CN110125931B - Method and device for scheduling tasks of navigation robot, robot and storage medium - Google Patents
Method and device for scheduling tasks of navigation robot, robot and storage medium Download PDFInfo
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- CN110125931B CN110125931B CN201910358196.4A CN201910358196A CN110125931B CN 110125931 B CN110125931 B CN 110125931B CN 201910358196 A CN201910358196 A CN 201910358196A CN 110125931 B CN110125931 B CN 110125931B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J13/00—Controls for manipulators
- B25J13/003—Controls for manipulators by means of an audio-responsive input
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1628—Programme controls characterised by the control loop
- B25J9/1653—Programme controls characterised by the control loop parameters identification, estimation, stiffness, accuracy, error analysis
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
- B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
- B25J9/1666—Avoiding collision or forbidden zones
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
- B25J9/1671—Programme controls characterised by programming, planning systems for manipulators characterised by simulation, either to verify existing program or to create and verify new program, CAD/CAM oriented, graphic oriented programming systems
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Abstract
A method and a device for scheduling tasks of a navigation robot, the robot and a storage medium are provided. The method comprises the following steps: acquiring task data, wherein the task data comprises data obtained by voice recognition, barrier data obtained by image recognition, path planning and dynamic obstacle avoidance algorithm data; analyzing the collected task data, matching the tasks with a priority configuration table according to the real-time acquired environment data and user request data, giving a dynamic priority value to the tasks, and putting the tasks to be executed into the task table; step three, sending instructions to each functional module of the robot according to the dynamic priority value of each task, so that each functional module dynamically executes each task according to the sequence; and step four, with the execution of the tasks and the updating of the environmental data, dynamically adjusting the priority values of all the tasks, and rescheduling the execution of all the tasks. The invention can reduce the system operation time and save the system resources.
Description
Technical Field
The present disclosure relates generally to the field of robots, and more particularly, to a method and an apparatus for scheduling tasks of a robot, a robot and a storage medium.
Background
The application of the robot gradually penetrates into all social industries, such as navigation, nursing, smart home and the like. The application requirements of each field for the robot are different, and the robot in each field needs to be independently developed. The current robot can be troubled by a plurality of complicated different tasks, the use of the tasks is not acceptable or prioritized, and in some cases, the running time of the system can be greatly increased by only using a single task execution method, and resources are seriously wasted. Task planning also marks the intelligence level of the robot to a certain extent, and is the key to planning and decision making. Most of environments where robots are located have strong complexity and dynamics, and how to enable the robots to perform autonomous task planning and execution in dynamic environments is a hotspot and difficulty of research in the field of robot task planning.
Disclosure of Invention
In view of the above-mentioned drawbacks and deficiencies of the prior art, a method for scheduling tasks of a navigation robot is provided. By using the task scheduling method, different functions or modules are scheduled under different conditions, the utilization efficiency of the system is improved, the motion state of the robot is corrected, the robot is informed of the selection or the rejection of different important tasks, and the robot is assisted to complete the tasks.
In a first aspect, an embodiment of the present application provides a method for scheduling a task of a navigation robot, which is applied to a navigation robot, and the method includes:
acquiring task data, wherein the task data comprises data obtained by voice recognition, barrier data obtained by image recognition, path planning and dynamic obstacle avoidance algorithm data;
analyzing the collected task data, matching the tasks with a priority configuration table according to the real-time acquired environment data and user request data, giving a dynamic priority value to the tasks, and putting the tasks to be executed into the task table;
step three, sending instructions to each functional module of the robot according to the dynamic priority value of each task, so that each functional module dynamically executes each task according to the sequence;
step four, with the execution of the task and the updating of the environmental data, dynamically adjusting the priority value of each task, and rescheduling the execution of each task;
and step five, repeatedly executing the step two to the step four until all tasks are scheduled and executed.
In a second aspect, an embodiment of the present application provides a task scheduling apparatus for a navigation robot, which is applied to a navigation robot, and the apparatus includes:
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring task data, and the task data comprises data obtained by voice recognition, barrier data obtained by image recognition, path planning and dynamic obstacle avoidance algorithm data;
the analysis unit is used for analyzing the collected task data, matching the tasks with the priority configuration table according to the real-time acquired environment data and the user request data, endowing a dynamic priority value for the tasks, and putting the tasks to be executed into the task table;
the execution unit is used for sending instructions to each functional module of the robot according to the dynamic priority value of each task, so that each functional module of the robot dynamically executes each task according to the sequence, dynamically adjusts the priority value of each task along with the execution of the tasks and the update of environmental data, and reschedules the execution of each task;
and the control unit is used for repeatedly executing the analysis unit and the execution unit until all tasks are scheduled and executed.
In a third aspect, an embodiment of the present application provides a robot, which is characterized by comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the method described in the embodiment of the present application is implemented.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, the computer program being configured to:
which when executed by a processor implements a method as described in embodiments of the present application.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
fig. 1 is a flowchart illustrating a task scheduling method for a navigation robot according to an embodiment of the present disclosure;
fig. 2 is a block diagram illustrating a task scheduling apparatus of a navigation robot according to another embodiment of the present disclosure.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Referring to fig. 1, fig. 1 is a schematic flowchart illustrating a task scheduling method of a navigation robot according to an embodiment of the present disclosure. The method is used for scheduling at least one functional module in a robot.
As shown in fig. 1, the method includes:
acquiring task data, wherein the task data comprises data obtained by voice recognition, barrier data obtained by image recognition, path planning and dynamic obstacle avoidance algorithm data;
analyzing the collected task data, matching the tasks with a priority configuration table according to the real-time acquired environment data and user request data, giving a dynamic priority value to the tasks, and putting the tasks to be executed into the task table;
step three, sending instructions to each functional module of the robot according to the dynamic priority value of each task, so that each functional module dynamically executes each task according to the sequence;
step four, with the execution of the task and the updating of the environmental data, dynamically adjusting the priority value of each task, and rescheduling the execution of each task;
and step five, repeatedly executing the step two to the step four until all tasks are scheduled and executed.
Further, assigning a dynamic priority value to each task specifically includes: distributing priorities to the tasks according to the task importance degree i, the resources j occupied by the tasks and the execution time k of the tasks; each task is assigned a priority level P, where the higher the value of P, the higher the priority of the task, where α, β, γ represent weight coefficients.
The task scheduling according to the priority comprises the following steps: adding the task to a task table according to the priority value of each task calculated by a formula P ═ α × i + β/j + γ/k;
executing each task in descending order according to the priority level; the priority is generally divided into three levels of emergency, real-time and common;
for each task with the priority level P larger than or equal to a first threshold value, executing the tasks with the same priority level P in a descending order according to the importance degree i of the tasks;
for each task with the priority level P smaller than a first threshold value, executing the tasks with the same priority level P in an ascending order according to the length of the task execution time k;
and deleting the tasks which are already executed in the task table.
The priority configuration table comprises task id, task type, task importance degree, resources occupied by the task and task execution time; the updating way of the priority configuration table comprises user input, system estimation and machine training; the machine training mode is as follows: the robot can judge the most suitable main line task under any condition after a large amount of machine training, and update the priority configuration table corresponding to the task by using the training result.
The robot function module includes at least one of: the intelligent anti-falling device comprises a charging task module, an anti-falling module, a laser module, a navigation module, a map building module, a motion control module, a storage module and a calculation module.
Referring to fig. 2, fig. 2 is a block diagram illustrating a task scheduling apparatus of a navigation robot according to another embodiment of the present application.
As shown in fig. 2, the apparatus includes:
the system comprises an acquisition unit 10, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring task data, and the task data comprises data obtained by voice recognition, barrier data obtained by image recognition, path planning and dynamic obstacle avoidance algorithm data;
the analysis unit 20 is configured to analyze the collected task data, match each task with the priority configuration table according to the environment data and the user request data obtained in real time, assign a dynamic priority value to each task, and place the task to be executed in the task table;
the execution unit 30 is configured to send an instruction to each function module of the robot according to the dynamic priority of each task, so that each function module of the robot dynamically executes each task in sequence, dynamically adjusts the priority of each task along with the execution of the tasks and the update of the environmental data, and reschedules the execution of each task;
and the control unit 40 is used for repeatedly executing the analysis unit and the execution unit until all tasks are scheduled and executed. The step of assigning a dynamic priority value to each task in the analysis unit specifically includes:
firstly, distributing priorities to tasks according to the importance degree i of the tasks, the resources j occupied by the tasks and the execution time k of the tasks; each task is assigned a priority level P, where the higher the value of P, the higher the priority of the task, where α, β, γ represent weight coefficients.
The task scheduling according to the priority in the execution unit comprises the following steps:
the calculation subunit is used for calculating the priority value of each task according to the formula P ═ α × i + β/j + γ/k and adding the task to the task table;
the first execution subunit is used for executing various tasks in a descending order according to the priority level;
the second execution subunit is used for executing the tasks with the same priority level P in a descending order according to the importance degree i of the tasks for which the priority level P is greater than or equal to the first threshold value, and executing the tasks with the same priority level P in an ascending order according to the length of the task execution time k for the tasks with the priority level P less than the first threshold value;
and the deleting subunit is used for deleting the tasks which have been executed in the task table.
The priority configuration table comprises task id, task type, task importance degree, resources occupied by the task and task execution time; the updating way of the priority configuration table comprises user input, system estimation and machine training; the machine training mode is as follows: the robot can judge the most suitable main line task under any condition after a large amount of machine training, and update the priority configuration table corresponding to the task by using the training result.
The robot function module includes at least one of: the intelligent anti-falling device comprises a charging task module, an anti-falling module, a laser module, a navigation module, a map building module, a motion control module, a storage module and a calculation module.
As another aspect, the present application further provides a robot, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the robot performs the navigation robot task scheduling method described in the present application.
As another aspect, the present application also provides a computer-readable storage medium, which may be the computer-readable storage medium included in the foregoing device in the foregoing embodiment; or it may be a separate computer readable storage medium not incorporated into the device. The computer readable storage medium stores one or more programs for one or more processors to perform the navigation robot task scheduling method described in the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic Gate circuit for realizing a logic function for a data signal, an asic having an appropriate combinational logic Gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), and the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments. In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (6)
1. A task scheduling method of a navigation robot is applied to the navigation robot and is characterized by comprising the following steps:
acquiring task data, wherein the task data comprises data obtained by voice recognition, barrier data obtained by image recognition, path planning and dynamic obstacle avoidance algorithm data;
analyzing the collected task data, matching the tasks with a priority configuration table according to the real-time acquired environment data and user request data, giving a dynamic priority value to the tasks, and putting the tasks to be executed into the task table;
step three, sending instructions to each functional module of the robot according to the dynamic priority value of each task, so that each functional module dynamically executes each task according to the sequence;
step four, with the execution of the task and the updating of the environmental data, dynamically adjusting the priority value of each task, and rescheduling the execution of each task;
step five, repeatedly executing the step two to the step four until all tasks are scheduled and executed;
wherein, assigning a dynamic priority value to each task specifically comprises:
firstly, distributing priorities to tasks according to the importance degree i of the tasks, the resources j occupied by the tasks and the execution time k of the tasks;
each task is assigned with a priority level P, the P is alpha, i, beta, j and gamma/k, the higher the P value is, the higher the priority of the task is, wherein alpha, beta and gamma represent weight coefficients;
the task scheduling according to the priority comprises the following steps:
adding the task to a task table according to the priority value of each task calculated by a formula P ═ α × i + β/j + γ/k;
executing each task in descending order according to the priority level;
for each task with the priority level P larger than or equal to a first threshold value, executing the tasks with the same priority level P in a descending order according to the importance degree i of the tasks;
for each task with the priority level P smaller than a first threshold value, executing the tasks with the same priority level P in an ascending order according to the length of the task execution time k;
deleting the tasks which have been executed in the task table;
the priority configuration table comprises a task id, a task type, a task importance degree, resources occupied by the task and execution time of the task; the updating way of the priority configuration table comprises user input, system estimation and machine training; wherein, the machine training mode is as follows: the robot can judge the most suitable main line task under any condition after a large amount of machine training, and update the priority configuration table corresponding to the task by using the training result.
2. The method for scheduling task of navigation robot as claimed in claim 1, wherein the functional module comprises at least one of: the intelligent anti-falling device comprises a charging task module, an anti-falling module, a laser module, a navigation module, a map building module, a motion control module, a storage module and a calculation module.
3. A task scheduling device of a navigation robot is applied to the navigation robot, and is characterized by comprising: the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring task data, and the task data comprises data obtained by voice recognition, barrier data obtained by image recognition, path planning and dynamic obstacle avoidance algorithm data;
the analysis unit is used for analyzing the collected task data, matching the tasks with the priority configuration table according to the real-time acquired environment data and the user request data, endowing a dynamic priority value for the tasks, and putting the tasks to be executed into the task table;
the execution unit is used for sending instructions to each functional module of the robot according to the dynamic priority value of each task, so that each functional module of the robot dynamically executes each task according to the sequence, dynamically adjusts the priority value of each task along with the execution of the tasks and the update of environmental data, and reschedules the execution of each task;
the control unit is used for repeatedly executing the analysis unit and the execution unit until all tasks are scheduled and executed;
wherein, the step of giving a dynamic priority value to each task in the analysis unit specifically comprises the following steps:
firstly, distributing priorities to tasks according to the importance degree i of the tasks, the resources j occupied by the tasks and the execution time k of the tasks; each task is assigned with a priority level P, the P is alpha, i, beta, j and gamma/k, the higher the P value is, the higher the priority of the task is, wherein alpha, beta and gamma represent weight coefficients;
the task scheduling according to the priority in the execution unit comprises the following steps:
the calculation subunit is used for calculating the priority value of each task according to the formula P ═ α × i + β/j + γ/k and adding the task to the task table;
the first execution subunit is used for executing various tasks in a descending order according to the priority level;
the second execution subunit is used for executing the tasks with the same priority level P in a descending order according to the importance degree i of the tasks for which the priority level P is greater than or equal to the first threshold value, and executing the tasks with the same priority level P in an ascending order according to the length of the task execution time k for the tasks with the priority level P less than the first threshold value; a deleting subunit, configured to delete a task that has been executed in the task table;
the priority configuration table comprises a task id, a task type, a task importance degree, resources occupied by the task and execution time of the task; the updating way of the priority configuration table comprises user input, system estimation and machine training; wherein, the machine training mode is as follows: the robot can judge the most suitable main line task under any condition after a large amount of machine training, and update the priority configuration table corresponding to the task by using the training result.
4. A navigation robot task scheduler according to claim 3, wherein said robot function module comprises at least one of: the intelligent anti-falling device comprises a charging task module, an anti-falling module, a laser module, a navigation module, a map building module, a motion control module, a storage module and a calculation module.
5. A robot comprising a memory, a processor and a computer program stored on the memory and executable on the processor, when executing the computer program, implementing the method according to any of claims 1-2.
6. A computer-readable storage medium having stored thereon a computer program for: the computer program, when executed by a processor, implementing the method as claimed in any one of claims 1-2.
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| CN113536878B (en) * | 2020-04-14 | 2024-03-08 | 杭州海康威视数字技术股份有限公司 | Intelligent analysis system and method |
| CN115674170B (en) * | 2021-07-30 | 2023-12-19 | 北京小米移动软件有限公司 | Robot control method, device, robot and storage medium |
| CN116991246A (en) * | 2023-09-27 | 2023-11-03 | 之江实验室 | Algorithm scheduling method and device for navigation robot and navigation robot system |
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