Intelligent scheduling method, device, equipment and storage medium of nursing robot

Abstract

The present invention discloses an intelligent scheduling method, device, equipment and storage medium for a nursing robot, which obtains the current state information of each nursing robot when receiving a scheduling task of a nursing robot; inputs the scheduling task and the current state information into a nursing robot selection mechanism, so that a first nursing robot is selected based on the nursing robot selection mechanism; and obtains a first scheduling motion trajectory of the first nursing robot when executing the scheduling task based on a first preset path planning algorithm, so that the first nursing robot moves according to the first scheduling motion trajectory until the scheduling task is completed. Compared with the prior art, the technical solution of the present invention can realize the reasonable and efficient allocation and use of a nursing robot group, while reducing unnecessary energy consumption and achieving an environmental protection effect.

Description

Technical Field

[0001] The present invention relates to the technical field of mobile robot control, and in particular to an intelligent scheduling method, device, equipment and storage medium of a nursing robot. Background Art

[0002] With the rapid development of science and technology, more and more intelligent devices are emerging to provide people with more convenient services. In public places such as hospitals and nursing homes, nursing robot groups undertake many service tasks. For example, nursing robots based on laser SLAM can not only replace wheelchairs to complete auxiliary mobility tasks, but also provide auxiliary standing, lying and other functions, which greatly facilitates the user's mobility. However, when scheduling nursing robots, the principle of random scheduling or nearby scheduling is often adopted, which is easy to cause scheduling interruptions due to insufficient power of nursing robots during the scheduling process, thereby affecting the scheduling efficiency. Therefore, how to select suitable nursing robots in the nursing robot group to perform tasks and realize the efficient use of the nursing robot group has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide an intelligent scheduling method, device, equipment and storage medium for nursing robots, so as to realize the reasonable and efficient allocation and use of nursing robot groups, reduce unnecessary energy consumption and achieve environmental protection.

[0004] In order to solve the above technical problems, the present invention provides an intelligent scheduling method for a nursing robot, comprising:

[0005] When receiving a scheduling task of a nursing robot, obtain the current status information of each nursing robot;

[0006] Inputting the scheduling task and the current state information into a nursing robot selection mechanism, so that a first nursing robot is selected based on the nursing robot selection mechanism;

[0007] Based on a first preset path planning algorithm, a first scheduling motion trajectory of the first nursing robot when performing the scheduling task is obtained, so that the first nursing robot moves according to the first scheduling motion trajectory until the scheduling task is completed.

[0008] In a possible implementation, the scheduling task and the current state information are input into a nursing robot selection mechanism, so that a first nursing robot is selected based on the nursing robot selection mechanism, specifically including:

[0009] Inputting the scheduling task and the current state information into a nursing robot selection mechanism, so that the nursing robot selection mechanism queries and obtains a plurality of first idle nursing robots according to the scheduling task and the current state information, wherein the scheduling task includes a usage time, a task start position and a task target position, and the current state information includes a first current position, a first remaining power and an idle time;

[0010] Based on a second preset path planning algorithm, the first current position, the task starting position and the task target position are called to generate a scheduling motion trajectory of each first idle nursing robot, and based on the scheduling motion trajectory, a first scheduling power consumption of each first idle nursing robot is predicted;

[0011] Compare the first remaining power corresponding to each first idle nursing robot with the first scheduled power consumption, so as to select a plurality of second idle nursing robots satisfying the condition that the first remaining power is greater than the first scheduled power consumption from a plurality of first idle nursing robots;

[0012] Obtain the scheduling motion trajectory corresponding to each second idle nursing robot, generate a scheduling motion trajectory data set, select the shortest scheduling motion trajectory from the scheduling motion trajectory data set, and use the second idle nursing robot corresponding to the shortest scheduling motion trajectory as the selected first nursing robot.

[0013] In a possible implementation, after completing the scheduling task, the method further includes:

[0014] When it is detected that the first nursing robot completes the scheduling task, detecting the second remaining power of the first nursing robot in real time, and comparing the second remaining power with a preset power threshold;

[0015] If the second remaining power is less than a preset power threshold, a charging reminder is sent to the manager, and the first nursing robot is dispatched to a charging position;

[0016] If the second remaining power is not less than the preset power threshold, it is determined whether the first nursing robot is in the robot docking position. If not, the first nursing robot is dispatched to the docking position.

[0017] In a possible implementation, based on a first preset path planning algorithm, obtaining a first scheduling motion trajectory of the first nursing robot when performing the scheduling task specifically includes:

[0018] Obtaining a dispatching motion trajectory corresponding to the first nursing robot, and controlling the first nursing robot to move along the dispatching motion trajectory, and detecting in real time whether there are obstacles during the movement;

[0019] When an obstacle is detected, a first distance between the first nursing robot and the obstacle at the current moment is obtained, and the first distance is called based on the first preset path planning algorithm to obtain a local obstacle avoidance motion trajectory at the next moment;

[0020] The scheduling motion trajectory is corrected according to the local obstacle avoidance motion trajectory to obtain a first scheduling motion trajectory.

[0021] In a possible implementation, based on the scheduling motion trajectory, predicting the first scheduling power consumption of each first idle nursing robot specifically includes:

[0022] The simulated dispatching power consumption per kilometer is obtained, and based on the dispatching motion trajectory, the trajectory length of the dispatching motion trajectory is obtained;

[0023] The first scheduled power consumption of each first idle nursing robot is predicted based on the trajectory length and the simulated scheduled power consumption.

[0024] In a possible implementation, dispatching the first nursing robot to a charging position specifically includes:

[0025] Obtaining the current working status of all current charging positions, wherein the current working status includes charging and not charging;

[0026] Filtering out a plurality of idle charging positions whose current working status is uncharged from all the charging positions;

[0027] Obtaining a second current position of the first nursing robot, respectively calculating second distances between the second current position and the plurality of idle charging positions, and generating a second distance data set;

[0028] Selecting the shortest second distance from the second distance data set, and using the idle charging position corresponding to the shortest second distance as the target charging position;

[0029] A charging scheduling motion trajectory is generated according to the second current position and the target charging position, and the first nursing robot is controlled to move to the target charging position according to the charging scheduling motion trajectory.

[0030] In a possible implementation, dispatching the first nursing robot to a docking position specifically includes:

[0031] Obtain the current working status of all current docking positions, wherein the current working status includes docked and undocked;

[0032] Filtering out a plurality of idle docking positions whose current working status is not docked from all the docking positions;

[0033] Acquire a third current position of the first nursing robot, calculate third distances between the third current position and the plurality of idle parking positions, and generate a third distance data set;

[0034] Selecting the shortest third distance from the third distance data set, and taking the idle parking position corresponding to the shortest third distance as the target parking position;

[0035] A docking scheduling motion trajectory is generated based on the third current position and the target docking position, and the first nursing robot is controlled to move to the target docking position according to the docking scheduling motion trajectory.

[0036] The embodiment of the present invention further provides an intelligent scheduling device for a nursing robot, comprising: a nursing robot information acquisition module, a first nursing robot selection module and a first nursing robot scheduling module;

[0037] Wherein, the nursing robot information acquisition module is used to obtain the current status information of each nursing robot when receiving the scheduling task of the nursing robot;

[0038] The first nursing robot selection module is used to input the scheduling task and the current state information into a nursing robot selection mechanism, so that a first nursing robot is selected based on the nursing robot selection mechanism;

[0039] The first nursing robot scheduling module is used to obtain a first scheduling motion trajectory of the first nursing robot when performing the scheduling task based on a first preset path planning algorithm, so that the first nursing robot moves according to the first scheduling motion trajectory until the scheduling task is completed.

[0040] In a possible implementation, the first nursing robot selection module is used to input the scheduling task and the current state information into a nursing robot selection mechanism so as to select a first nursing robot based on the nursing robot selection mechanism, specifically including:

[0041] Inputting the scheduling task and the current state information into a nursing robot selection mechanism, so that the nursing robot selection mechanism queries and obtains a plurality of first idle nursing robots according to the scheduling task and the current state information, wherein the scheduling task includes a usage time, a task start position and a task target position, and the current state information includes a first current position, a first remaining power and an idle time;

[0042] Based on a second preset path planning algorithm, the first current position, the task starting position and the task target position are called to generate a scheduling motion trajectory of each first idle nursing robot, and based on the scheduling motion trajectory, a first scheduling power consumption of each first idle nursing robot is predicted;

[0043] Compare the first remaining power corresponding to each first idle nursing robot with the first scheduled power consumption, so as to select a plurality of second idle nursing robots satisfying the condition that the first remaining power is greater than the first scheduled power consumption from a plurality of first idle nursing robots;

[0044] Obtain the scheduling motion trajectory corresponding to each second idle nursing robot, generate a scheduling motion trajectory data set, select the shortest scheduling motion trajectory from the scheduling motion trajectory data set, and use the second idle nursing robot corresponding to the shortest scheduling motion trajectory as the selected first nursing robot.

[0045] An intelligent scheduling device for a nursing robot provided by an embodiment of the present invention further includes: the first nursing robot detection module;

[0046] The first nursing robot detection module is used to detect the second remaining power of the first nursing robot in real time after detecting that the first nursing robot has completed the scheduling task, and compare the second remaining power with a preset power threshold; if the second remaining power is less than the preset power threshold, a charging reminder is sent to the management personnel, and the first nursing robot is scheduled to the charging position; if the second remaining power is not less than the preset power threshold, it is determined whether the first nursing robot is in the robot docking position, if not, the first nursing robot is dispatched to the docking position.

[0047] In a possible implementation, the first nursing robot scheduling module is used to obtain a first scheduling motion trajectory of the first nursing robot when performing the scheduling task based on a first preset path planning algorithm, specifically including:

[0048] Obtaining a dispatching motion trajectory corresponding to the first nursing robot, and controlling the first nursing robot to move along the dispatching motion trajectory, and detecting in real time whether there are obstacles during the movement;

[0049] When an obstacle is detected, a first distance between the first nursing robot and the obstacle at the current moment is obtained, and the first distance is called based on the first preset path planning algorithm to obtain a local obstacle avoidance motion trajectory at the next moment;

[0050] The scheduling motion trajectory is corrected according to the local obstacle avoidance motion trajectory to obtain a first scheduling motion trajectory.

[0051] In a possible implementation, the first nursing robot selection module is used to predict the first scheduling power consumption of each first idle nursing robot based on the scheduling motion trajectory, specifically including:

[0052] The simulated dispatching power consumption per kilometer is obtained, and based on the dispatching motion trajectory, the trajectory length of the dispatching motion trajectory is obtained;

[0053] The first scheduled power consumption of each first idle nursing robot is predicted based on the trajectory length and the simulated scheduled power consumption.

[0054] In a possible implementation, the first care robot detection module is used to dispatch the first care robot to a charging position, specifically including:

[0055] Obtaining the current working status of all current charging positions, wherein the current working status includes charging and not charging;

[0056] Filtering out a plurality of idle charging positions whose current working status is uncharged from all the charging positions;

[0057] Obtaining a second current position of the first nursing robot, respectively calculating second distances between the second current position and the plurality of idle charging positions, and generating a second distance data set;

[0058] Selecting the shortest second distance from the second distance data set, and using the idle charging position corresponding to the shortest second distance as the target charging position;

[0059] A charging scheduling motion trajectory is generated according to the second current position and the target charging position, and the first nursing robot is controlled to move to the target charging position according to the charging scheduling motion trajectory.

[0060] In a possible implementation, the first nursing robot detection module is used to dispatch the first nursing robot to a docking position, specifically including:

[0061] Obtain the current working status of all current docking positions, wherein the current working status includes docked and undocked;

[0062] Filtering out a plurality of idle docking positions whose current working status is not docked from all the docking positions;

[0063] Acquire a third current position of the first nursing robot, calculate third distances between the third current position and the plurality of idle parking positions, and generate a third distance data set;

[0064] Selecting the shortest third distance from the third distance data set, and taking the idle parking position corresponding to the shortest third distance as the target parking position;

[0065] A docking scheduling motion trajectory is generated based on the third current position and the target docking position, and the first nursing robot is controlled to move to the target docking position according to the docking scheduling motion trajectory.

[0066] An embodiment of the present invention also provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the intelligent scheduling method for a nursing robot as described in any one of the above items is implemented.

[0067] An embodiment of the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the intelligent scheduling method for a nursing robot as described in any one of the above.

[0068] The intelligent scheduling method, device, equipment and storage medium of a nursing robot according to the embodiment of the present invention have the following beneficial effects compared with the prior art:

[0069] When receiving a scheduling task of a nursing robot, the current state information of each nursing robot is obtained; the scheduling task and the current state information are input into the nursing robot selection mechanism, so that the first nursing robot is selected based on the nursing robot selection mechanism; based on the first preset path planning algorithm, the first scheduling motion trajectory of the first nursing robot when executing the scheduling task is obtained, so that the first nursing robot moves according to the first scheduling motion trajectory until the scheduling task is completed. Compared with the prior art, the technical solution of the present invention can realize the reasonable and efficient allocation and use of the nursing robot group, while reducing unnecessary energy consumption and achieving the effect of environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 It is a flow chart of an embodiment of an intelligent scheduling method for a nursing robot provided by the present invention;

[0071] Figure 2 It is a structural schematic diagram of an embodiment of an intelligent scheduling device of a nursing robot provided by the present invention;

[0072] Figure 3 It is a structural schematic diagram of another embodiment of an intelligent scheduling device for a nursing robot provided by the present invention. DETAILED DESCRIPTION

[0073] The following will be combined with the accompanying drawings in the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0074] Example 1

[0075] See also Figure 1 , Figure 1 : is a flow chart of an embodiment of an intelligent scheduling method for a nursing robot provided by the present invention, such as Figure 1 As shown, the method includes steps 101 to 103, which are specifically as follows:

[0076] Step 101: When receiving a scheduling task for a nursing robot, obtain the current status information of each nursing robot.

[0077] In one embodiment, when a nursing robot needs to be called, the user needs to fill in the usage requirements into the front-end APP, which converts the usage requirements into scheduling tasks and sends the scheduling tasks to the back-end scheduling center for processing; wherein the scheduling tasks include information such as usage time, task starting location, and task target location.

[0078] Preferably, the usage time in the scheduling task is determined by simulating the usage of the nursing robot in different time periods on all routes before the nursing robot is put into use, and recording the corresponding driving time, so as to estimate the time required for the nursing robot to perform the scheduling task. In order to prevent the actual usage time from being greater than the estimated time due to situations such as obstacle avoidance, the estimated time is appropriately inflated to avoid the error between the expected usage time and the actual usage time, resulting in insufficient battery power of the nursing robot or overlap with subsequent scheduling tasks.

[0079] In one embodiment, after receiving the scheduling task, the back-end scheduling center calls the current status information of each nursing robot from the back-end database, and receives the current status information of each nursing robot returned by the back-end database; wherein the current status information includes information such as the first current position, the first remaining power and the idle time.

[0080] Step 102: Input the scheduling task and the current status information into the nursing robot selection mechanism, so as to select the first nursing robot based on the nursing robot selection mechanism.

[0081] In one embodiment, the scheduling task and the current status information are input into a nursing robot selection mechanism, so that the nursing robot selection mechanism queries and obtains a plurality of first idle nursing robots according to the scheduling task and the current status information.

[0082] Specifically, the usage time in the scheduling task and the idle time in the current status information are compared. If the usage time is within the idle time range, the nursing robot is considered to be the first idle nursing robot. If the usage time is not within the idle time range, the nursing robot is considered not to be the first idle nursing robot.

[0083] In one embodiment, the first current position, the task starting position and the task target position are called based on a second preset path planning algorithm to generate a scheduling motion trajectory of each first idle nursing robot, and based on the scheduling motion trajectory, the first scheduling power consumption of each first idle nursing robot is predicted;

[0084] Specifically, after obtaining multiple first idle nursing robots, it is also necessary to plan the scheduling motion trajectories of the multiple first idle nursing robots. By determining the task starting position and the task target position, the moving path is planned based on the second preset path planning algorithm to generate the scheduling motion trajectory of each first idle nursing robot; wherein, the second preset path planning algorithm includes the A* path planning algorithm.

[0085] In one embodiment, after generating the scheduling motion trajectory, the first scheduling power consumption of each first idle nursing robot is predicted based on the scheduling motion trajectory.

[0086] Specifically, before the first idle nursing robot is put into use, the usage of the first idle nursing robot when carrying users of different weights is simulated, and the power consumption per kilometer when users of different weights use each route is analyzed to obtain the simulated scheduling power consumption per kilometer when executing the scheduling task, and based on the scheduling motion trajectory, the trajectory length of the scheduling motion trajectory is obtained, and according to the trajectory length and the simulated scheduling power consumption, the first scheduling power consumption of each first idle nursing robot is predicted.

[0087] Preferably, in order to prevent the simulated scheduling power consumption from being less than the actual power usage due to the first idle nursing robot using assisted standing, assisted lying down functions and other reasons, in which the power consumption is increased, in this embodiment, a preset power consumption is also added to the simulated scheduling power consumption to ensure that the simulated scheduling power consumption can meet the actual power usage needs, thereby improving the use reliability of the first idle nursing robot.

[0088] In one embodiment, based on the current status information of the nursing robot, the first remaining power of each first idle nursing robot is obtained, and the first remaining power corresponding to each first idle nursing robot is compared with the first scheduled power consumption, so as to select a plurality of second idle nursing robots that satisfy the requirement that the first remaining power is greater than the first scheduled power consumption from a plurality of first idle nursing robots; wherein the second idle nursing robots are nursing robots that meet the usage time of the scheduled task and the scheduled power consumption required for the scheduled task.

[0089] In one embodiment, the scheduling motion trajectory corresponding to each second idle nursing robot is obtained, a scheduling motion trajectory data set is generated, the shortest scheduling motion trajectory is selected from the scheduling motion trajectory data set, and the second idle nursing robot corresponding to the shortest scheduling motion trajectory is selected as the selected first nursing robot.

[0090] Preferably, the number of the first care robots may be one, or the number of the first care robots may be multiple.

[0091] Step 103: Based on a first preset path planning algorithm, a first scheduling motion trajectory of the first nursing robot when performing the scheduling task is obtained, so that the first nursing robot moves according to the first scheduling motion trajectory until the scheduling task is completed.

[0092] In one embodiment, the nursing robot is a laser SLAM-based nursing robot. The laser SLAM-based nursing robot uses a Raspberry Pi 4B as a host computer and an STM32 microcontroller as a slave computer, is equipped with a ROS system, and uses sensors such as laser radar and inertial measurement units to collect information. The robot's environment is mapped and the nursing robot's position is located through SLAM technology, and path planning is performed based on a first preset path planning algorithm.

[0093] In one embodiment, after the first nursing robot is selected, the scheduling task is sent to the upper computer of the first nursing robot. After receiving the scheduling task, the selected nursing robot plans the scheduling motion trajectory from the first current position to the task starting position and during the operation, and sends the scheduling task to the lower computer for execution. The lower computer of the first nursing robot drives the motor to rotate according to the scheduling task, and returns the information collected by the encoder to the upper computer for positioning and mapping.

[0094] In one embodiment, the information collected by the encoder is returned to the host computer. Specifically, the encoder collects the number of rotations of the wheels of the first nursing robot, and the distance traveled by the wheels is calculated based on the known circumference of the wheels. The speed information of the first nursing robot is further calculated, and the speed information is sent to the host computer for calculating the mileage information of the first nursing robot.

[0095] In one embodiment, positioning and mapping are performed through SLAM technology. Specifically, mileage information is calculated by an encoder in combination with an inertial measurement unit, and an initial estimate of the position and posture of the first nursing robot is obtained using a nursing robot motion model. Then, laser data is obtained through an onboard lidar sensor and combined with an observation model to accurately correct the position and posture of the first nursing robot to obtain precise positioning of the first nursing robot. Based on the precise positioning, the laser data is added to the grid map. The construction of the entire scene map can be completed by the continuous movement of the first nursing robot in the environment.

[0096] In one embodiment, the scheduling motion trajectory corresponding to the first nursing robot is obtained, and the first nursing robot is controlled to move along the scheduling motion trajectory, and during the movement, a laser radar is used to detect in real time whether there is an obstacle; when an obstacle is detected, the first distance between the first nursing robot and the obstacle at the current moment is obtained, and the first distance is called based on the first preset path planning algorithm to obtain a local obstacle avoidance motion trajectory at the next moment; the scheduling motion trajectory is corrected according to the local obstacle avoidance motion trajectory to obtain a first scheduling motion trajectory.

[0097] In this embodiment, the first preset path planning algorithm is set to the DWA algorithm. By using a laser radar to measure the first distance from the first nursing robot to the obstacle, the DWA algorithm can quickly obtain the optimal solution of the local obstacle avoidance motion trajectory at the next moment to solve the problem in actual usage scenarios that the movement process of the first nursing robot needs to avoid obstacles and achieve the purpose of obstacle avoidance.

[0098] Preferably, during the actual task execution process, the first nursing robot may run out of battery power before completing the scheduling task due to the influence of obstacle avoidance and other factors, and may be unable to complete the current scheduling task; at this time, the background scheduling center will re-call the nursing robot selection mechanism, select another nursing robot to the current position of the first nursing robot, replace it to complete the next scheduling task, and notify the administrator to move the first nursing robot to the charging position for charging.

[0099] In one embodiment, when it is detected that the first nursing robot has completed the scheduling task, the second remaining power of the first nursing robot is detected in real time, and the second remaining power is compared with a preset power threshold; if the second remaining power is less than the preset power threshold, a charging reminder is sent to the management personnel, and the first nursing robot is scheduled to the charging position; if the second remaining power is not less than the preset power threshold, it is determined whether the first nursing robot is in the robot docking position, if not, the first nursing robot is dispatched to the docking position.

[0100] In one embodiment, a power detection module is provided inside the first nursing robot to measure the battery power of the first nursing robot; specifically, the power detection module is provided as a coulomb meter, and a sampling resistor is integrated on the coulomb meter chip. Since different currents flowing through the sampling resistor will generate different voltage differences, the coulomb meter chip converts the voltage difference into current, and the total power consumed by the battery is obtained by integrating the current and time. Each battery has an initial capacity or rated capacity. The second remaining power of the first nursing robot can be obtained by subtracting the total power consumed from the rated capacity.

[0101] In one embodiment, the back-end dispatch center monitors the second remaining power of the first nursing robot in real time, and sends the second remaining power to the back-end database to update the relevant information of the first nursing robot in the database.

[0102] In one embodiment, the first nursing robot is dispatched to a charging position; specifically, the current working status of all current charging positions is obtained, wherein the current working status includes charging and uncharging; a plurality of idle charging positions whose current working status is uncharging are screened out from all the charging positions; the second current position of the first nursing robot is obtained, and the second distances between the second current position and the plurality of idle charging positions are respectively calculated to generate a second distance data set; the shortest second distance is selected from the second distance data set, and the idle charging position corresponding to the shortest second distance is used as the target charging position; a charging scheduling motion trajectory is generated according to the second current position and the target charging position, and the first nursing robot is controlled to move to the target charging position according to the charging scheduling motion trajectory, thereby completing the charging work for the first nursing robot.

[0103] In one embodiment, the first nursing robot is dispatched to a docking position; specifically, the current working status of all current docking positions is obtained, wherein the current working status includes docked and undocked; a plurality of idle docking positions whose current working status is undocked are screened out from all the docking positions; the third current position of the first nursing robot is obtained, and the third distances between the third current position and the plurality of idle docking positions are respectively calculated to generate a third distance data set; the shortest third distance is selected from the third distance data set, and the idle docking position corresponding to the shortest third distance is used as the target docking position; a docking scheduling motion trajectory is generated according to the third current position and the target docking position, and the first nursing robot is controlled to move to the target docking position according to the docking scheduling motion trajectory to complete the docking work of the first nursing robot.

[0104] In summary, the intelligent scheduling method for nursing robots provided in this embodiment can enable the nursing robot group to undertake numerous service tasks in public places while giving full play to the nursing robot's ability of autonomous navigation and automatic obstacle avoidance, making the use of the nursing robot group more convenient and efficient, and providing users with more convenient services. In addition, the reasonable and efficient allocation and use of the nursing robot group can also reduce unnecessary energy consumption and achieve the effect of environmental protection.

[0105] Example 2

[0106] See also Figure 2 , Figure 2 is a structural schematic diagram of an embodiment of an intelligent scheduling device for a nursing robot provided by the present invention, such as Figure 2 As shown, the device includes a nursing robot information acquisition module 201, a first nursing robot selection module 202 and a first nursing robot scheduling module 203, which are specifically as follows:

[0107] The nursing robot information acquisition module 201 is used to obtain the current status information of each nursing robot when receiving a scheduling task of the nursing robot.

[0108] The first nursing robot selection module 202 is used to input the scheduling task and the current state information into the nursing robot selection mechanism, so as to select the first nursing robot based on the nursing robot selection mechanism.

[0109] The first nursing robot scheduling module 203 is used to obtain a first scheduling motion trajectory of the first nursing robot when performing the scheduling task based on a first preset path planning algorithm, so that the first nursing robot moves according to the first scheduling motion trajectory until the scheduling task is completed.

[0110] In one embodiment, the first nursing robot selection module 202 is used to input the scheduling task and the current state information into the nursing robot selection mechanism, so as to select the first nursing robot based on the nursing robot selection mechanism. Specifically, the scheduling task and the current state information are input into the nursing robot selection mechanism, so that the nursing robot selection mechanism can query and obtain multiple first idle nursing robots according to the scheduling task and the current state information, wherein the scheduling task includes the usage time, the task starting position and the task target position, and the current state information includes the first current position, the first remaining power and the idle time; based on the second preset path planning algorithm, the first current position, the task starting position and the task target position are called to generate the scheduling motion trajectory of each first idle nursing robot, and based on the scheduling motion trajectory, the first scheduling power consumption of each first idle nursing robot is predicted; the first remaining power corresponding to each first idle nursing robot is compared with the first scheduling power consumption, so as to select multiple second idle nursing robots that meet the requirement that the first remaining power is greater than the first scheduling power consumption from multiple first idle nursing robots; the scheduling motion trajectory corresponding to each second idle nursing robot is obtained to generate a scheduling motion trajectory data set, the shortest scheduling motion trajectory is selected from the scheduling motion trajectory data set, and the second idle nursing robot corresponding to the shortest scheduling motion trajectory is selected as the selected first nursing robot.

[0111] In one embodiment, the intelligent scheduling device for a nursing robot provided in this embodiment further includes the first nursing robot detection module 204, such as Figure 3 As shown, Figure 3 It is a structural schematic diagram of another embodiment of the intelligent scheduling device of the nursing robot.

[0112] In one embodiment, the first nursing robot detection module 204 is used to detect the second remaining power of the first nursing robot in real time after detecting that the first nursing robot has completed the scheduling task, and compare the second remaining power with a preset power threshold; if the second remaining power is less than the preset power threshold, a charging reminder is sent to the management personnel, and the first nursing robot is scheduled to the charging position; if the second remaining power is not less than the preset power threshold, it is determined whether the first nursing robot is in the robot docking position, if not, the first nursing robot is dispatched to the docking position.

[0113] In one embodiment, the first nursing robot scheduling module 203 is used to obtain a first scheduling motion trajectory of the first nursing robot when performing the scheduling task based on a first preset path planning algorithm; specifically, obtain the scheduling motion trajectory corresponding to the first nursing robot, and control the first nursing robot to move along the scheduling motion trajectory, and detect in real time whether there are obstacles during the movement; when an obstacle is detected, obtain the first distance between the first nursing robot and the obstacle at the current moment, call the first distance based on the first preset path planning algorithm, and obtain the local obstacle avoidance motion trajectory at the next moment; correct the scheduling motion trajectory according to the local obstacle avoidance motion trajectory to obtain the first scheduling motion trajectory.

[0114] In one embodiment, the first nursing robot selection module 202 is used to predict the first scheduling power consumption of each first idle nursing robot based on the scheduling motion trajectory; specifically, obtain the simulated scheduling power consumption per kilometer, and based on the scheduling motion trajectory, obtain the trajectory length of the scheduling motion trajectory; according to the trajectory length and the simulated scheduling power consumption, predict the first scheduling power consumption of each first idle nursing robot.

[0115] In one embodiment, the first nursing robot detection module 204 is used to dispatch the first nursing robot to a charging position; specifically, obtain the current working status of all current charging positions, wherein the current working status includes charging and not charging; select a plurality of idle charging positions whose current working status is not charging from all the charging positions; obtain the second current position of the first nursing robot, calculate the second distances between the second current position and the plurality of idle charging positions, and generate a second distance data set; select the shortest second distance from the second distance data set, and use the idle charging position corresponding to the shortest second distance as the target charging position; generate a charging scheduling motion trajectory based on the second current position and the target charging position, and control the first nursing robot to move to the target charging position according to the charging scheduling motion trajectory.

[0116] In one embodiment, the first nursing robot detection module 204 is used to dispatch the first nursing robot to a docking position; specifically, obtain the current working status of all current docking positions, wherein the current working status includes docked and undocked; filter out multiple idle docking positions whose current working status is undocked from all the docking positions; obtain the third current position of the first nursing robot, and respectively calculate the third distances between the third current position and the multiple idle docking positions to generate a third distance data set; select the shortest third distance from the third distance data set, and use the idle docking position corresponding to the shortest third distance as the target docking position; generate a docking scheduling motion trajectory based on the third current position and the target docking position, and control the first nursing robot to move to the target docking position according to the docking scheduling motion trajectory.

[0117] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0118] It should be noted that the above-mentioned embodiment of the intelligent scheduling device of the nursing robot is only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0119] Based on the above-mentioned embodiment of the intelligent scheduling method for a nursing robot, another embodiment of the present invention provides an intelligent scheduling terminal device for a nursing robot, the intelligent scheduling terminal device for a nursing robot comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the intelligent scheduling method for a nursing robot according to any embodiment of the present invention is implemented.

[0120] Exemplarily, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the intelligent scheduling terminal device of the nursing robot.

[0121] The intelligent scheduling terminal device of the nursing robot can be a computing device such as a desktop computer, a notebook, a palm computer, a cloud server, etc. The intelligent scheduling terminal device of the nursing robot can include, but is not limited to, a processor and a memory.

[0122] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the intelligent dispatching terminal device of the nursing robot, and uses various interfaces and lines to connect various parts of the intelligent dispatching terminal device of the entire nursing robot.

[0123] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the intelligent scheduling terminal device of the nursing robot by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0124] Based on the above-mentioned embodiment of the intelligent scheduling method for a nursing robot, another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the intelligent scheduling method for a nursing robot of any embodiment of the present invention.

[0125] In this embodiment, the storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0126] In summary, the present invention discloses an intelligent scheduling method, device, equipment and storage medium for a nursing robot, which obtains the current status information of each nursing robot when receiving a scheduling task of a nursing robot; inputs the scheduling task and the current status information into a nursing robot selection mechanism, so that a first nursing robot is selected based on the nursing robot selection mechanism; based on a first preset path planning algorithm, a first scheduling motion trajectory of the first nursing robot when performing the scheduling task is obtained, so that the first nursing robot moves according to the first scheduling motion trajectory until the scheduling task is completed. Compared with the prior art, the technical solution of the present invention can realize the reasonable and efficient allocation and use of a nursing robot group, while reducing unnecessary energy consumption and achieving an environmental protection effect.

[0127] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention. These improvements and substitutions should also be regarded as the scope of protection of the present invention.

Claims

1. An intelligent scheduling method for a nursing robot, characterized in that: include: When receiving a scheduling task of a nursing robot, obtain the current status information of each nursing robot; Inputting the scheduling task and the current state information into a nursing robot selection mechanism, so that a first nursing robot is selected based on the nursing robot selection mechanism; Based on a first preset path planning algorithm, a first scheduling motion trajectory of the first nursing robot when performing the scheduling task is obtained, so that the first nursing robot moves according to the first scheduling motion trajectory until the scheduling task is completed; The step of inputting the scheduling task and the current state information into a nursing robot selection mechanism so as to select a first nursing robot based on the nursing robot selection mechanism specifically includes: The scheduling task and the current state information are input into the nursing robot selection mechanism, so that the nursing robot selection mechanism can query and obtain multiple first idle nursing robots according to the scheduling task and the current state information, wherein the scheduling task includes the usage time, the task starting position and the task target position, and the current state information includes the first current position, the first remaining power and the idle time; the first current position, the task starting position and the task target position are called based on the second preset path planning algorithm to generate a scheduling motion trajectory of each first idle nursing robot, and based on the scheduling motion trajectory, the first scheduling power consumption of each first idle nursing robot is predicted; the first remaining power corresponding to each first idle nursing robot is compared with the first scheduling power consumption, so as to select multiple second idle nursing robots that meet the requirement that the first remaining power is greater than the first scheduling power consumption from multiple first idle nursing robots; the scheduling motion trajectory corresponding to each second idle nursing robot is obtained to generate a scheduling motion trajectory data set, the shortest scheduling motion trajectory is selected from the scheduling motion trajectory data set, and the second idle nursing robot corresponding to the shortest scheduling motion trajectory is selected as the first nursing robot.

2. The intelligent scheduling method of a nursing robot according to claim 1, characterized in that: After completing the scheduling task, it also includes: When it is detected that the first nursing robot completes the scheduling task, detecting the second remaining power of the first nursing robot in real time, and comparing the second remaining power with a preset power threshold; If the second remaining power is less than a preset power threshold, a charging reminder is sent to the manager, and the first nursing robot is dispatched to a charging position; If the second remaining power is not less than the preset power threshold, it is determined whether the first nursing robot is in the robot docking position. If not, the first nursing robot is dispatched to the docking position.

3. The intelligent scheduling method of a nursing robot according to claim 1, characterized in that: Based on a first preset path planning algorithm, obtaining a first scheduling motion trajectory of the first nursing robot when performing the scheduling task specifically includes: Obtaining a dispatching motion trajectory corresponding to the first nursing robot, and controlling the first nursing robot to move along the dispatching motion trajectory, and detecting in real time whether there are obstacles during the movement; When an obstacle is detected, a first distance between the first nursing robot and the obstacle at the current moment is obtained, and the first distance is called based on the first preset path planning algorithm to obtain a local obstacle avoidance motion trajectory at the next moment; The scheduling motion trajectory is corrected according to the local obstacle avoidance motion trajectory to obtain a first scheduling motion trajectory.

4. The intelligent scheduling method of a nursing robot according to claim 1, characterized in that: Predicting the first scheduling power consumption of each first idle nursing robot based on the scheduling motion trajectory specifically includes: The simulated dispatching power consumption per kilometer is obtained, and based on the dispatching motion trajectory, the trajectory length of the dispatching motion trajectory is obtained; The first scheduled power consumption of each first idle nursing robot is predicted based on the trajectory length and the simulated scheduled power consumption.

5. The intelligent scheduling method of a nursing robot according to claim 2, characterized in that: Dispatching the first nursing robot to a charging position specifically includes: Obtaining the current working status of all current charging positions, wherein the current working status includes charging and not charging; Filtering out a plurality of idle charging positions whose current working status is uncharged from all the charging positions; Obtaining a second current position of the first nursing robot, respectively calculating second distances between the second current position and the plurality of idle charging positions, and generating a second distance data set; Selecting the shortest second distance from the second distance data set, and using the idle charging position corresponding to the shortest second distance as the target charging position; A charging scheduling motion trajectory is generated according to the second current position and the target charging position, and the first nursing robot is controlled to move to the target charging position according to the charging scheduling motion trajectory.

6. The intelligent scheduling method of a nursing robot according to claim 2, characterized in that: Dispatching the first nursing robot to a docking position specifically includes: Obtain the current working status of all current docking positions, wherein the current working status includes docked and undocked; Filtering out a plurality of idle docking positions whose current working status is not docked from all the docking positions; Acquire a third current position of the first nursing robot, calculate third distances between the third current position and the plurality of idle parking positions, and generate a third distance data set; Selecting the shortest third distance from the third distance data set, and taking the idle parking position corresponding to the shortest third distance as the target parking position; A docking scheduling motion trajectory is generated based on the third current position and the target docking position, and the first nursing robot is controlled to move to the target docking position according to the docking scheduling motion trajectory.

7. An intelligent scheduling device for a nursing robot, characterized in that: include: A nursing robot information acquisition module, a first nursing robot selection module and a first nursing robot scheduling module; Wherein, the nursing robot information acquisition module is used to obtain the current status information of each nursing robot when receiving the scheduling task of the nursing robot; The first nursing robot selection module is used to input the scheduling task and the current state information into a nursing robot selection mechanism, so that a first nursing robot is selected based on the nursing robot selection mechanism; The first nursing robot scheduling module is used to obtain a first scheduling motion trajectory of the first nursing robot when performing the scheduling task based on a first preset path planning algorithm, so that the first nursing robot moves according to the first scheduling motion trajectory until the scheduling task is completed; The step of inputting the scheduling task and the current state information into a nursing robot selection mechanism so as to select a first nursing robot based on the nursing robot selection mechanism specifically includes: The scheduling task and the current state information are input into the nursing robot selection mechanism, so that the nursing robot selection mechanism can query and obtain multiple first idle nursing robots according to the scheduling task and the current state information, wherein the scheduling task includes the usage time, the task starting position and the task target position, and the current state information includes the first current position, the first remaining power and the idle time; the first current position, the task starting position and the task target position are called based on the second preset path planning algorithm to generate a scheduling motion trajectory of each first idle nursing robot, and based on the scheduling motion trajectory, the first scheduling power consumption of each first idle nursing robot is predicted; the first remaining power corresponding to each first idle nursing robot is compared with the first scheduling power consumption, so as to select multiple second idle nursing robots that meet the requirement that the first remaining power is greater than the first scheduling power consumption from multiple first idle nursing robots; the scheduling motion trajectory corresponding to each second idle nursing robot is obtained to generate a scheduling motion trajectory data set, the shortest scheduling motion trajectory is selected from the scheduling motion trajectory data set, and the second idle nursing robot corresponding to the shortest scheduling motion trajectory is selected as the first nursing robot.

8. A terminal device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the intelligent scheduling method of the nursing robot as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the intelligent scheduling method for the nursing robot as described in any one of claims 1 to 6.

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