribution failures in an embodiment;

[0046] Figure 3 It is a structural block diagram of the distribution recovery control device for the delivery robot during distribution failures in an embodiment;

[0047] Figure 4 It is an internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] In order to make the objectives, technical solutions, and advantages of the present application more clearly understood, the following further details the present application in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0049] The distribution recovery control method for the delivery robot provided by the embodiments of the present application can be applied as Figure 1In the application environment shown. Among them, the order system 100 sends a delivery task to the central scheduling system 200, and the central scheduling system 200 communicates with multiple delivery robots 300 through a network. After receiving the delivery task, the central scheduling system 200 controls the delivery robot 300 to execute the delivery task. During the process of the delivery robot 300 delivering goods, if a delivery failure occurs, the steps of the delivery recovery control method for the delivery robot during a delivery failure provided in the embodiments of the present application are executed to ensure the smooth completion of the delivery task. Among them, the central scheduling system 200 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0050] In an exemplary embodiment, as Figure 2 shown, a delivery recovery control method for a delivery robot during a delivery failure is provided, and the method includes:

[0051] S201, obtaining failure sensing information collected by multiple types of sensors, where the failure sensing information includes mechanical state information, power system operation information, navigation information, and communication state information.

[0052] Multiple types of sensors are installed on the delivery robot, and these sensors collect the operation state information of each key aspect of the robot in real time. The mechanical state information characterizes the attitude and relative position of the goods relative to the delivery robot. For example, the goods are tilted, or the change in load caused by the goods falling off, etc. The power system operation information characterizes the state of the components that provide moving power for the delivery robot. For example, the rotation speed of the motor, the speed of the wheels, etc. The navigation information is used to reflect whether there is a path planning deviation during the process of the delivery robot delivering goods. The navigation system can combine the analysis of the lidar point cloud matching degree and the IMU (Inertial Measurement Unit) data verification to achieve a more accurate and reliable navigation function to obtain reliable navigation information. The communication state information characterizes whether the communication between the delivery robot and other delivery robots, monitoring terminals, the central scheduling system and other devices is stable and whether there is a signal interruption. The communication state information can include the heartbeat packet loss counter data and the signal strength heat map. Based on the heartbeat packet loss counter and the signal strength heat map records, it can be judged whether there is a failure of the communication module.

[0053] S202, in the case of determining that there is a delivery failure according to the failure sensing information, determining a failure classification based on the failure sensing information.

[0054] Comprehensively analyze the obtained fault sensing information, and determine the type of fault according to the preset rules and patterns. For example, if the angle of the goods relative to the loading platform of the transportation robot in the mechanical state information is abnormal, it may be classified as a mechanical state abnormality. If the battery power in the power system continuously drops rapidly and there is an overload prompt for the motor, it may be determined as a power system fault. Or if the current sensor detects motor overload and the encoder monitors abnormal wheel speed, it can also be determined as a power system fault. If there are frequent inaccurate positioning and normal communication status, the type of navigation system fault can be determined. Through fault classification, more targeted subsequent countermeasures can be taken to ensure that the goods can be successfully delivered to the designated location.

[0055] In an exemplary technique, the fault classification includes primary faults, secondary faults, and tertiary faults. Determining the fault classification based on the fault sensing information includes:

[0056] If it is determined from the fault sensing information that the transportation robot has lost power or there is a risk of fire, it is determined as a primary fault;

[0057] If it is determined from the fault sensing information that the local function of the transportation robot is abnormal, it is determined as a secondary fault;

[0058] If it is determined from the fault sensing information that the performance of the transportation robot has deteriorated, it is determined as a tertiary fault.

[0059] S203, perform distribution recovery control based on the fault sensing information, the fault classification, the fault classification decision-making mechanism, the dynamic emergency response mechanism, and the preset intelligent recovery protocol;

[0060] Among them, the dynamic emergency response mechanism includes:

[0061] When it is identified from the mechanical state information that there is a risk of the goods falling, open the anti-drop airbag of the transportation robot to support the goods.

[0062] The mechanical state information may include data detected by a gyroscope and a pressure sensor.

[0063] Use a fault classification decision-making mechanism, a dynamic emergency response mechanism, and a preset intelligent recovery protocol (which stipulates the specific protocol content for how to restore the normal delivery function of the robot in various situations) to jointly control the recovery of the robot's delivery task. Comprehensively consider various factors that may cause delivery failures, and take measures from different perspectives to restore the normal delivery of goods. For the situation where there is a risk of the goods falling (which can be judged by various mechanical state-related indicators such as the tilt angle of the robot and the swing amplitude of the robotic arm), immediately open the anti-drop airbag of the delivery robot. After the airbag is deployed, it can support the goods, prevent the goods from falling to the ground and being damaged due to robot failures, and reduce economic losses. For example, when the robot is moving forward and tilts to one side by 30 degrees as a result of a failure of a certain mechanical component, it is considered that there is a risk of the goods falling. At this time, the dynamic emergency response mechanism is quickly triggered, and the anti-drop airbag is quickly opened to firmly support the goods and ensure the integrity of the goods. The position of the anti-drop airbag is set lower than the placement position of the goods on the delivery robot, so that in the event of a risk of the goods falling, the goods can be caught to prevent damage caused by falling to the ground.

[0064] Specifically, obtain the fault sensing information collected by multiple types of sensors for fault detection. When it is determined that a delivery failure has occurred, based on the fault sensing information, determine the fault classification, and combine the fault sensing information, fault classification, fault classification decision-making mechanism, dynamic emergency response mechanism, and preset intelligent recovery protocol for delivery recovery control; among them, for the situation of the risk of the goods falling, open the anti-drop airbag of the delivery robot to support the goods. It can effectively reduce the risk of the delivery task failing due to encountering failures in the delivery of goods such as office supplies, thereby ensuring the smooth completion of the delivery task.

[0065] That is, the delivery recovery control when the delivery robot fails provided by the embodiments of the present application, by comprehensively collecting fault sensing information, accurately grasping various aspects of the problems that occur in the robot delivery, and performing fault classification and multi-protocol comprehensive processing when a failure occurs, so that there are corresponding countermeasures for different faults, enabling the robot to continue to complete the delivery task and ensuring the success rate of the delivery task. Opening the anti-drop airbag for the risk of the goods falling avoids economic losses caused by the goods falling and being damaged, protects the interests of the goods owner, and also improves the reliability and service quality of the entire logistics delivery link.

[0066] In addition, accurate fault classification and targeted recovery control measures can reduce the situation where the robot stays stagnant for a long time waiting for repair due to failures, reduce the time cost for maintenance personnel to troubleshoot problems, and at the same time reduce the risk of further damage to other components of the robot that may be caused by failures, thereby reducing the overall operation and maintenance cost and improving the use efficiency and economic benefits of the robot.

[0067] In one embodiment, the dynamic emergency response mechanism further includes:

[0068] When the mechanical state information identifies a risk of cargo dropping, the warning light operates in the first state to indicate the risk of cargo dropping.

[0069] When the mechanical state information identifies a risk of cargo dropping, making the warning light operate in the first state. On the one hand, it can remind on-site staff to help correct the cargo, and the position of the cargo that needs to be adjusted can be quickly determined without on-site staff checking one by one. On the other hand, it can also warn nearby personnel to avoid the cargo falling and hitting nearby personnel. Therefore, the warning light triggering mechanism when there is a risk of cargo dropping can ensure cargo safety, improve work efficiency, reduce potential safety hazards, and is conducive to enhancing the reliability and stability of the entire logistics distribution or related operation scenarios.

[0070] In an exemplary technique, the preset intelligent recovery protocol includes:

[0071] When it is determined that there is a distribution fault according to the fault sensing information, based on the Mesh network composed of multiple delivery robots, summon neighboring delivery robots to construct a temporary cargo conveyor belt.

[0072] When it is determined that there is a distribution fault according to the fault sensing information, the distribution task may face interruption. By summoning neighboring delivery robots based on the Mesh network composed of multiple delivery robots to construct a temporary cargo conveyor belt, the cargo can be quickly transported to the designated destination at the fault point with the help of the temporarily constructed conveyor belt, maximizing the continuity of the distribution task and reducing the overall distribution delay caused by individual robot failures, which helps to maintain the efficiency of distribution.

[0073] In addition, by using the existing Mesh network composed of multiple delivery robots, there is no need to allocate other special transportation equipment or wait for manual intervention to handle the cargo, reducing hardware and labor costs.

[0074] In an exemplary technique, the fault classification decision mechanism includes:

[0075] If a first-level fault occurs, the delivery robot immediately stops.

[0076] If a second-level fault occurs, the delivery robot operates in a degraded mode.

[0077] If a third-level fault occurs, the delivery robot continues to operate and sends an alarm message to the monitoring terminal.

[0078] A first-level fault means a dangerous situation where the robot loses its own power or there is a risk of fire. Such a situation may cause collisions with other delivery robots or spread the fire. At this time, immediately stopping the machine can prevent the situation from deteriorating further, minimize the scope of negative impacts, and reduce losses.

[0079] By allowing the robot to operate in a degraded mode, even when there are local functional abnormalities, its remaining working capacity can still be utilized to continue to undertake part of the delivery tasks, which helps to maintain the delivery efficiency. Especially during peak logistics periods or when delivery tasks are urgent, it can minimize the impact on the overall delivery plan while ensuring the safety of the delivery robot and the goods, enabling the delivery work to continue orderly and avoiding large-scale delivery delays caused by the failure of a single delivery robot.

[0080] For a third-level fault, sending an alarm message to the monitoring terminal enables the operation and maintenance personnel to obtain the fault information in a timely manner, so that the robot can be repaired promptly when it is idle, reducing the idle time of the robot due to faults and increasing the effective working hours of the delivery robot.

[0081] In an exemplary technology, the dynamic emergency response mechanism further includes at least one of the following:

[0082] When it is determined according to the fault sensing information that the wheel group of the delivery robot is jammed, enable the redundant Mecanum wheel independent drive mode.

[0083] When the wheel group is jammed, it seriously affects the normal movement of the robot. By enabling the redundant Mecanum wheel independent drive mode, the robot can continue to obtain the ability to move relying on the spare Mecanum wheels, avoiding being completely stalled in place due to wheel group faults, thus ensuring that the delivery task can continue, maintaining the coherence of the delivery process, and reducing delivery delays caused by the robot being stalled in place.

[0084] When it is determined according to the fault sensing information that the motor of the delivery robot is overheated, switch on the standby cooling system to cool the motor.

[0085] If the overheated motor is not dealt with in a timely manner, it will cause irreversible damage to key components such as the windings and insulating materials inside the motor, seriously affecting the service life and performance of the motor. By switching on the standby cooling system (for example, a Peltier thermoelectric cooler), the excess heat generated by the motor can be quickly removed, bringing its temperature back to the normal operating range, effectively protecting the motor from overheating damage, and reducing the maintenance or replacement costs caused by motor faults.

[0086] When it is determined according to the fault sensing information that the delivery robot is powered off, pop out the emergency roller group for inertial sliding braking.

[0087] In the event of a sudden power failure, the robot will be out of control due to the loss of power control, easily collide with surrounding objects, causing damage to itself and surrounding facilities, as well as the dropping of goods, and may even endanger the safety of on-site personnel. Popping out the emergency roller group for inertial sliding braking can make the robot stop smoothly as soon as possible after a power failure, avoid the occurrence of accidental collision accidents, and ensure the safety of the robot, goods and surrounding environment.

[0088] When it is determined according to the fault sensing information that the lidar of the transport robot is abnormal, switch to binocular vision SLAM to scan the QR code installed on the ceiling for positioning.

[0089] When the lidar cannot work properly, the combination of binocular vision SLAM and QR code positioning can make up for its lack of function to a certain extent, enabling the robot to maintain normal operating capabilities in more diverse environmental conditions and fault scenarios, ensuring that the robot can still clearly identify its own location, guaranteeing the accuracy of navigation, enabling the delivery task to continue according to the plan, avoiding delivery chaos caused by positioning failure, and improving the overall environmental adaptability and robustness of the transport robot.

[0090] When it is determined according to the fault sensing information that the map of the transport robot is lost, call the building BIM model to generate a temporary navigation grid.

[0091] The map is crucial for the navigation of the robot. Once lost, it will be difficult for the robot to plan a reasonable path to reach the destination, and the delivery task will be in trouble. Calling the building BIM model to generate a temporary navigation grid can quickly reconstruct the map information required for navigation for the robot, enabling it to continue planning the path based on the newly generated temporary navigation grid, restoring the navigation function, ensuring that the delivery task is not interrupted, and maintaining the normal completion of the delivery task. In addition, using the existing building BIM model data, there is no need to carry out complex map construction work again, saving time and computing resources.

[0092] When it is determined according to the fault sensing information that the path of the transport robot is blocked, start the acoustic matrix scanning.

[0093] Based on the information obtained from the acoustic matrix scanning (for example, it can be 40kHz ultrasonic array detection), a reasonable obstacle avoidance path can be further analyzed and planned, bypassing the blocked area and continuing to the destination, avoiding long-term stagnation due to path blockage, improving the ability of the transport robot to independently respond to complex environmental changes, and ensuring the efficient and smooth execution of the delivery task.

[0094] When it is identified according to the mechanical state information that there is a misalignment of the goods, drive the six-axis robotic arm of the transport robot to assist in correcting the goods to the position before misalignment, and drive the vacuum suction cup of the transport robot to fix the goods.

[0095] Goods misalignment may cause goods to shake and collide during transportation, thereby causing damage to the goods. By timely correcting the position of the goods with a six-axis robotic arm and fixing them with a vacuum suction cup, the subsequent damage risk caused by misalignment can be effectively avoided, ensuring that the goods are transported in a correct and stable state, protecting the integrity of the goods, and reducing the economic losses caused by damaged goods.

[0096] When it is identified according to the mechanical state information that there is a liquid leakage in the carried goods, the nano waterproof layer of the transportation robot is unfolded, and a chemical substance detection instruction is sent to the monitoring terminal.

[0097] Liquid leakage may pollute the equipment of the robot itself, resulting in failures such as electrical short circuits. At the same time, it may also pollute the surrounding environment and affect other goods or facilities. Unfolding the nano waterproof layer can timely block the leaked liquid within a certain range, preventing its further spread, protecting the key components inside the robot as well as the surrounding environment and goods from being contaminated and damaged, and reducing a series of subsequent risks caused by liquid leakage. Synchronously sending a chemical substance detection instruction to the monitoring terminal enables the monitoring personnel to know immediately about the liquid leakage of the goods and the possible chemical substances involved, facilitating the timely organization of professional personnel to take corresponding emergency treatment measures, such as preparing appropriate cleaning tools, protective equipment, etc. At the same time, it also helps in the subsequent monitoring and assessment of the leaked substances, ensuring the safety and stability of the entire logistics distribution environment.

[0098] In an exemplary technology, the intelligent recovery protocol includes progressive self-repair of the transportation robot in the order of isolating the faulty module, firmware hot swapping, and actuator calibration from front to back.

[0099] Isolating the faulty module through the hardware fusing mechanism can quickly isolate the specific part with problems, control the impact of the fault within the minimum range, and maintain the relative stable state of the entire transportation robot, ensuring that other key functions can still work normally and reducing the risk of the entire robot system being paralyzed due to a single module failure.

[0100] If the transportation robot still cannot resume operation after isolating the faulty module, firmware hot swapping can be performed based on the alternate flashing of the dual storage partitions, enabling the relevant functions to continue to function properly, maintaining the basic operating ability of the robot, and ensuring the smooth progress of the distribution task.

[0101] If firmware hot swapping fails to restore normal operation, actuator calibration is performed based on the automatic calibration feedback from the laser tracker to correct the deviation that occurs after the actuator has been used for a long time or has experienced special working conditions, enabling the actuator to return to its optimal working state. This ensures that during subsequent delivery tasks of the delivery robot, operations such as grasping and placing goods are more precise, improving the accuracy and success rate of goods delivery and reducing cases of goods damage or delivery errors caused by inaccurate actuator movements.

[0102] In an exemplary technique, the above method further includes:

[0103] Constructing a digital twin prediction model to predict the motor life, battery health, and bearing wear of the delivery robot.

[0104] Specifically, a motor life decay curve can be established through Monte Carlo simulation. By comprehensively considering factors such as fluctuations in motor load, changes in ambient temperature, and manufacturing process differences that affect motor life, the remaining life of the motor under different usage conditions can be predicted. Maintenance personnel can estimate in advance when the motor may fail, and then plan maintenance and repair work in advance or arrange for motor replacement at an appropriate time, avoiding sudden damage to the motor during operation that could cause the robot to stop, ensuring the normal operation of the delivery robot, and reducing delivery interruptions caused by sudden motor failures.

[0105] The health of the battery directly affects the robot's endurance and working hours. By combining the number of charge cycles and internal resistance changes to predict battery health, the performance degradation of the battery can be monitored in real time. Maintenance personnel can take corresponding measures based on this, such as promptly repairing, replacing, or adjusting the delivery task arrangement of the robot for batteries with a rapid decline in health (for example, avoiding assigning long-distance delivery tasks to robots with poor battery health), ensuring that the robot has sufficient power to complete delivery tasks, maintaining the efficiency and continuity of goods delivery, and reducing delivery delays caused by insufficient battery power.

[0106] The wear condition of the bearing is not easily observable directly, but it can be reflected by changes in the sound generated during operation. By establishing a bearing wear sound pattern feature library and comparing it, early signs of bearing wear can be captured in a timely manner using acoustic signals, potential fault hazards can be detected in advance, and corresponding maintenance measures can be taken before the bearing wear seriously affects the normal operation of the robot, such as replacing worn bearings and performing lubrication maintenance, avoiding mechanical failures caused by excessive bearing wear, ensuring the stable operation of the robot's mechanical structure, and reducing maintenance costs and downtime caused by mechanical failures.

[0107] In one embodiment, a preventive maintenance system is constructed. For example, the adaptability of the distribution environment is transformed. By installing a dynamic environment modeling base station (such as a millimeter-wave radar combined with a thermal imaging composite sensor), setting up an emergency charging island (dual-mode of wireless charging and mechanical contact), etc., it can ensure the continuity and smoothness of the delivery tasks performed by the delivery robot.

[0108] In one embodiment, based on the human-machine collaboration protocol, hierarchical alarm and safe handover can be carried out:

[0109] For example, in the case of a first-level fault, the color of the status indicator light changes (for example, emits yellow or red light).

[0110] In the case of a second-level fault, the touch screen displays a fault QR code (including a link to the maintenance manual).

[0111] In the case of a third-level fault, the AR-assisted interface is activated (for example, the faulty component is displayed through a tablet computer).

[0112] Safe handover:

[0113] The goods can be loaded into the enclosed carriage of the delivery robot. When it is necessary to transfer the goods to other objects, the emergency hatch of the delivery robot can be opened based on biometric features such as palm veins and dynamic password recognition verification to ensure the safety of the goods.

[0114] Based on the hash value generated by the timestamp and the fault sensing information collected by the above sensors, the status of the goods is stored on the blockchain.

[0115] The maintenance record automatically generates an NFT log (including the digital signature of the maintenance personnel).

[0116] The distribution recovery control method for the delivery robot during distribution failure provided by the embodiments of the present application can effectively reduce the risk of the failure of distribution tasks caused by faults encountered by goods such as office supplies during distribution through fault detection, emergency handling of preventing goods from falling, recovery mechanism, and preventive measures based on the digital twin prediction model.

[0117] The distribution recovery control method for the delivery robot during distribution failure provided by the embodiments of the present application can be executed by an embedded FPGA to achieve millisecond-level fault response. It can also use 5G MEC edge computing to ensure low-latency decision-making, and at the same time combine digital twin technology to achieve predictive maintenance. It is combined with a monthly fault injection test (FIT) to verify the system reliability.

[0118] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown according to the indications of the arrows, these steps do not necessarily have to be executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps does not have a strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages do not necessarily have to be executed at the same time, but can be executed at different times. The execution order of these steps or stages does not necessarily have to be sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0119] Based on the same inventive concept, an embodiment of the present application also provides a distribution recovery control device for a delivery robot when a distribution failure occurs, which is used to implement the distribution recovery control method for the delivery robot when a distribution failure occurs as described above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the distribution recovery control device for a delivery robot when a distribution failure occurs provided below can refer to the limitations on the distribution recovery control method for a delivery robot when a distribution failure occurs in the above text, and will not be repeated here.

[0120] In an exemplary embodiment, as Figure 3 shown, a distribution recovery control device 300 for a delivery robot when a distribution failure occurs is provided, including:

[0121] A fault sensing information acquisition module 301, configured to acquire fault sensing information collected by multiple types of sensors, where the fault sensing information includes mechanical state information, power system operation information, navigation information, and communication status information;

[0122] A fault classification module 302, configured to determine a fault classification based on the fault sensing information;

[0123] A distribution recovery control module 303, configured to perform distribution recovery control based on the fault sensing information, the fault classification, a fault classification decision mechanism, a dynamic emergency response mechanism, and a preset intelligent recovery protocol;

[0124] Among them, the dynamic emergency response mechanism includes:

[0125] In the case where it is recognized from the mechanical state information that there is a risk of cargo dropping, the anti-drop airbag of the delivery robot is opened to support the cargo.

[0126] Each module in the above-described delivery recovery control device for a delivery robot during a delivery failure can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above modules can be embedded in the processor of a computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form to facilitate the processor to call and execute the operations corresponding to each of the above modules. Other modules or units in the delivery recovery control device for a delivery robot during a delivery failure can also perform other steps in the above method embodiments and achieve corresponding beneficial effects.

[0127] In an exemplary embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 4 shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a delivery recovery control method for a delivery robot during a delivery failure.

[0128] Those skilled in the art can understand that Figure 4 the structure shown in

[0129] is only a block diagram of a part of the structure related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different component layout.

[0130] In an embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps in each of the above method embodiments.

[0131] In one embodiment, a computer program product is provided, on which a computer program is stored, and the computer program is executed by a processor to perform the steps in the above-mentioned method embodiments.

[0132] Those of ordinary skill in the art can understand that all or part of the processes of implementing the above-mentioned method embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned method embodiments. Among them, any reference to a memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include Read-Only Memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, Resistive Random Access Memory (ReRAM), Magnetoresistive Random Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene memory, etc. Volatile memory can include Random Access Memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, Artificial Intelligence (AI) processors, etc., without limitation.

[0133] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in the present application.

[0134] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A distribution recovery control method for a delivery robot in case of distribution failure, characterized in that, The method includes: Obtaining fault sensing information collected by multiple types of sensors, where the fault sensing information includes mechanical state information, power system operation information, navigation information, and communication status information; When it is determined that there is a delivery fault based on the fault sensing information, determining a fault classification based on the fault sensing information; Performing delivery recovery control based on the fault sensing information, the fault classification, a fault classification decision mechanism, a dynamic emergency response mechanism, and a preset intelligent recovery protocol; Among them, the dynamic emergency response mechanism includes: When it is identified from the mechanical state information that there is a risk of the goods falling, opening the anti-drop airbag of the delivery robot to support the goods.

2. The method according to claim 1, wherein The dynamic emergency response mechanism also includes: When it is identified from the mechanical state information that there is a risk of the goods falling, the warning light works in the first state to indicate the risk of the goods falling.

3. The method according to claim 1, characterized in that, The preset intelligent recovery protocol includes: When it is determined that there is a delivery fault based on the fault sensing information, summoning adjacent delivery robots based on a Mesh network composed of multiple delivery robots to construct a temporary conveyor belt for the goods.

4. The method according to claim 1, wherein The fault classification includes first-level faults, second-level faults, and third-level faults. Determining the fault classification based on the fault sensing information includes: If it is determined from the fault sensing information that the delivery robot has lost power or there is a risk of fire, it is determined as a first-level fault; If it is determined from the fault sensing information that the local function of the delivery robot is abnormal, it is determined as a second-level fault; If it is determined from the fault sensing information that the performance of the delivery robot has deteriorated, it is determined as a third-level fault.

5. The method according to claim 4, wherein The fault classification decision mechanism includes: If a first-level fault occurs, the delivery robot stops immediately; If a second-level fault occurs, the delivery robot operates in a degraded mode; If a third-level fault occurs, the delivery robot maintains operation and sends an alarm message to the monitoring terminal.

6. The method according to claim 1, wherein The dynamic emergency response mechanism also includes at least one of the following: When it is determined from the fault sensing information that the wheel set of the delivery robot is stuck, enabling the redundant Mecanum wheel independent drive mode; When it is determined from the fault sensing information that the motor of the delivery robot is overheated, switching on the standby cooling system to cool the motor; When it is determined from the fault sensing information that the delivery robot loses power, popping out the emergency roller set for inertial sliding braking; When it is determined from the fault sensing information that the lidar of the delivery robot is abnormal, switching to binocular vision SLAM to scan the QR code installed on the ceiling for positioning; When it is determined from the fault sensing information that the map of the delivery robot is lost, calling the building BIM model to generate a temporary navigation grid; When it is determined from the fault sensing information that the path of the delivery robot is blocked, starting the acoustic matrix scan; When it is identified from the mechanical state information that there is a misalignment of the goods, driving the six-axis robotic arm of the delivery robot to assist in correcting the goods to the position before misalignment, and driving the vacuum suction cup of the delivery robot to fix the goods; When it is identified from the mechanical state information that there is a liquid leakage in the carried goods, the nano waterproof layer of the delivery robot is deployed, and a chemical substance detection instruction is sent to the monitoring terminal.

7. The method according to claim 1, characterized in that The intelligent recovery protocol includes progressive self-repair of the delivery robot in the order of isolating the faulty module, firmware hot-switching, and actuator calibration from front to back.

8. The method according to any one of claims 1 to 7, characterized in that The method further includes: Constructing a digital twin prediction model to predict the motor life, battery health, and bearing wear of the delivery robot.

9. A distribution recovery control device for a delivery robot in case of distribution failure, characterized in that, The device includes: A fault sensing information acquisition module for acquiring fault sensing information collected by multiple types of sensors, where the fault sensing information includes mechanical state information, power system operation information, navigation information, and communication status information; A fault classification module for determining the fault classification based on the fault sensing information when it is determined from the fault sensing information that there is a distribution fault; A distribution recovery control module for performing distribution recovery control based on the fault sensing information, the fault classification, a fault classification decision mechanism, a dynamic emergency response mechanism, and a preset intelligent recovery protocol; Among them, the dynamic emergency response mechanism includes: When it is identified from the mechanical state information that there is a risk of the goods falling, the anti-fall airbag of the delivery robot is opened to support the goods.

10. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

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