CN115157263A - Intelligent scheduling method, device, equipment and storage medium for nursing robot - Google Patents
Intelligent scheduling method, device, equipment and storage medium for nursing robot Download PDFInfo
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Abstract
The invention discloses an intelligent scheduling method, device, equipment and storage medium of nursing robots, which are used for acquiring the current state information of each nursing robot when a scheduling task of the nursing robot is received; inputting the scheduling task and the current state information into a care robot selection mechanism such that a first care robot is selected based on the care robot selection mechanism; and obtaining a first scheduling motion track of the first nursing robot when the first nursing robot executes the scheduling task based on a first preset path planning algorithm, so that the first nursing robot moves according to the first scheduling motion track until the scheduling task is completed. Compared with the prior art, the technical scheme of the invention can realize reasonable and efficient distribution and use of nursing machine groups, and simultaneously reduce unnecessary energy consumption and achieve the effect of environmental protection.
Description
Technical Field
The invention relates to the technical field of mobile robot control, in particular to an intelligent scheduling method, device, equipment and storage medium for a nursing robot.
Background
With the rapid development of science and technology, more and more intelligent devices are appeared to provide more convenient services for people. In public use places such as hospitals and nursing homes, nursing robot groups undertake numerous service tasks, for example, a nursing robot based on laser SLAM can replace a wheelchair to complete auxiliary moving tasks, and can also provide functions of assisting standing, lying and the like, so that the user can move out of a journey conveniently. Therefore, how to select a proper nursing robot from the nursing robot group to execute tasks and realize the efficient use of the nursing robot group becomes a technical problem which is urgently needed to be solved at present.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the intelligent scheduling method, the intelligent scheduling device, the intelligent scheduling equipment and the intelligent scheduling storage medium for the nursing robots are provided, the nursing robots are reasonably and efficiently distributed and used, meanwhile, unnecessary energy consumption is reduced, and the environment-friendly effect is achieved.
In order to solve the technical problem, the invention provides an intelligent scheduling method of a nursing robot, which comprises the following steps:
when a scheduling task of the nursing robot is received, current state information of each nursing robot is obtained;
inputting the scheduling task and the current state information into a care robot selection mechanism such that a first care robot is selected based on the care robot selection mechanism;
and obtaining a first scheduling motion track of the first nursing robot when the first nursing robot executes the scheduling task based on a first preset path planning algorithm, so that the first nursing robot moves according to the first scheduling motion track until the scheduling task is completed.
In one possible implementation manner, the scheduling task and the current state information are input into a nursing robot selection mechanism, so that the selecting a first nursing robot based on the nursing robot selection mechanism specifically includes:
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 comprises use time, a task starting position and a task target position, and the current state information comprises a first current position, a first remaining capacity and idle time;
calling the first current position, the task starting position and the task target position based on a second preset path planning algorithm, generating a scheduling motion track of each first idle nursing robot, and predicting first scheduling power consumption of each first idle nursing robot based on the scheduling motion track;
comparing the first residual electric quantity corresponding to each first idle nursing robot with the first scheduling consumed electric quantity so as to screen a plurality of second idle nursing robots meeting the condition that the first residual electric quantity is larger than the first scheduling consumed electric quantity from the plurality of first idle nursing robots;
and acquiring a scheduling motion trail corresponding to each second idle nursing robot, generating a scheduling motion trail data set, selecting the shortest scheduling motion trail from the scheduling motion trail data set, and taking the second idle nursing robot corresponding to the shortest scheduling motion trail as the selected first nursing robot.
In a possible implementation manner, after the scheduling task is completed, the method further includes:
when the first nursing robot is detected to finish the scheduling task, detecting a second residual electric quantity of the first nursing robot in real time, and comparing the second residual electric quantity with a preset electric quantity threshold value;
if the second remaining electric quantity is smaller than a preset electric quantity threshold value, sending a charging prompt to a manager, and dispatching the first nursing robot to a charging position;
if the second remaining electric quantity is not smaller than a preset electric quantity threshold value, whether the first nursing robot is located at a robot parking position or not is judged, and if not, the first nursing robot is dispatched to the parking position.
In a possible implementation manner, obtaining a first scheduling motion trajectory of the first nursing robot when executing the scheduling task based on a first preset path planning algorithm specifically includes:
acquiring a scheduling motion track corresponding to the first nursing robot, controlling the first nursing robot to move along the scheduling motion track, and detecting whether an obstacle exists in real time in the moving process;
when an obstacle is detected to exist, acquiring a first distance between the first nursing robot and the obstacle at the current moment, and calling the first distance based on the first preset path planning algorithm to obtain a local obstacle avoidance motion track at the next moment;
and correcting the scheduling motion track according to the local obstacle avoidance motion track to obtain a first scheduling motion track.
In a possible implementation manner, predicting the first scheduled power consumption of each first idle nursing robot based on the scheduled motion trajectory specifically includes:
acquiring the power consumption of each kilometer of simulation scheduling, and acquiring the track length of the scheduling motion track based on the scheduling motion track;
and predicting the first scheduling consumed electric quantity of each first idle nursing robot according to the track length and the simulation scheduling consumed electric quantity.
In a possible implementation manner, the dispatching the first nursing robot to a charging position specifically includes:
acquiring current working states of all current charging positions, wherein the current working states comprise charging and non-charging;
screening a plurality of idle charging positions with uncharged current working states from all the charging positions;
acquiring 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 taking the idle charging position corresponding to the shortest second distance as a target charging position;
and generating a charging scheduling motion track according to the second current position and the target charging position, and controlling the first nursing robot to move to the target charging position according to the charging scheduling motion track.
In a possible implementation manner, the dispatching the first nursing robot to the parking position specifically includes:
acquiring current working states of all current parking positions, wherein the current working states comprise parked positions and non-parked positions;
screening a plurality of idle parking positions which are not parked in the current working state from all the parking positions;
acquiring a third current position of the first nursing robot, respectively calculating third distances between the third current position and the plurality of idle parking positions, and generating 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 a target parking position;
and generating a parking scheduling motion trail according to the third current position and the target parking position, and controlling the first nursing robot to move to the target parking position according to the parking scheduling motion trail.
The embodiment of the invention also provides an intelligent scheduling device of the nursing robot, which comprises: the system comprises a nursing robot information acquisition module, a first nursing robot selection module and a first nursing robot scheduling module;
the nursing robot information acquisition module is used for acquiring current state information of each nursing robot when a scheduling task of the nursing robot is received;
the first nursing robot selection module is used for 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;
the first nursing robot scheduling module is configured to obtain a first scheduling motion trajectory of the first nursing robot when the first nursing robot executes 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.
In a possible implementation manner, the first nursing robot selecting module is configured to input the scheduling task and the current state information into a nursing robot selection mechanism, so that the selecting of the first nursing robot based on the nursing robot selection mechanism specifically includes:
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 comprises use time, a task starting position and a task target position, and the current state information comprises a first current position, a first remaining capacity and idle time;
calling the first current position, the task starting position and the task target position based on a second preset path planning algorithm, generating a scheduling motion track of each first idle nursing robot, and predicting first scheduling power consumption of each first idle nursing robot based on the scheduling motion track;
comparing the first remaining power and the first scheduling consumed power corresponding to each first idle nursing robot, so as to screen a plurality of second idle nursing robots meeting the condition that the first remaining power is greater than the first scheduling consumed power from the plurality of first idle nursing robots;
and acquiring a scheduling motion track corresponding to each second idle nursing robot, generating a scheduling motion track data set, selecting the shortest scheduling motion track from the scheduling motion track data set, and taking the second idle nursing robot corresponding to the shortest scheduling motion track as the selected first nursing robot.
The intelligent scheduling device of the nursing robot provided by the embodiment of the invention further comprises: the first nursing robot detection module;
the first nursing robot detection module is used for detecting a second residual electric quantity of the first nursing robot in real time after the first nursing robot is detected to complete the scheduling task, and comparing the second residual electric quantity with a preset electric quantity threshold value; if the second remaining electric quantity is smaller than a preset electric quantity threshold value, sending a charging prompt to a manager, and dispatching the first nursing robot to a charging position; if the second remaining electric quantity is not smaller than a preset electric quantity threshold value, whether the first nursing robot is located at a robot parking position or not is judged, and if not, the first nursing robot is dispatched to the parking position.
In a possible implementation manner, the first nursing robot scheduling module is configured to obtain a first scheduling motion trajectory of the first nursing robot when the first nursing robot executes the scheduling task based on a first preset path planning algorithm, and specifically includes:
acquiring a scheduling motion track corresponding to the first nursing robot, controlling the first nursing robot to move along the scheduling motion track, and detecting whether an obstacle exists in real time in the moving process;
when the obstacle is detected to exist, acquiring a first distance between the first nursing robot and the obstacle at the current moment, and calling the first distance based on the first preset path planning algorithm to obtain a local obstacle avoidance motion track at the next moment;
and correcting the scheduling motion track according to the local obstacle avoidance motion track to obtain a first scheduling motion track.
In a possible implementation manner, the first nursing robot selecting module is configured to predict a first scheduled power consumption amount of each first idle nursing robot based on the scheduled motion trajectory, and specifically includes:
acquiring the consumed electric quantity of the simulation scheduling of each kilometer, and acquiring the track length of the scheduling motion track based on the scheduling motion track;
and predicting the first scheduling consumed electric quantity of each first idle nursing robot according to the track length and the simulation scheduling consumed electric quantity.
In a possible implementation manner, the first nursing robot detecting module is configured to dispatch the first nursing robot to a charging location, and specifically includes:
acquiring current working states of all current charging positions, wherein the current working states comprise charging and non-charging;
screening a plurality of idle charging positions with uncharged current working states from all the charging positions;
acquiring 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 taking the idle charging position corresponding to the shortest second distance as a target charging position;
generating a charging scheduling motion track according to the second current position and the target charging position, and controlling the first nursing robot to move to the target charging position according to the charging scheduling motion track.
In a possible implementation manner, the first nursing robot detecting module is configured to dispatch the first nursing robot to a parking position, and specifically includes:
acquiring current working states of all current parking positions, wherein the current working states comprise parked positions and non-parked positions;
screening a plurality of idle parking positions which are not parked in the current working state from all the parking positions;
acquiring a third current position of the first nursing robot, respectively calculating third distances between the third current position and the plurality of idle parking positions, and generating 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 a target parking position;
and generating a parking scheduling motion track according to the third current position and the target parking position, and controlling the first nursing robot to move to the target parking position according to the parking scheduling motion track.
The embodiment of the present invention further provides a terminal device, which 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, the processor implements the intelligent scheduling method for a nursing robot as described in any one of the above items.
The embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, and when the computer program runs, a device where the computer-readable storage medium is located is controlled to execute the intelligent scheduling method for a nursing robot according to any one of the above-mentioned methods.
Compared with the prior art, the intelligent scheduling method, the intelligent scheduling device, the intelligent scheduling equipment and the intelligent scheduling storage medium for the nursing robot have the following beneficial effects:
when a scheduling task of the nursing robot is received, current state information of each nursing robot is obtained; inputting the scheduling task and the current state information into a care robot selection mechanism such that a first care robot is selected based on the care robot selection mechanism; and obtaining a first scheduling motion track of the first nursing robot when the first nursing robot executes the scheduling task based on a first preset path planning algorithm, so that the first nursing robot moves according to the first scheduling motion track until the scheduling task is completed. Compared with the prior art, the technical scheme of the invention can realize reasonable and efficient distribution and use of nursing machine groups, and simultaneously reduce unnecessary energy consumption and achieve the effect of environmental protection.
Drawings
FIG. 1 is a schematic flow chart diagram illustrating an embodiment of an intelligent scheduling method for a nursing robot according to the present invention;
fig. 2 is a schematic structural diagram of an embodiment of an intelligent scheduling device for a nursing robot according to the present invention;
fig. 3 is a schematic structural diagram of another embodiment of an intelligent scheduling apparatus for a nursing robot according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
Example 1
Referring to fig. 1, fig. 1 is a schematic flowchart of an embodiment of an intelligent scheduling method for a nursing robot according to the present invention, as shown in fig. 1, the method includes steps 101 to 103, which are as follows:
step 101: and when the scheduling task of the nursing robot is received, acquiring the current state information of each nursing robot.
In one embodiment, when a nursing robot needs to be called, a user needs to fill a use requirement into a front-end APP, convert the use requirement into a scheduling task based on the front-end APP, and send the scheduling task to a rear-end scheduling center for processing; the scheduling task comprises information such as using time, a task starting position and a task target position.
Preferably, for the determination of the service time in the scheduling task, before the nursing robot is put into use, the service conditions of the nursing robot in different time periods of all routes are simulated, and the corresponding running time is recorded, so that the time required by the nursing robot to execute the scheduling task is estimated, and in order to prevent the actual service time from being longer than the estimated time due to the condition of obstacle avoidance and the like, the estimated time is expanded appropriately, thereby avoiding the conditions that the electric quantity of the nursing robot is insufficient due to the error between the estimated service time and the actual service time, or the nursing robot is overlapped with the subsequent scheduling task, and the like.
In one embodiment, after receiving the scheduling task, the back-end scheduling center calls the current state information of each nursing robot to the back-end database, and receives the current state information of each nursing robot returned by the back-end database; the current state information includes information such as a first current position, a first remaining power amount, and idle time.
Step 102: the scheduling task and the current state information are input into a care robot selection mechanism such that a first care robot is selected based on the care robot selection mechanism.
In one embodiment, the scheduling task and the current state information are input into a nursing robot selection mechanism, so that the nursing robot selection mechanism queries a plurality of first idle nursing robots according to the scheduling task and the current state information.
Specifically, the use time in the scheduling task is compared with the idle time in the current state information, if the use time is within the idle time range, the nursing robot is considered to be a first idle nursing robot, and if the use time is not within the idle time range, the nursing robot is considered not to be the first idle nursing robot.
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, a scheduling motion track of each first idle nursing robot is generated, and first scheduling power consumption of each first idle nursing robot is predicted based on the scheduling motion track;
specifically, after a plurality of first idle nursing robots are obtained, scheduling motion trail planning needs to be carried out on the plurality of first idle nursing robots, a moving path is planned based on a second preset path planning algorithm by determining a task starting position and a task target position, and a scheduling motion trail of each first idle nursing robot is generated; wherein the second pre-determined path planning algorithm comprises an Ah path planning algorithm.
In an embodiment, the first scheduled power consumption of each first idle nursing robot is predicted based on the scheduled motion trajectory after the scheduled motion trajectory is generated.
Specifically, before the first idle nursing robot is put into use, consumption of electric quantity per kilometer when users with different weights are carried by the first idle nursing robot is simulated, consumption of electric quantity per kilometer when the users with different weights are used in all routes is analyzed, so that simulated scheduling consumed electric quantity per kilometer when scheduling tasks are executed is obtained, the track length of the scheduling motion track is obtained based on the scheduling motion track, and first scheduling consumed electric quantity of each first idle nursing robot is predicted according to the track length and the simulated scheduling consumed electric quantity.
Preferably, in order to prevent the power consumption of the simulated scheduling from being less than the power consumption in actual use due to the fact that the first idle nursing robot uses the auxiliary standing function, the auxiliary lying function and the like, in this embodiment, the preset power consumption is further increased for the power consumption of the simulated scheduling, so that the power consumption of the simulated scheduling can meet the power requirement in actual use, and the use reliability of the first idle nursing robot is improved.
In one embodiment, a first remaining power of each first idle nursing robot is acquired based on current state information of the nursing robot, and the first remaining power corresponding to each first idle nursing robot is compared with the first scheduling consumed power, so that a plurality of second idle nursing robots meeting the condition that the first remaining power is greater than the first scheduling consumed power are screened out from the plurality of first idle nursing robots; the second idle nursing robot is the nursing robot which meets the service time of the scheduling task and meets the scheduling power consumption required by the scheduling task.
In one embodiment, a scheduling motion trajectory corresponding to each second idle nursing robot is obtained, a scheduling motion trajectory data set is generated, a 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 used as the selected first nursing robot.
Preferably, the number of the first nursing robots may be one, and the number of the first nursing robots may be plural.
Step 103: and obtaining a first scheduling motion track of the first nursing robot when the first nursing robot executes the scheduling task based on a first preset path planning algorithm, so that the first nursing robot moves according to the first scheduling motion track until the scheduling task is completed.
In one embodiment, the nursing robot is a nursing robot based on a laser SLAM, the nursing robot based on the laser SLAM adopts a raspberry and a 4B as an upper computer and an STM32 single chip microcomputer as a lower computer, an ROS system is carried, sensors such as a laser radar and an inertia measurement unit are adopted to collect information, an environment where the robot is located is mapped and the position where the nursing robot is located through an SLAM technology, and path planning is carried out based on a first preset path planning algorithm.
In one embodiment, after a first nursing robot is selected, the scheduling task is sent to an upper computer of the first nursing robot, after the selected nursing robot receives the scheduling task, a scheduling motion track from a first current position to a task starting position and during operation is planned, the scheduling task is sent to a lower computer to be executed, the lower computer of the first nursing robot drives a motor to rotate according to the scheduling task, and information collected by an encoder is returned to the upper computer for positioning and mapping.
In one embodiment, the information collected by the encoder is returned to the upper computer, specifically, the number of turns of the wheel of the first nursing robot is collected by the encoder, the driving distance of the wheel is calculated according to the known circumference of the wheel, the speed information of the first nursing robot is further calculated, and the speed information is sent to the upper computer and used for calculating the mileage information of the first nursing robot.
In one embodiment, positioning and mapping are performed through an SLAM technology, specifically, mileage information is obtained through calculation of an encoder and an inertia measurement unit, a primary pose estimation of a first nursing robot is obtained through a nursing robot motion model, then laser data obtained through a carried laser radar sensor is combined with an observation model to accurately correct the pose of the first nursing robot to obtain accurate positioning of the first nursing robot, the laser data are added into a grid map on the basis of the accurate positioning, and construction of a whole scene map can be completed through continuous motion of the first nursing robot in an environment.
In one embodiment, a scheduling motion track corresponding to the first nursing robot is obtained, the first nursing robot is controlled to move along the scheduling motion track, and whether an obstacle exists is detected in real time by using a laser radar in the moving process; when the obstacle is detected to exist, acquiring a first distance between the first nursing robot and the obstacle at the current moment, and calling the first distance based on the first preset path planning algorithm to obtain a local obstacle avoidance motion track at the next moment; and correcting the scheduling motion track according to the local obstacle avoidance motion track to obtain a first scheduling motion track.
In this embodiment, the first preset path planning algorithm is set to be a DWA algorithm, the first distance from the first nursing robot to the obstacle is measured by using the laser radar, and the optimal solution of the local obstacle avoidance motion trajectory at the next moment can be rapidly obtained through the DWA algorithm, so that the problem that the obstacle needs to be avoided in the moving process of the first nursing robot in the actual use scene is solved, and the purpose of avoiding the obstacle is achieved.
Preferably, in the actual task execution process, due to the influence of conditions such as obstacle avoidance, the first nursing robot may cause that the electric quantity is too low when the first nursing robot has not executed the scheduling task, and cannot execute the current scheduling task; at the moment, the background scheduling center calls the nursing robot selection mechanism again, other nursing robots are selected to the current position of the first nursing robot to replace the current position of the first nursing robot to complete the next scheduling task, and the administrator is informed to move the first nursing robot to the charging position to perform charging.
In one embodiment, after the first nursing robot is detected to complete the scheduling task, a 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 electric quantity is smaller than a preset electric quantity threshold value, sending a charging prompt to a manager, and dispatching the first nursing robot to a charging position; if the second residual electric quantity is not smaller than a preset electric quantity threshold value, whether the first nursing robot is located at a robot parking position or not is judged, and if not, the first nursing robot is dispatched to the parking position.
In one embodiment, an electric quantity detection module is arranged in the first nursing robot and used for measuring the electric quantity of a battery of the first nursing robot; specifically, the electric quantity detection module is set as a coulometer, a sampling resistor is integrated on a coulometer chip, different differential pressures can be generated due to different currents flowing through the sampling resistor, the coulometer chip converts the differential pressures into currents, and the total electric quantity consumed by the battery is obtained by integrating the currents and time. Each battery has an initial capacity or a rated capacity, and the second remaining capacity of the first nursing robot can be obtained by subtracting the total consumed electric quantity from the rated capacity.
In an embodiment, the back-end scheduling 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, so as to update the related information of the first nursing robot in the database.
In one embodiment, the first care robot is dispatched to a charging location; specifically, current working states of all current charging positions are obtained, wherein the current working states comprise charging and non-charging; screening a plurality of idle charging positions with uncharged current working states from all the charging positions; acquiring 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 taking the idle charging position corresponding to the shortest second distance as a target charging position; and generating a charging scheduling motion track according to the second current position and the target charging position, and controlling the first nursing robot to move to the target charging position according to the charging scheduling motion track, so as to finish the charging work of the first nursing robot.
In an embodiment, the first care robot is dispatched to a parking location; specifically, current working states of all current parking positions are obtained, wherein the current working states comprise parked positions and non-parked positions; screening a plurality of idle parking positions which are not parked in the current working state from all the parking positions; acquiring a third current position of the first nursing robot, respectively calculating third distances between the third current position and the plurality of idle parking positions, and generating 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 a target parking position; and generating a parking scheduling motion track according to the third current position and the target parking position, and controlling the first nursing robot to move to the target parking position according to the parking scheduling motion track to finish the parking work of the first nursing robot.
In conclusion, according to the intelligent scheduling method for the nursing robot provided by the embodiment, the self-navigation and automatic obstacle avoidance capabilities of the nursing robot can be fully exerted while a plurality of service tasks of people are undertaken by the nursing robot group in a public place, so that the nursing robot group is more convenient and efficient to use, more convenient and faster services are provided for users, unnecessary energy consumption can be reduced by reasonable and efficient distribution and use of the nursing robot group, and the effect of environmental protection is achieved.
Example 2
Referring to fig. 2, fig. 2 is a schematic structural diagram of an embodiment of an intelligent scheduling apparatus for a nursing robot provided by the present invention, and as shown in fig. 2, the apparatus includes a nursing robot information obtaining module 201, a first nursing robot selecting module 202, and a first nursing robot scheduling module 203, which are specifically as follows:
the nursing robot information obtaining module 201 is configured to obtain current state information of each nursing robot when a scheduling task of the nursing robot is received.
The first nursing robot selecting module 202 is configured to input the scheduling task and the current state information into a nursing robot selecting mechanism, so that a first nursing robot is selected based on the nursing robot selecting mechanism.
The first nursing robot scheduling module 203 is configured to obtain a first scheduling motion trajectory of the first nursing robot when the first nursing robot executes 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.
In an embodiment, the first nursing robot selecting module 202 is configured to input the scheduling task and the current state information into a nursing robot selecting mechanism, so that the first nursing robot is selected based on the nursing robot selecting mechanism. Specifically, the scheduling task and the current state 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 state information, wherein the scheduling task comprises use time, a task starting position and a task target position, and the current state information comprises a first current position, a first remaining power and idle time; calling the first current position, the task starting position and the task target position based on a second preset path planning algorithm, generating a scheduling motion track of each first idle nursing robot, and predicting first scheduling power consumption of each first idle nursing robot based on the scheduling motion track; comparing the first remaining power and the first scheduling consumed power corresponding to each first idle nursing robot, so as to screen a plurality of second idle nursing robots meeting the condition that the first remaining power is greater than the first scheduling consumed power from the plurality of first idle nursing robots; and acquiring a scheduling motion trail corresponding to each second idle nursing robot, generating a scheduling motion trail data set, selecting the shortest scheduling motion trail from the scheduling motion trail data set, and taking the second idle nursing robot corresponding to the shortest scheduling motion trail as the selected first nursing robot.
In an embodiment, the intelligent scheduling apparatus for nursing robots provided in this embodiment further includes the first nursing robot detecting module 204, as shown in fig. 3, and fig. 3 is a schematic structural diagram of another embodiment of the intelligent scheduling apparatus for nursing robots.
In an embodiment, the first nursing robot detecting module 204 is configured to detect a second remaining power of the first nursing robot in real time after the first nursing robot is detected to complete the scheduling task, and compare the second remaining power with a preset power threshold; if the second remaining electric quantity is smaller than a preset electric quantity threshold value, sending a charging prompt to a manager, and dispatching the first nursing robot to a charging position; if the second remaining electric quantity is not smaller than a preset electric quantity threshold value, whether the first nursing robot is located at a robot parking position or not is judged, and if not, the first nursing robot is dispatched to the parking position.
In an embodiment, the first nursing robot scheduling module 203 is configured to obtain a first scheduling motion trajectory of the first nursing robot when executing the scheduling task based on a first preset path planning algorithm; specifically, a dispatching motion track corresponding to the first nursing robot is obtained, the first nursing robot is controlled to move along the dispatching motion track, and whether an obstacle exists or not is detected in real time in the moving process; when the obstacle is detected to exist, acquiring a first distance between the first nursing robot and the obstacle at the current moment, and calling the first distance based on the first preset path planning algorithm to obtain a local obstacle avoidance motion track at the next moment; and correcting the scheduling motion track according to the local obstacle avoidance motion track to obtain a first scheduling motion track.
In an embodiment, the first nursing robot selecting module 202 is configured to predict a first scheduled power consumption amount of each first idle nursing robot based on the scheduled motion trajectory; specifically, the power consumption of the simulation scheduling of each kilometer is obtained, and the track length of the scheduling motion track is obtained based on the scheduling motion track; and predicting the first scheduling consumed electric quantity of each first idle nursing robot according to the track length and the simulation scheduling consumed electric quantity.
In one embodiment, the first nursing robot detecting module 204 is configured to dispatch the first nursing robot to a charging location; specifically, current working states of all current charging positions are obtained, wherein the current working states comprise charging and non-charging; screening a plurality of idle charging positions with uncharged current working states from all the charging positions; acquiring 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 taking the idle charging position corresponding to the shortest second distance as a target charging position; and generating a charging scheduling motion track according to the second current position and the target charging position, and controlling the first nursing robot to move to the target charging position according to the charging scheduling motion track.
In one embodiment, the first nursing robot detecting module 204 is configured to dispatch the first nursing robot to a docking location; specifically, the current working states of all the current parking positions are obtained, wherein the current working states comprise parked positions and non-parked positions; screening a plurality of idle parking positions which are not parked in the current working state from all the parking positions; acquiring a third current position of the first nursing robot, respectively calculating third distances between the third current position and the plurality of idle parking positions, and generating 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 a target parking position; and generating a parking scheduling motion trail according to the third current position and the target parking position, and controlling the first nursing robot to move to the target parking position according to the parking scheduling motion trail.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working process of the apparatus described above may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
It should be noted that the above-mentioned embodiment of the intelligent scheduling apparatus for a nursing robot is merely illustrative, wherein the modules described as separate components may or may not be physically separate, and the components displayed as modules may or may not be physical units, that is, may be located in one place, or may also be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
On the basis of the above-mentioned embodiment of the intelligent scheduling method for the nursing robot, another embodiment of the present invention provides an intelligent scheduling terminal device for a nursing robot, which 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, the intelligent scheduling method for the nursing robot according to any one of the embodiments of the present invention is implemented.
Illustratively, the computer program may be partitioned in this embodiment into one or more modules that are stored in the memory and executed by the processor to implement the invention. The one or more modules can be a series of instruction segments of the computer program capable of completing specific functions, and the instruction segments are used for describing the execution process of the computer program in the intelligent scheduling terminal device of the nursing robot.
The intelligent dispatching terminal equipment of the nursing robot can be computing equipment such as a desktop computer, a notebook computer, a palm computer and a cloud server. The intelligent scheduling terminal device of the nursing robot can comprise, but is not limited to, a processor and a memory.
The processor may be a Central Processing Unit (CPU), other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general processor may be a microprocessor or the processor may be any conventional processor, etc., and the processor is a control center of the intelligent scheduling terminal device of the nursing robot, and various interfaces and lines are used to connect various parts of the intelligent scheduling terminal device of the whole nursing robot.
The memory may be used to store the computer programs and/or modules, and the processor may implement various functions of the intelligent scheduling terminal device of the nursing robot by executing or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the mobile phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
On the basis of the above embodiment of the intelligent scheduling method for the nursing robot, another embodiment of the present invention provides a storage medium, where the storage medium includes a stored computer program, and when the computer program runs, the apparatus on which the storage medium is located is controlled to execute the intelligent scheduling method for the nursing robot according to any one of the embodiments of the present invention.
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, and so on. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
In summary, the present invention discloses an intelligent scheduling method, apparatus, device and storage medium for nursing robots, which obtains current status information of each nursing robot when receiving a scheduling task of the nursing robot; inputting the scheduling task and the current state information into a care robot selection mechanism such that a first care robot is selected based on the care robot selection mechanism; and obtaining a first scheduling motion track of the first nursing robot when the first nursing robot executes the scheduling task based on a first preset path planning algorithm, so that the first nursing robot moves according to the first scheduling motion track until the scheduling task is completed. Compared with the prior art, the technical scheme of the invention can realize reasonable and efficient distribution and use of nursing machine groups, reduce unnecessary energy consumption and achieve the effect of environmental protection.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and substitutions can be made without departing from the technical principle of the present invention, and these modifications and substitutions should also be regarded as the protection scope of the present invention.
Claims (10)
1. An intelligent scheduling method of a nursing robot is characterized by comprising the following steps:
when a scheduling task of the nursing robot is received, current state information of each nursing robot is obtained;
inputting the scheduling task and the current state information into a care robot selection mechanism such that a first care robot is selected based on the care robot selection mechanism;
and obtaining a first scheduling motion track of the first nursing robot when the first nursing robot executes the scheduling task based on a first preset path planning algorithm, so that the first nursing robot moves according to the first scheduling motion track until the scheduling task is completed.
2. The intelligent scheduling method of a care robot according to claim 1, wherein the scheduling task and the current state information are input into a care robot selection mechanism, such that based on the care robot selection mechanism, selecting a first care robot specifically comprises:
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 comprises use time, a task starting position and a task target position, and the current state information comprises a first current position, a first residual electric quantity and idle time;
calling the first current position, the task starting position and the task target position based on a second preset path planning algorithm, generating a scheduling motion track of each first idle nursing robot, and predicting first scheduling power consumption of each first idle nursing robot based on the scheduling motion track;
comparing the first residual electric quantity corresponding to each first idle nursing robot with the first scheduling consumed electric quantity so as to screen a plurality of second idle nursing robots meeting the condition that the first residual electric quantity is larger than the first scheduling consumed electric quantity from the plurality of first idle nursing robots;
and acquiring a scheduling motion track corresponding to each second idle nursing robot, generating a scheduling motion track data set, selecting the shortest scheduling motion track from the scheduling motion track data set, and taking the second idle nursing robot corresponding to the shortest scheduling motion track as the selected first nursing robot.
3. The intelligent scheduling method of a care robot of claim 1, further comprising, after completing the scheduling task:
when the first nursing robot is detected to finish the scheduling task, detecting a second residual electric quantity of the first nursing robot in real time, and comparing the second residual electric quantity with a preset electric quantity threshold value;
if the second remaining electric quantity is smaller than a preset electric quantity threshold value, sending a charging prompt to a manager, and dispatching the first nursing robot to a charging position;
if the second remaining electric quantity is not smaller than a preset electric quantity threshold value, whether the first nursing robot is located at a robot parking position or not is judged, and if not, the first nursing robot is dispatched to the parking position.
4. The intelligent scheduling method of a nursing robot according to claim 2, wherein obtaining a first scheduling motion trajectory of the first nursing robot when executing the scheduling task based on a first preset path planning algorithm specifically comprises:
acquiring a scheduling motion track corresponding to the first nursing robot, controlling the first nursing robot to move along the scheduling motion track, and detecting whether an obstacle exists in real time in the moving process;
when the obstacle is detected to exist, acquiring a first distance between the first nursing robot and the obstacle at the current moment, and calling the first distance based on the first preset path planning algorithm to obtain a local obstacle avoidance motion track at the next moment;
and correcting the scheduling motion track according to the local obstacle avoidance motion track to obtain a first scheduling motion track.
5. The intelligent scheduling method of nursing robots according to claim 2, wherein predicting the first scheduled power consumption of each first idle nursing robot based on the scheduled motion trajectory specifically comprises:
acquiring the power consumption of each kilometer of simulation scheduling, and acquiring the track length of the scheduling motion track based on the scheduling motion track;
and predicting the first scheduling consumed electric quantity of each first idle nursing robot according to the track length and the simulation scheduling consumed electric quantity.
6. The intelligent scheduling method of a nursing robot according to claim 3, wherein the scheduling the first nursing robot to the charging position specifically includes:
acquiring current working states of all current charging positions, wherein the current working states comprise charging and non-charging;
screening a plurality of idle charging positions with uncharged current working states from all the charging positions;
acquiring 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 taking the idle charging position corresponding to the shortest second distance as a target charging position;
and generating a charging scheduling motion track according to the second current position and the target charging position, and controlling the first nursing robot to move to the target charging position according to the charging scheduling motion track.
7. The intelligent scheduling method of a care robot according to claim 3, wherein scheduling the first care robot to a parking position specifically comprises:
acquiring current working states of all current parking positions, wherein the current working states comprise parked positions and non-parked positions;
screening a plurality of idle parking positions which are not parked in the current working state from all the parking positions;
acquiring a third current position of the first nursing robot, respectively calculating third distances between the third current position and the plurality of idle parking positions, and generating 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 a target parking position;
and generating a parking scheduling motion trail according to the third current position and the target parking position, and controlling the first nursing robot to move to the target parking position according to the parking scheduling motion trail.
8. An intelligent scheduling device of a nursing robot, comprising: the system comprises a nursing robot information acquisition module, a first nursing robot selection module and a first nursing robot scheduling module;
the nursing robot information acquisition module is used for acquiring current state information of each nursing robot when a scheduling task of the nursing robot is received;
the first nursing robot selection module is used for 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;
the first nursing robot scheduling module is configured to obtain a first scheduling motion trajectory of the first nursing robot when the first nursing robot executes 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.
9. A terminal device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the intelligent scheduling method of a nursing robot according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, comprising a stored computer program, wherein the computer program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the intelligent scheduling method of the nursing robot according to any one of claims 1 to 7.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107291078A (en) * | 2017-06-06 | 2017-10-24 | 歌尔股份有限公司 | The dispatching method and device of a kind of service robot |
| US20180333847A1 (en) * | 2016-01-04 | 2018-11-22 | Hangzhou Yameilijia Technology Co., Ltd. | Method and apparatus for working-place backflow of robots |
| CN110977979A (en) * | 2019-12-13 | 2020-04-10 | 华南智能机器人创新研究院 | Idle robot scheduling method and system |
| CN111224443A (en) * | 2020-02-05 | 2020-06-02 | 广州赛特智能科技有限公司 | Big data based distribution robot charging method and system and processing terminal |
| CN111596658A (en) * | 2020-05-11 | 2020-08-28 | 东莞理工学院 | Multi-AGV collision-free operation path planning method and scheduling system |
| CN112184098A (en) * | 2019-07-05 | 2021-01-05 | 坎德拉（深圳）科技创新有限公司 | Scheduling method of intelligent distribution system, server and storage medium |
| CN113592173A (en) * | 2021-07-28 | 2021-11-02 | 安徽大学 | System and method for scheduling multiple disinfection robots applied to large public places |
| CN114256940A (en) * | 2022-02-28 | 2022-03-29 | 北京蒙帕信创科技有限公司 | Multi-robot charging scheduling method, device and system |
-
2022
- 2022-08-01 CN CN202210920387.7A patent/CN115157263A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180333847A1 (en) * | 2016-01-04 | 2018-11-22 | Hangzhou Yameilijia Technology Co., Ltd. | Method and apparatus for working-place backflow of robots |
| CN107291078A (en) * | 2017-06-06 | 2017-10-24 | 歌尔股份有限公司 | The dispatching method and device of a kind of service robot |
| CN112184098A (en) * | 2019-07-05 | 2021-01-05 | 坎德拉（深圳）科技创新有限公司 | Scheduling method of intelligent distribution system, server and storage medium |
| CN110977979A (en) * | 2019-12-13 | 2020-04-10 | 华南智能机器人创新研究院 | Idle robot scheduling method and system |
| CN111224443A (en) * | 2020-02-05 | 2020-06-02 | 广州赛特智能科技有限公司 | Big data based distribution robot charging method and system and processing terminal |
| CN111596658A (en) * | 2020-05-11 | 2020-08-28 | 东莞理工学院 | Multi-AGV collision-free operation path planning method and scheduling system |
| CN113592173A (en) * | 2021-07-28 | 2021-11-02 | 安徽大学 | System and method for scheduling multiple disinfection robots applied to large public places |
| CN114256940A (en) * | 2022-02-28 | 2022-03-29 | 北京蒙帕信创科技有限公司 | Multi-robot charging scheduling method, device and system |
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