A method and device for energy management of a robot, a server, and a storage medium

Abstract

The application relates to the technical field of industrial automation, and discloses an energy management method and device of a robot, a server and a storage medium. The energy management method comprises the following steps: acquiring residual power information of each robot in a target site; in the case that it is determined according to the residual power information that the residual power of any one robot is less than a first power threshold, controlling the robot to enter a first sleep mode, wherein the robot in the first sleep mode stops participating in task execution; in the case that it is detected that there is a free charging position in the target site, awakening the robot in the first sleep mode according to the free charging position, and dispatching the awakened robot to the corresponding free charging position for charging. The method can realize that a robot with low power does not cause task interruption or operation abnormity due to insufficient power, and greatly improves the safety and stability of operation.

Description

Technical Field

[0001] This application relates to the field of industrial automation technology, and in particular to a method, device, server, and storage medium for energy management of a robot. Background Technology

[0002] With the widespread application of intelligent robots and automated systems in scenarios such as warehousing and logistics, industrial inspection, and flexible manufacturing, intelligent site systems based on multi-robot collaborative operations are gradually becoming an important means to improve operational efficiency. In such systems, a server typically performs unified scheduling and task allocation for multiple robots to automate tasks such as goods handling, route transportation, or work execution within the target site, thereby improving the overall system's operational efficiency and responsiveness.

[0003] However, in actual operation, multiple robots are usually deployed simultaneously in the target area, and each robot will continuously consume its own power while performing tasks. When the power of some robots gradually decreases and is close to being exhausted, if they continue to participate in task scheduling and execution, it may not only cause the robots to interrupt their work due to insufficient power during task execution, but also cause problems such as task failure, path blockage, or even paralysis of local areas, thereby affecting the stable operation of the entire system.

[0004] In existing technologies, the handling of low robot battery is often rather crude, relying heavily on manual intervention or simple low battery alarm mechanisms. This means that low-battery robots may continue to receive task commands or remain in operation, unable to exit the task system and enter the safe charging process in a timely manner. Furthermore, when charging resources are limited, conflicts or uneven scheduling can easily occur when multiple robots need to charge simultaneously, preventing some low-battery robots from replenishing their energy in time and further reducing overall operational efficiency. Summary of the Invention

[0005] This application provides a robot energy management method, device, server, and storage medium. By acquiring the remaining power information of each robot in the target area, and controlling any robot to enter a first sleep mode when its power is lower than a first power threshold, the robot stops participating in task execution. At the same time, when an available charging position is detected, the robot in the sleep state is woken up and scheduled to the corresponding charging position for charging. This solves the technical problems of power outages and chaotic charging scheduling caused by low-power robots continuously participating in task execution in multi-robot systems. It realizes timely isolation and orderly charging scheduling of low-power robots, avoids task interruption or abnormal operation caused by insufficient power, and greatly improves the safety and stability of operation.

[0006] To achieve the above objectives, the main technical solutions adopted in this application include:

[0007] In a first aspect, embodiments of this application provide a robot energy management method, the energy management method comprising: acquiring remaining power information of each robot in a target area; when it is determined, based on the remaining power information, that any robot's remaining power is less than a first power threshold, controlling the robot to enter a first sleep mode, wherein the robot in the first sleep mode stops participating in task execution; and when an available charging station is detected in the target area, waking up the robot in the first sleep mode according to the available charging station, and scheduling the woken-up robot to the corresponding available charging station for charging.

[0008] According to the robot energy management method provided in this application embodiment, by acquiring the remaining power information of each robot in the target site, and controlling any robot to enter a first sleep mode when the remaining power is less than a first power threshold, the robot stops participating in task execution, thereby preventing low-power robots from continuing to participate in task execution; and when an empty charging position is detected in the target site, the robot in the first sleep mode is woken up and scheduled to the corresponding empty charging position for charging, thereby realizing timely isolation and resumption of scheduling of low-power robots, thereby avoiding the problem of task interruption or system malfunction caused by robots continuing to execute tasks due to insufficient power, improving the utilization efficiency of charging resources, ensuring the continuity and stability of multi-robot collaborative operation in the target site, and thus improving the overall operating efficiency.

[0009] Optionally, in some embodiments of this application, after the awakened robot is scheduled to a corresponding available charging position for charging, the energy management method further includes: when the robot's charging power reaches a second power threshold, controlling the robot to stop charging so as to respond to the task instructions issued by the server in a timely manner, wherein the second power threshold is greater than the first power threshold.

[0010] Optionally, in some embodiments of this application, the first power threshold is 10%-20%, and the second power threshold is 70%-80%.

[0011] Optionally, in some embodiments of this application, the second power threshold is determined based on the operating conditions within the target site.

[0012] Optionally, in some embodiments of this application, before controlling the robot to enter a first sleep mode, the method further includes: controlling the robot with remaining power less than a first power threshold to run to a waiting area, wherein the first power threshold is greater than the power required for the robot to run from the waiting area to the charging position after waking up.

[0013] Optionally, in some embodiments of this application, when the remaining battery power of the robot is greater than or equal to a first battery power threshold, the energy management method further includes: if the server does not currently issue a task instruction to the robot, controlling the robot to enter a second sleep mode, wherein the robot in the second sleep mode can be woken up by the server and respond to the task instruction issued by the server in a timely manner.

[0014] Optionally, in some embodiments of this application, the communication frequency between the robot in the second sleep mode and the server is greater than the communication frequency between the robot in the first sleep mode and the server.

[0015] Secondly, this application provides a robot energy management device applied to a server. The energy management device includes: a power acquisition module for acquiring the remaining power information of each robot in a target area; and an energy control module for controlling a robot to enter a first sleep mode when the remaining power of any robot is determined to be less than a first power threshold based on the remaining power information, and for waking up the robot in the first sleep mode based on the available charging position when an available charging position is detected in the target area, and scheduling the woken-up robot to the corresponding available charging position for charging. The robot in the first sleep mode refuses to respond to task instructions issued by the server.

[0016] According to the robot energy management device provided in this application embodiment, the remaining power information of each robot in the target area is obtained through the power acquisition module, and the energy control module controls any robot to enter a first sleep mode when the remaining power is less than a first power threshold, so that it stops participating in task execution, thereby preventing low-power robots from continuing to participate in task execution; when an empty charging position is detected in the target area, the robot in the first sleep mode is woken up and scheduled to the corresponding empty charging position for charging, realizing timely isolation and resumption of scheduling of low-power robots, thereby avoiding the problem of task interruption or system malfunction caused by robots continuing to execute tasks due to insufficient power, improving the utilization efficiency of charging resources, ensuring the continuity and stability of multi-robot collaborative operation in the target area, and thus improving the overall operating efficiency.

[0017] Thirdly, embodiments of this application also provide a server, including: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the robot energy management method described in the above embodiments.

[0018] According to the server provided in the embodiments of this application, the processor executes computer instructions stored in the memory to perform the robot energy management method described in the above embodiments. This allows the server to obtain the remaining power information of each robot in the target area and control it to enter a first sleep mode when the remaining power of any robot is less than a first power threshold, causing it to refuse to respond to task instructions, thereby preventing low-power robots from continuing to participate in task execution. When an available charging station is detected in the target area, the robot in the first sleep mode is woken up and scheduled to the corresponding available charging station for charging, realizing timely isolation and resumption of scheduling of low-power robots. This avoids the problem of task interruption or system malfunction caused by robots continuing to perform tasks due to insufficient power, improves the utilization efficiency of charging resources, ensures the continuity and stability of multi-robot collaborative operation in the target area, and thus improves the overall operating efficiency.

[0019] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the robot energy management method described in the above embodiments.

[0020] According to the computer-readable storage medium provided in the embodiments of this application, when the computer program stored therein is executed by a processor, the robot energy management method described in the above embodiments is executed. This allows the remaining power information of each robot in the target area to be obtained, and when the remaining power of any robot is less than a first power threshold, it is controlled to enter a first sleep mode, causing it to stop participating in task execution, thereby preventing low-power robots from continuing to participate in task execution; when an empty charging position is detected in the target area, the robot in the first sleep mode is woken up and scheduled to the corresponding empty charging position for charging, realizing timely isolation and resumption of scheduling of low-power robots, thereby avoiding the problem of task interruption or system malfunction caused by robots continuing to perform tasks due to insufficient power, improving the utilization efficiency of charging resources, ensuring the continuity and stability of multi-robot collaborative operation in the target area, and thus improving the overall operating efficiency. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific 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 from these drawings without creative effort.

[0022] Figure 1 A flowchart illustrating a robot energy management method according to one embodiment of this application;

[0023] Figure 2 This is a schematic diagram illustrating the switching between a robot's normal operating state and its charging state, provided in one embodiment of this application.

[0024] Figure 3 A block diagram illustrating an energy management device for a robot according to one embodiment of this application;

[0025] Figure 4 This is a schematic diagram of a robot target site scene provided in one embodiment of this application;

[0026] Figure 5 This is a block diagram of a server provided in one embodiment of this application. Detailed Implementation

[0027] 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 embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0028] In related technologies, automated warehousing systems or multi-robot collaborative operation systems typically involve multiple robots working together to perform tasks such as handling, transporting, and storing goods, achieving efficient automated operations within the target site. However, in actual operation, when some robots experience low battery levels, a lack of refined energy management may prevent them from continuing to participate in task scheduling and execution, or they may rely solely on simple low battery alarms, preventing them from promptly exiting the task execution system and entering the charging process. In such cases, if multiple robots are simultaneously low on battery but cannot be charged in time, some robots may be forced to interrupt their work due to insufficient power, leading to task failures, work path blockages, and in severe cases, even affecting the overall normal operation of the target site and reducing the continuity and stability of multi-robot collaborative operations.

[0029] Therefore, the robot energy management method, device, server, and storage medium provided in this application obtain the remaining power information of each robot in the target site, and when it is determined that the remaining power of any robot is less than a first power threshold based on the remaining power information, the robot is controlled to enter a first sleep mode, wherein the robot in the first sleep mode stops participating in task execution; and when it is detected that there is a spare charging position in the target site, the robot in the first sleep mode is woken up according to the spare charging position, and the woken-up robot is scheduled to the corresponding spare charging position for charging. The above technical solution enables real-time acquisition and monitoring of the remaining battery power of each robot in the target area. When the remaining battery power of a robot falls below a first battery threshold, it is put into a first sleep mode to stop participating in task execution, thus preventing low-battery robots from continuing to participate in task execution. At the same time, when there are available charging positions, the robots in the first sleep mode are woken up and scheduled to the corresponding available charging positions for charging. This achieves timely isolation and orderly charging scheduling of low-battery robots, thereby avoiding problems such as task execution interruption or system malfunction due to insufficient robot power, improving the utilization efficiency of charging resources, ensuring the continuity and stability of multi-robot collaborative operation in the target area, and greatly improving the overall operating efficiency.

[0030] The energy management method, apparatus, server, and storage medium for robots provided in embodiments of this application will now be described with reference to the accompanying drawings.

[0031] like Figure 1 The diagram shown is a flowchart illustrating a robot energy management method according to an embodiment of this application. It should be noted that the steps shown in the flowchart can be executed in a computer system, such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that presented here.

[0032] Furthermore, the robot energy management method provided in this embodiment can be used on a remote server. For example... Figure 1 As shown, the robot's energy management method includes the following steps:

[0033] S1. Obtain the remaining battery power information of each robot in the target area;

[0034] S2. If, based on the remaining power information, it is determined that the remaining power of any robot is less than a first power threshold, the robot is controlled to enter a first sleep mode, wherein the robot in the first sleep mode stops participating in task execution.

[0035] S3. If an available charging station is detected in the target site, the robot in the first hibernation mode is woken up according to the available charging station, and the woken robot is scheduled to the corresponding available charging station for charging.

[0036] For example, in this robot's energy management method, the server first continuously acquires the remaining battery information of each robot within the target area. For instance, in an automated warehousing scenario, the server can collect real-time battery data from each storage robot using internal battery sensors or a periodic status reporting mechanism, and generate a battery status list. When a robot (e.g., the handling robot numbered R1) drops below a first battery threshold after continuously performing a handling task, the server can identify this abnormal battery status. In this case, the server controls the robot to enter a first sleep mode. For example, after the battery level of robot R1 falls below the threshold, it will no longer respond to the server's instructions for handling goods or scheduling paths (i.e., it will no longer participate in task execution), thus avoiding problems such as task interruption, path congestion, or equipment downtime caused by its continued operation in a low-battery state. At this time, the robot is essentially "isolated" from the task execution queue, retaining only basic communication or status reporting capabilities.

[0037] Furthermore, when there are available charging spots within the target site, such as after some charging stations in the charging area have completed charging and released their batteries, the server detects that charging resources are available and triggers a wake-up mechanism to reactivate the robots in the first sleep mode. For example, robot R1 with the lowest battery level or closest to a charging spot can be selected from multiple low-battery robots and woken up from its sleep state. Then, based on the distribution of available charging spots, it can be scheduled to the designated charging spot to complete the charging operation, thereby achieving the orderly allocation of charging resources.

[0038] This application embodiment first acquires the remaining battery information of each robot in the target area in real time to achieve dynamic perception of the robot's energy status. Second, by setting a first battery threshold and controlling low-battery robots to enter a first sleep mode, they cease participating in task execution, thus preventing low-battery robots from continuing to participate in task execution and reducing the risk of task failure. Finally, by waking up the sleep robots when there are available charging positions and scheduling them to those positions, dynamic utilization and rational allocation of charging resources are achieved. Therefore, this application embodiment can effectively solve the problem of low-battery robots still participating in task execution in existing multi-robot systems, leading to task interruption or system instability, while improving the utilization efficiency of charging resources, ensuring the continuity and stability of multi-robot collaborative operation in the target area, and improving the overall reliability and scheduling efficiency of operation.

[0039] Optionally, in some embodiments of this application, after the awakened robot is scheduled to a corresponding available charging position for charging, the energy management method further includes: when the robot's charging power reaches a second power threshold, controlling the robot to stop charging so as to respond to the task instructions issued by the server in a timely manner, wherein the second power threshold is greater than the first power threshold.

[0040] Specifically, in this embodiment, after the awakened robot is scheduled to a corresponding available charging position for charging, the server continuously monitors the robot's charging level. For example, in an automated warehousing scenario, after robot R1 is scheduled to a designated charging position and begins charging, its battery level will gradually increase during the charging process. The server can obtain the current charging level in real time through data feedback from the charging interface or the battery management system. When the charging level of the robot is detected to have reached a second charging threshold, the server can control the robot to stop charging and exit the charging state. For example, even if the charging position is still in a state where charging can continue, the robot will no longer continue charging but will release the charging control so that it can respond to subsequent task instructions issued by the server in a timely manner. Furthermore, the control to stop charging is not just a simple disconnection of the charging connection, but may also include removing the robot from the charging queue, updating its status to "scheduling status", and re-incorporating it into the task scheduling pool, thereby ensuring that it can quickly participate in new handling or transportation tasks.

[0041] Through the above steps, on the one hand, by setting a second power threshold (higher than the first power threshold), the robot charging process can be controlled in stages. This allows the robot to exit the charging state once its power level is sufficient to support normal task execution, thus avoiding the problem of excessive charging resource occupation time caused by overcharging and improving the turnover efficiency of charging positions. On the other hand, by stopping charging and restoring scheduling capabilities in a timely manner after reaching the second power threshold, the robot can quickly switch from the charging state to the task response state, thereby reducing waiting time and improving the overall task response speed and scheduling efficiency. Therefore, the embodiments of this application can achieve efficient utilization of charging resources and rapid connection of task scheduling while ensuring the safe recovery of robot power, further improving the continuous operation capability and overall operating efficiency of multiple robots in the target site.

[0042] Optionally, in some embodiments of this application, the first power threshold is 10%-20%, and the second power threshold is 70%-80%.

[0043] In detail, the charging process of commonly used energy storage batteries such as lithium batteries can generally be divided into three stages. The first stage is the low-charge pre-charging stage (approximately 0% to 20%). During this stage, the battery is in a deep discharge state, and to protect battery safety, the charging current is usually limited, resulting in a slow charging speed. For example, when the R1 robot's battery level is below 20%, the battery management system will use a small current for pre-charging to avoid excessive internal stress or rapid temperature rise in the battery. Therefore, this stage is not suitable for prolonged high-speed charging. The second stage is the constant-current fast charging stage (approximately 20% to 80%). During this stage, the internal chemical reaction of the battery is relatively stable and can withstand a larger charging current, resulting in higher charging efficiency and faster capacity increase per unit time. For example, when the R1 robot starts charging from 20% battery level, its charging power can be increased to a high level, quickly restoring most of the capacity in a short time, thus significantly shortening the charging time and improving charging station turnover efficiency. The third stage is the constant-voltage replenishment stage (approximately 80% to 100%). As the battery level approaches full charge, the charging current gradually decreases to prevent overcharging and protect battery life. Therefore, the charging speed decreases significantly during this stage. For example, once the R1 robot's battery reaches 80%, even if it continues to charge, the rate of battery growth will decrease significantly, and the benefit per unit time will be lower, which can easily lead to a longer period of time that the charging station is occupied.

[0044] Based on the aforementioned charging characteristics, this embodiment sets the first power threshold to 10%–20%, corresponding to the trigger range for low-power sleep control; and sets the second power threshold to 70%–80%, corresponding to the end control point of the fast charging phase. For example, when the robot's power level is below 20%, it enters the first sleep mode and begins charging; when the power level reaches 80%, charging stops, thus avoiding entering the third phase where charging efficiency is lower. Through the above threshold settings and segmented charging control method, on the one hand, the robot mainly completes most of its energy replenishment in the second phase where charging efficiency is highest, thereby significantly shortening the time occupied by a single charging cycle and improving the turnover efficiency of charging positions; on the other hand, it avoids the robot entering the low-efficiency replenishment phase, reducing the waste of charging resources, enabling limited charging positions to serve more robots, and improving the overall resource utilization rate.

[0045] In practical applications of this application, the second power threshold is determined based on the operating conditions within the target site.

[0046] Preferably, in some other embodiments of this application, in order to achieve dynamic control over the robot's charging completion time and enable the second power threshold to be adaptively adjusted according to the task load and urgency of the task in the target area, a method for calculating the dynamic second power threshold is proposed. Specifically, the dynamic second power threshold is denoted as Eth, and its calculation is as follows:

[0047] Eth = Emin + (Emax - Emin) · (1 - α · U) where Eth represents the battery level threshold at which the robot stops charging, in percentage (%); Emin represents the lower limit of the second battery level threshold, corresponding to the minimum charging cutoff battery level for the robot to meet basic task execution capabilities, for example, 70%, in percentage; Emax represents the upper limit of the second battery level threshold, corresponding to the maximum cutoff battery level for the robot to be fully charged, for example, 80%, also in percentage. α is an adjustment coefficient used to control the influence of task urgency on the charging threshold adjustment, its value range is 0 to 1, and it is a dimensionless parameter; U represents the comprehensive index of task load urgency, a dimensionless parameter, its value range is 0 to 1.

[0048] In some embodiments of this application, the comprehensive index U of task load urgency is further defined as: U=β·Uq+(1-β)·Up, where β is a weighting coefficient used to balance the influence of task queue load and task urgency, and is a dimensionless parameter with a value range of 0 to 1; Uq represents the task queue load rate, which is a dimensionless parameter, and is calculated as the ratio of the number of currently pending tasks to the maximum task processing capacity of the system, i.e., Uq=Ncur / Nmax, where Ncur represents the number of currently pending tasks, and Nmax represents the maximum task processing capacity, both with the same unit, so the ratio is dimensionless; Up represents the proportion of high-priority tasks, which is a dimensionless parameter, and is calculated as the ratio of the number of high-priority tasks to the total number of current tasks, i.e., Up=Nhigh / Ncur, where Nhigh represents the number of high-priority tasks, and Ncur represents the total number of current tasks.

[0049] By normalizing the task queue status and task urgency into dimensionless indicators through the above method, a weighted fusion is performed to obtain a unified system load urgency indicator U. This is then further mapped to the adjustment process of the second power threshold, so that the charging stop point can change dynamically with the system status.

[0050] Specifically, when the task load in the target area is high or the proportion of high-priority tasks is large, the comprehensive index U increases. At this time, (1-α·U) in the formula decreases, causing Eth to approach the lower Emin. This means the robot can stop charging and resume scheduling execution capability at a lower battery level, thus improving task response speed. Conversely, when the task load is low, U is smaller, and Eth approaches the higher Emax, allowing the robot more sufficient charging time, thereby improving endurance and reducing frequent charging scheduling. Through this dynamic calculation method, the embodiments of this application can achieve adaptive matching between charging behavior and task scheduling state while ensuring the uniformity of the dimensions of each parameter and the stability of the calculation results. On the one hand, it improves the task response efficiency of multiple robots in high-load scenarios; on the other hand, it improves the utilization rate of a single charge and the robot's endurance in low-load scenarios, thereby improving the overall continuity and operational efficiency of multi-robot collaborative operations in the target area.

[0051] Optionally, in some other embodiments of this application, in order to avoid the lack of global adaptability of the charging strategy due to the linear setting of the second power threshold based solely on the busyness of a single task, this application also proposes a joint dynamic determination method based on the charging resource competition state and the robot's return scheduling benefit to determine the second power threshold, so that the charging cut-off decision can simultaneously reflect the system resource supply and demand relationship and the changes in task execution value.

[0052] Specifically, the second energy threshold is denoted as Eth, and its calculation method is as follows:

[0053] Eth = Emin + (Emax − Emin) · (1 - Pc · Rr) where Eth represents the battery threshold when the robot stops charging, in percentage (%); Emin represents the lower limit of the second battery threshold, for example, 70%, in percentage; Emax represents the upper limit of the second battery threshold, for example, 80%, in percentage. Pc represents the charging resource competition pressure coefficient, a dimensionless parameter; Rr represents the robot's expected regression return coefficient, a dimensionless parameter.

[0054] In some embodiments of this application, the charging resource competition pressure coefficient Pc is used to characterize the scarcity of charging resources within the target site. It is calculated as the ratio of the number of robots currently waiting to be charged to the number of currently available charging positions, i.e., Pc = Nwait / Nch, where Nwait represents the number of robots currently waiting to be charged, and Nch represents the number of currently available charging positions. Since both have the same unit, this ratio is a dimensionless parameter. When the number of robots waiting to be charged is greater or the number of charging positions is less, Pc approaches 1, indicating a scarcity of charging resources; conversely, it approaches 0, indicating an abundance of charging resources.

[0055] In some embodiments of this application, the robot regression benefit expectation coefficient Rr is used to characterize the comprehensive scheduling benefit brought about by a robot ending charging early and returning to the task system. It is obtained by weighting a time benefit factor and a task matching benefit factor, specifically: Rr = γ·Rt + (1-γ)·Rm, where γ is a weighting coefficient, a dimensionless parameter with a value range of 0 to 1, used to balance the influence of time benefit and task matching benefit; Rt represents the time benefit factor; and Rm represents the task matching benefit factor. Since both are normalized dimensionless parameters, Rr is also a dimensionless parameter with a value range of 0 to 1.

[0056] In some embodiments of this application, the time benefit factor Rt is used to characterize the urgency of the task waiting time relative to the robot's complete "charging-execution" cycle. It is calculated as follows: Rt = Twait / (Twait + Tcycle), where Twait represents the average waiting time (min) for tasks within the current target area, characterizing the degree of task backlog; Tcycle represents the average cycle time (min) for the robot to complete one complete work loop, including charging, travel, and task execution. Since both have the same unit, Rt is a dimensionless parameter, and its value ranges from 0 to 1. When the task waiting time is short, Rt is small; when the task backlog is severe, Rt approaches 1.

[0057] In some embodiments of this application, the task matching benefit factor Rm is used to characterize the robot's comprehensive adaptability to high-value tasks in the current state, and its calculation method is as follows:

[0058] Where N represents the number of currently allocable tasks; w i a represents the priority weight (dimensionless) of the i-th task; i Rm represents the robot's fitness score for the i-th task (0-1, dimensionless), used to characterize the degree to which the robot performs the task under the current constraints of power, position, and capability. Since both the numerator and denominator are dimensionless weighted sums, Rm is a dimensionless parameter with a value range of 0-1.

[0059] As can be seen from the above, Rt and Rm represent the robot's return benefit from two different dimensions: time urgency and task execution value suitability. A larger Rt indicates a significant backlog in the task queue, and releasing the robot early can significantly reduce waiting delays. A larger Rm indicates that the robot can perform higher-value or more relevant tasks after returning, thereby improving overall benefit. Therefore, Rr, obtained through weighted fusion, can comprehensively reflect the overall benefit level of whether it is worthwhile to end charging early and release the robot.

[0060] Furthermore, a coupling term, Pc·Rr, is introduced into the calculation of the second power threshold to represent the linkage between the scarcity of charging resources and the value of early release. When charging resources are scarce at the target site and the robot's return benefit is high, this product approaches 1, causing Eth to approach Emin, thus allowing the robot to stop charging at a lower power level, thereby improving charging station turnover efficiency and task response efficiency. When charging resources are abundant or the return benefit is low, this product approaches 0, causing Eth to approach Emax, thereby allowing the robot to obtain more charging time, improving endurance and reducing system overhead caused by frequent scheduling. Through the above method, this embodiment extends the charging decision from a single task load-driven approach to a joint decision-making model of resource supply and demand status + scheduling benefit value, enabling the second power threshold to adaptively adjust according to the operating conditions in the target site. This improves the utilization efficiency of charging station resources, avoids resources being occupied by low-yield resources, and also improves the overall operating efficiency and collaborative stability of multiple robots.

[0061] Optionally, in some embodiments of this application, before controlling the robot to enter a first hibernation mode, the method further includes:

[0062] A robot with remaining battery power less than a first battery power threshold is controlled to move to a waiting area, wherein the first battery power threshold is greater than the battery power required for the robot to move from the waiting area to the charging station after being woken up.

[0063] For example, the first power threshold is set to satisfy the following constraint: the first power threshold is greater than the minimum power consumption required for the robot to travel from the waiting area to the charging station after being woken up. For instance, assuming the shortest travel distance from the waiting area to the nearest charging station is 100 meters, and the average energy consumption per unit distance is 0.1% power / meter, then the minimum power consumption for this path is 10%. Considering redundancy factors such as path congestion, detours, and positioning adjustments, the safety redundancy coefficient can be set to 1.3, resulting in an actual safe power requirement of 13%. In this case, the first power threshold can be set to 15%–18%, thereby ensuring that the robot can safely reach the charging station under any path disturbance conditions.

[0064] Furthermore, when a robot detects that its remaining battery level has dropped below a first battery threshold during task execution, it will no longer be allowed to continue performing non-essential tasks. For example, in a warehousing and logistics scenario, if a robot's current battery level is 16% and the first battery threshold is 18%, no new tasks will be assigned to the robot. Instead, it will be guided to a pre-defined waiting area (such as a centralized parking point or path node buffer near a charging station).

[0065] In some embodiments of this application, the waiting area can be configured as a buffer space near the charging area, such as a dedicated parking area or a low-interference path node area around the charging station, enabling the robot to maintain the lowest movement cost and shortest response path while waiting for scheduling or for a charging spot to become available. When a charging spot becomes available or the scheduling system allows, the robot can be quickly woken up directly from the waiting area and guided to the charging spot, thereby reducing path replanning time.

[0066] In this embodiment, since the first power threshold is higher than the minimum energy consumption requirement for the robot to move from the waiting area to the charging station, the robot still has enough power to complete the safe transfer even under complex conditions such as path congestion, navigation errors, or temporary detours, thereby avoiding the problem of power outages and shutdowns during the journey and improving the safety and reliability of operation. At the same time, by guiding low-power robots to the waiting area in a unified manner, they form a concentrated distribution in space. Compared with being scattered in the work area, this can effectively reduce the complexity of path search for scheduling, making the allocation of charging stations more efficient, and thus improving the utilization rate and turnover efficiency of charging resources. In addition, this method can also reduce the occupation of low-power robots in the main work area, reduce the probability of path congestion and operational conflicts, and thus optimize the stability of the overall traffic flow. Furthermore, since the waiting area is usually set up near the charging station, the robot can quickly complete wake-up and path scheduling when released from the charging station, shorten the charging access time, improve response speed and scheduling continuity, not only realizing the safe convergence and orderly management of low-power robots, but also improving the overall charging scheduling efficiency, path passage efficiency, and the stability and operational efficiency of multi-robot collaborative operations.

[0067] Optionally, in some embodiments of this application, when the robot's remaining battery power is greater than or equal to a first battery power threshold, the energy management method further includes:

[0068] If the server does not currently issue a task instruction to the robot, it controls the robot to enter a second sleep mode, wherein the robot in the second sleep mode can be woken up by the server and respond to the task instructions issued by the server in a timely manner.

[0069] In practical applications of this application, when the robot's remaining battery power is greater than or equal to a first battery threshold, if the server has not yet issued a task instruction to the robot, the robot is controlled to enter a second sleep mode to achieve refined energy and scheduling management of idle robots. Specifically, the second sleep mode is a low-power standby state for robots with sufficient battery power but no tasks at the moment. In this state, the robot shuts down or reduces some high-power modules (e.g., continuous operation of drive motors, high-frequency calculation of path planning, high-precision positioning refresh, etc.), while retaining the communication module and basic sensing capabilities, enabling it to maintain a continuous connection with the server and be quickly awakened and immediately participate in task execution upon receiving a task instruction. For example, in an automated warehousing scenario, a robot currently has 65% remaining battery power, which is higher than the first battery threshold (e.g., 20%), but the current task demand is low, and the server has not assigned it any handling tasks. If the robot remains in a full-power standby state, unnecessary energy consumption will occur. Based on this, the server can control it to enter a second sleep mode, causing it to stop ineffective movement and enter a low-power listening state; when a new handling task is generated later, the server can send a wake-up command in time, and the robot can resume operation and perform tasks in a very short time, thereby ensuring timely response.

[0070] It should be noted that the second sleep mode in this application embodiment is fundamentally different from the first sleep mode. The first sleep mode is a low-battery protection mechanism triggered when the robot's remaining battery power is below a first battery threshold. Its purpose is to ensure that the robot will not stop during execution due to battery depletion. Therefore, a robot in the first sleep mode will refuse to respond to task instructions issued by the server and exit the task scheduling system, retaining only minimal communication and status reporting capabilities, waiting to be scheduled to a charging station for energy replenishment. The second sleep mode, on the other hand, is triggered when the robot has sufficient battery power but no task. Its goal is to reduce energy consumption during idle periods while maintaining task responsiveness. Therefore, a robot in the second sleep mode still retains scheduling capabilities and can be woken up by the server at any time to participate in task execution.

[0071] This application embodiment effectively reduces the energy consumption of the robot during idle periods by introducing a second sleep mode when there are no tasks, extends the effective working time after a single charge, reduces the charging frequency, and thus improves the overall energy utilization efficiency. Furthermore, since the robot still maintains communication capabilities and has a rapid wake-up capability in the second sleep mode, it does not affect the real-time performance of task response and can quickly start running when a task arrives, avoiding response delays caused by complete shutdown or deep sleep. At the same time, by distinguishing between the first sleep mode and the second sleep mode, the robot can adopt different control strategies in two different states: low battery and no tasks, to achieve synergistic optimization of energy safety and operating efficiency, thereby significantly improving the operational stability and overall scheduling efficiency of multiple robots.

[0072] Optionally, in some embodiments of this application, the communication frequency between the robot in the second sleep mode and the server is greater than the communication frequency between the robot in the first sleep mode and the server, so as to achieve differentiated control of communication strategies under different operating states. Specifically, the second sleep mode corresponds to a standby state with sufficient power but no tasks at the moment. At this time, the robot still has the ability to participate in task execution at any time, so a higher communication frequency needs to be maintained in order to receive task instructions or scheduling information issued by the server in real time. For example, in an automated warehousing scenario, the robot in the second sleep mode can send status heartbeat information (including current position, power, availability, etc.) to the server every 1 second or every 2 seconds, while continuously listening to the server's task broadcasts or scheduling instructions. Once a task is generated, the server can assign the task to the robot and wake it up in a very short time, so that it can quickly enter the task execution state.

[0073] For robots in the first sleep mode, since the trigger condition is that the remaining battery power falls below a first battery power threshold, the goal is to prioritize energy security and wait for charging scheduling. Therefore, there is no need to maintain high-frequency communication. In this first sleep mode, the robot can reduce the communication frequency, for example, only synchronizing its status with the server once every 5 seconds, 10 seconds, or even longer, and only use it to report basic battery power information or receive charging scheduling instructions, thereby reducing the energy consumption of the communication module and extending the usable time of the remaining battery power.

[0074] In this embodiment, for the second sleep mode, by increasing the communication frequency, the robot can maintain a high sensitivity to task scheduling, thereby achieving rapid wake-up and rapid response, avoiding task allocation delays caused by communication latency, and improving overall scheduling efficiency and real-time performance. On the other hand, for the first sleep mode, by reducing the communication frequency, the energy consumption expenditure in the low power state is further reduced, so that the limited power is prioritized for maintaining basic survival capabilities and the energy needs for subsequent movement to the charging position, thereby improving the safety and reliability of operation.

[0075] Specifically, in this embodiment, combined with Figure 2 The energy management method for the robot is described in detail below. This method is used in scenarios where multiple robots work collaboratively within a target site. By comprehensively managing the robot's operating status, remaining battery power, and task scheduling, a balance is achieved between overall operating efficiency and energy utilization efficiency.

[0076] In detail, during operation, the server first obtains the remaining battery information of each robot in the target area in real time, and then determines the robot's status based on this information. When the remaining battery of any robot is less than a preset first battery threshold, it indicates that the robot is no longer suitable to participate in the task. At this time, as... Figure 2 As shown, the robot switches from normal operation to a low-power management process. Optionally, before entering the first sleep mode, the robot can first move to a preset waiting area. This waiting area is typically located near the charging area to ensure that the robot can quickly reach the charging position with low energy consumption when subsequently woken up. Furthermore, the first power threshold must be set such that its value is greater than the minimum power required for the robot to move from the waiting area to any charging position, thereby preventing the robot from failing to complete the charging scheduling process due to insufficient power.

[0077] Once the robot arrives at the waiting area, it is controlled to enter the first sleep mode. In this mode, the robot ceases task execution and reduces the frequency of communication with the server to minimize energy consumption. Figure 2 As shown, this state corresponds to the first sleep mode (refusing to respond to task commands, low-frequency communication).

[0078] During continuous operation, the server monitors the charging resources within the target area. When an available charging spot is detected, the server selectively wakes up the robots in the first sleep mode based on the number and location of the available charging spots. The woken-up robots then depart from the waiting area, move to the corresponding available charging spot under scheduling control, enter the charging state, and begin charging.

[0079] During charging, the robot's battery level is continuously monitored. When the battery level reaches a second threshold, the robot stops charging. This second threshold is higher than a first threshold, for example, it can be set to 70%–80%, while the first threshold can be set to 10%–20%. This tiered threshold design allows the robot to be put back into use without being fully charged, thereby shortening charging time and improving overall task response efficiency. Furthermore, in some implementations, the second threshold can be dynamically adjusted based on the operating load, task density, or number of robots in the target area to further optimize overall performance.

[0080] After charging is stopped, the robot switches back to normal operating mode and can participate in task execution again, forming a complete energy management closed loop, such as... Figure 2 The path shown is the path from the charging state back to the normal operating state.

[0081] On the other hand, when the robot's remaining battery power is greater than or equal to the first battery threshold, it indicates that it has the basic energy conditions to perform the task. In this case, if the server does not issue a task instruction to the robot, the robot is controlled to enter a second sleep mode. This second sleep mode differs from the first sleep mode and is a standby low-power state. The robot in the second sleep mode maintains a relatively high communication frequency, can receive server instructions in real time, and is quickly awakened and restored to normal working status upon receiving a task instruction, such as... Figure 2 The second sleep mode shown is (can be woken up to respond to tasks, high-frequency communication).

[0082] Furthermore, the communication frequency between the robot in the second sleep mode and the server is higher than that between the robot in the first sleep mode. This ensures that low-power robots can conserve energy as much as possible, while also ensuring that robots with execution capabilities can respond to tasks quickly, thus achieving a reasonable balance between energy saving and response speed.

[0083] Based on the above, this embodiment realizes refined management of the robot under different power levels and task states, which not only effectively avoids the risks caused by low power operation, but also significantly improves the overall operating efficiency and resource utilization.

[0084] According to the robot energy management method provided in this application embodiment, the remaining power information of each robot in the target area is first obtained. When it is detected that the remaining power of any robot is less than a first power threshold, it is controlled to enter a first sleep mode, so that the low-power robot exits the task execution system and refuses to respond to task instructions. Then, when it is detected that there is an available charging position, the robot in the first sleep mode is woken up and scheduled to the corresponding charging position for charging. At the same time, when the robot's charging power reaches a second power threshold, it is controlled to stop charging, so that it can restore its task response capability in time. The second power threshold is dynamically adjusted according to the operation of the target area. In addition, before entering the first sleep mode, the low-power robot is guided to a waiting area for centralized management. When the robot has sufficient power but no task, it is controlled to enter a second sleep mode. At the same time, the communication frequency in the second sleep mode is higher than that in the first sleep mode to ensure its rapid response capability. The above technical solution achieves layered energy management and dynamic scheduling control of robots throughout the entire process from low battery protection and charging scheduling to idle standby. On the one hand, it can avoid the problem of robots continuing to perform tasks when low battery is caused by mid-process shutdown or task interruption, thereby improving the safety and stability of operation. On the other hand, by controlling the charging to stop within a suitable battery range and dynamically adjusting the threshold in combination with the operating status, the turnover efficiency and resource utilization of charging positions are improved. Therefore, while ensuring the energy safety of robots, it can achieve synergistic optimization of charging resource utilization efficiency and task scheduling efficiency, avoid operational instability or task execution interruption caused by individual battery issues, and significantly improve the continuous operation capability of multiple robots and the overall operating efficiency.

[0085] In some embodiments of this application, such as Figure 3 As shown, a robot energy management device 100 is also provided, applied to a server, the energy management device 100 comprising:

[0086] The power acquisition module 101 is used to acquire the remaining power information of each robot in the target area;

[0087] The energy control module 102 is used to control a robot to enter a first sleep mode when it is determined that the remaining power of any robot is less than a first power threshold based on the remaining power information. When it is detected that there is a spare charging position in the target site, the module wakes up the robot in the first sleep mode according to the spare charging position and schedules the woken-up robot to the corresponding spare charging position for charging. The robot in the first sleep mode refuses to respond to the task instructions issued by the server.

[0088] Preferably, the energy control module 102 is further configured to, after scheduling the awakened robot to a corresponding available charging position for charging, control the robot to stop charging when the robot's charging power reaches a second power threshold, so as to respond to the task instructions issued by the server in a timely manner, wherein the second power threshold is greater than the first power threshold.

[0089] Optionally, in some embodiments of this application, the first power threshold is 10%-20%, and the second power threshold is 70%-80%.

[0090] Optionally, in some other embodiments of this application, the second power threshold is determined based on the operating conditions within the target site.

[0091] Preferably, the energy control module 102 is further configured to, before controlling the robot to enter the first sleep mode, control the robot with remaining power less than a first power threshold to run to the waiting area, wherein the first power threshold is greater than the power required for the robot to run from the waiting area to the charging position after waking up.

[0092] Preferably, the energy control module 102 is further configured to, when the remaining power of the robot is greater than or equal to a first power threshold, control the robot to enter a second sleep mode if the server has not currently issued a task instruction to the robot, wherein the robot in the second sleep mode can be woken up by the server and respond to the task instruction issued by the server in a timely manner.

[0093] According to the robot energy management device 100 provided in the embodiments of this application, the remaining power information of each robot in the target site is obtained by the power acquisition module 101, and the energy control module 102 controls the robot to enter the first sleep mode when it detects that the remaining power of any robot is less than the first power threshold, so that the low power robot exits the task execution system and refuses to respond to the task instructions issued by the server. At the same time, if an empty charging position is detected, the robot in the first sleep mode is woken up and scheduled to the corresponding charging position for charging. Further, when the robot's charging power reaches the second power threshold, it is controlled to stop charging so that it can restore its task response capability in time. The second power threshold can be dynamically adjusted according to the operation of the target site. At the same time, before entering the first sleep mode, the low power robot is guided to the waiting area to achieve centralized management of the low power robot. When the robot has sufficient power but has not received a task instruction, it is controlled to enter the second sleep mode to reduce idle energy consumption and maintain wake-up capability. Through the above structural design, the system achieves coordinated operation of real-time acquisition of robot power status, low-power protection control, charging scheduling, and idle state management. On the one hand, it avoids the problem of robots continuing to perform tasks with low power, which could lead to mid-process shutdowns or task interruptions, thereby improving the safety and stability of system operation. On the other hand, by controlling charging to stop within a suitable power range and dynamically adjusting the threshold, the turnover efficiency and resource utilization of charging positions are improved. At the same time, through centralized scheduling of the waiting area and the low-power standby mechanism of the second sleep mode, the system reduces ineffective energy consumption and path interference, and ensures rapid response capability when tasks arrive, thereby avoiding a decrease in overall scheduling efficiency. Therefore, while ensuring robot energy safety, it can achieve synergistic optimization of charging resource utilization efficiency and task scheduling efficiency, significantly improving the continuous operation capability and overall operating efficiency of multi-robot systems.

[0094] The energy management device of the robot in this application embodiment is presented in the form of a functional module. Here, a module refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0095] Furthermore, the specific implementation process of the robot's energy management device described in the above embodiments is similar to the implementation process of the robot's energy management method described in the above embodiments, and will not be described in detail here.

[0096] To more clearly illustrate the working principle and practical application effect of the energy management device described in this application, the energy management method of this application will be further explained below in conjunction with a specific application scenario. It should be noted that the operating scenarios in the following embodiments are only used to explain the technical solution of this application and do not constitute a limitation on the scope of protection of this application. In this embodiment, the energy management device is deployed in a server and achieves real-time monitoring and scheduling control of the robot's energy status through communication and interaction with each robot in the target site. Its specific operating process is as follows:

[0097] like Figure 4 As shown, the Figure 4 This paper illustrates an operational scenario of robot energy management within a target area, as shown in one embodiment of this application. The entire target area includes a server, multiple robots (R1 to R7), charging stations, and a waiting area. The server maintains a communication connection with each robot to obtain real-time information on the remaining battery power of each robot. Based on this remaining battery power information, task command status, and charging resource occupancy, the server performs unified scheduling and energy management control of the robots.

[0098] In this embodiment, each robot operates in a different state within the target area. For example, R5 (approximately 65% ​​battery) and R6 (approximately 45% battery) are in normal working condition and are performing tasks; while R7 (approximately 50% battery), without receiving task instructions, is controlled to enter a second sleep mode. Although the robot in the second sleep mode is not performing tasks, it still maintains a relatively high communication frequency so that it can be promptly awakened and put into work when the server issues a task, thus balancing energy saving and response efficiency.

[0099] Furthermore, when the server determines, based on the collected remaining battery information, that a robot's battery level is below a first battery threshold (e.g., 10%–20%), the server first controls the robot to stop its current task and guides it to a preset waiting area, as shown in the dashed box in the figure. This waiting area is used for centralized management of low-battery robots and ensures that the robot retains sufficient battery power before entering hibernation to meet the energy requirements for moving from the waiting area to the charging station. Subsequently, the server controls the robot to enter a first hibernation mode. In this mode, the robot stops participating in task execution, and the communication frequency is relatively low to further reduce energy consumption.

[0100] like Figure 4 As shown, R3 and R4 are robots in the first sleep mode, waiting for scheduling within the waiting area. Meanwhile, R1 and R2 are at their charging positions and charging, indicating that the charging resources are currently occupied. When the server detects an available charging position within the target area (e.g., ...), ... Figure 4When a robot is in its first sleep mode (e.g., R3 or R4), a wake-up and scheduling mechanism will be triggered. Specifically, the server selects the target robot (e.g., R3 or R4) from the robots in the first sleep mode, sends it a wake-up command, and schedules it to move from the waiting area to the corresponding available charging spot for charging.

[0101] During charging, the server continuously monitors the robot's battery level. When a robot (such as R1 or R2) reaches a second battery threshold (e.g., 70%–80%), the server stops charging, enabling it to immediately perform tasks. This avoids overcharging and congestion of charging resources, improving overall operational efficiency. It should be noted that this second battery threshold can be dynamically adjusted based on the actual operational load of the target site. For example, the threshold can be appropriately lowered during periods of high task intensity to improve robot turnaround efficiency.

[0102] In summary, the server enables precise management of the energy status of each robot within the target area. For robots with low remaining battery power, they are controlled to enter a first sleep mode, achieving task exit and energy reduction. This, combined with the waiting area, allows for centralized management and orderly scheduling of such robots. For robots with sufficient remaining battery power but not currently receiving task instructions, they are controlled to enter a second sleep mode, reducing energy consumption while maintaining a rapid response capability to task instructions, thereby improving overall response and operational efficiency. Furthermore, the different communication frequencies in different sleep modes improve the utilization efficiency of communication resources.

[0103] Please see Figure 5 This is a schematic diagram of the structure of a server provided in one embodiment of this application, as shown below. Figure 5 As shown, the server includes one or more processors 1301, memory 1302, and communication interfaces 1303 for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other using different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the server, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interface). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories, if desired. Figure 5 Take the 1301 processor as an example.

[0104] Processor 1301 may be a central processing unit, a network processor, or a combination thereof. Processor 1301 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0105] The memory 1302 stores instructions executable by at least one processor 1301 to cause at least one processor 1301 to perform the energy management method for the robot shown in the above embodiments.

[0106] Memory 1302 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on server usage. Furthermore, memory 1302 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, memory 1302 may optionally include memory remotely located relative to processor 1301, and this remote memory may be connected to the server via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0107] The memory 1302 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 1302 may also include a combination of the above types of memory.

[0108] Communication interface 1303 is used for the server to communicate with other devices or communication networks.

[0109] This application also provides a computer-readable storage medium. The robot energy management method described above according to this application embodiment can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the robot energy management method described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that a computer, processor, microprocessor controller, or programmable hardware includes a storage component capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the robot energy management method shown in the above embodiments is implemented.

[0110] The energy management method for robots applied to a server provided in this application acquires and monitors the remaining power information of each robot in the target area in real time. Using a preset first power threshold as a criterion, when the power of any robot falls below this threshold, it is automatically controlled to enter a first sleep mode, ceasing to respond to task commands and being prioritized for charging scheduling. If an available charging station is detected in the area, the server wakes up the robot in the first sleep mode and schedules it to the corresponding available charging station to perform the charging task, thereby achieving centralized scheduling and orderly energy replenishment management of robots with low power. Simultaneously, during the charging process, when the robot's power reaches a second power threshold higher than the first power threshold, it is controlled to stop charging to promptly restore its task execution capability. Furthermore, by matching the low power threshold with the power required for the robot to travel from the waiting area to the charging station, a safe and accessible charging path is ensured for the robot.

[0111] In further optimization, this application dynamically determines a second power threshold based on the target site's operating status. When the robot's power level is not lower than the first power threshold and the server has not issued any tasks, the robot enters a second sleep mode to reduce communication and scheduling load. The communication frequency in the second sleep mode is higher than in the first sleep mode to balance wake-up response speed and communication resource consumption. Through this hierarchical sleep and communication mechanism, refined management of the robot's operating status can be achieved while ensuring timely charging scheduling.

[0112] The robot energy management method provided in this application realizes the active identification and centralized scheduling of robots with low battery levels, avoiding the problem that robots cannot return to the charging position autonomously because they are still performing tasks when the battery is too low. At the same time, the utilization efficiency of charging resources is improved by matching available charging positions and waking up the scheduling strategy, reducing the idle or congested situation of charging piles. Furthermore, combined with the waiting area pre-transfer strategy, it ensures that robots with low battery levels can complete the displacement and charging connection within a safe battery range, improving the overall production efficiency and task continuity, as well as enhancing the stability of robot cluster operation and the level of intelligent energy management.

[0113] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and all such modifications and variations fall within the scope defined by the appended claims.

[0114] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0115] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0116] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0117] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0118] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0119] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0120] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0121] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0122] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

[0123] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for energy management of a robot, characterized in that, The energy management method includes: Obtain the remaining battery power information of each robot in the target area; If, based on the remaining power information, it is determined that the remaining power of any robot is less than a first power threshold, the robot is controlled to enter a first sleep mode, wherein the robot in the first sleep mode stops participating in task execution. If an available charging station is detected in the target site, the robot in the first hibernation mode is woken up according to the available charging station, and the woken robot is scheduled to the corresponding available charging station for charging.

2. The energy management method for a robot according to claim 1, characterized in that, After scheduling the awakened robot to a corresponding available charging position for charging, the energy management method further includes: When the robot's charging power reaches a second power threshold, the robot is controlled to stop charging in order to respond promptly to the task instructions issued by the server, wherein the second power threshold is greater than the first power threshold.

3. The energy management method for a robot according to claim 2, characterized in that, The first power threshold is 10%-20%, and the second power threshold is 70%-80%.

4. The energy management method for a robot according to claim 2, characterized in that, The second power threshold is determined based on the operating conditions within the target site.

5. The energy management method for a robot according to any one of claims 1-4, characterized in that, Before controlling the robot to enter the first hibernation mode, the method further includes: A robot with remaining battery power less than a first battery power threshold is controlled to move to a waiting area, wherein the first battery power threshold is greater than the battery power required for the robot to move from the waiting area to the charging station after being woken up.

6. The energy management method for a robot according to any one of claims 1-4, characterized in that, If the robot's remaining battery power is greater than or equal to a first battery power threshold, the energy management method further includes: If the server does not currently issue a task instruction to the robot, it controls the robot to enter a second sleep mode, wherein the robot in the second sleep mode can be woken up by the server and respond to the task instructions issued by the server in a timely manner.

7. The energy management method for a robot according to claim 6, characterized in that, The communication frequency between the robot in the second sleep mode and the server is greater than the communication frequency between the robot in the first sleep mode and the server.

8. An energy management device for a robot, characterized in that, The energy management device, applied to a server, includes: The power acquisition module is used to acquire the remaining power information of each robot in the target area; The energy control module is used to control a robot to enter a first sleep mode when it is determined that the remaining power of any robot is less than a first power threshold based on the remaining power information. When an available charging station is detected in the target site, the module wakes up the robot in the first sleep mode according to the available charging station and schedules the woken-up robot to the corresponding available charging station for charging. The robot in the first sleep mode refuses to respond to the task instructions issued by the server.

9. A server, characterized in that, include: A memory and a processor are communicatively connected, the memory storing computer instructions, and the processor executing the computer instructions to perform the energy management method of the robot according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, performs the energy management method for the robot according to any one of claims 1-7.

Images