CN-121981477-A - Robot cluster cooperative scheduling and navigation method and system

CN121981477ACN 121981477 ACN121981477 ACN 121981477ACN-121981477-A

Abstract

The invention provides a method and a system for collaborative scheduling and navigation of a robot cluster, which are characterized in that a task queue to be allocated is periodically updated, whether the number of tasks to be allocated in the task queue to be allocated meets a preset condition is judged, when the number of the tasks to be allocated in the task queue to be allocated meets the preset condition, at least one temporary waiting area is determined in a task scheduling area, wherein the temporary waiting area is in the task scheduling area, and when the number of parked robots is greater than or equal to 1, the interference coefficient on the navigation path of the robot cluster is smaller than a preset first threshold value And the attribute matching degree with the task to be distributed in the task queue to be distributed is larger than a preset second threshold value When any robot enters an idle state from a working state, the robot is navigated to the temporary waiting area, so that the dispatching efficiency of the robot can be further improved.

Inventors

LI XIAOJUN
LI KEWEI
ZHAO MING
LIANG JINJI

Assignees

深圳易普森科技股份有限公司
易普森智慧健康科技（深圳）有限公司
易普森生物科技(深圳)有限公司

Dates

Publication Date: 20260505
Application Date: 20260126

Claims (10)

1. The robot cluster collaborative scheduling and navigation method is characterized by comprising the following steps of: periodically updating a task queue to be allocated, wherein the task queue to be allocated is composed of tasks to be allocated to the robots in the robot cluster for execution; judging whether the number of tasks to be distributed in the task queue to be distributed accords with a preset condition or not; When the number of tasks to be distributed in the task queue to be distributed accords with a preset condition, determining at least one temporary waiting area in a task scheduling area range, wherein the temporary waiting area is in the task scheduling area range, and when the number of robots parked is greater than or equal to 1, the interference coefficient on the navigation path of the robot cluster is smaller than a preset first threshold value And the attribute matching degree with the task to be distributed in the task queue to be distributed is larger than a preset second threshold value Is a region of (2); and when any robot enters an idle state from a working state, navigating the robot to the temporary waiting area.
2. The method of collaborative scheduling and navigation of a robot cluster according to claim 1, further comprising, prior to the step of determining at least one temporary waiting area within a task scheduling area: Acquiring an environment image of the task scheduling area in a period of time by one or more robots in a robot cluster; performing an integration analysis on the environmental image to determine a dockable region within the task scheduling region; and selecting points in the parkable area to obtain a plurality of candidate parking points.
3. The method for collaborative scheduling and navigation of a robot cluster according to claim 2, wherein the step of determining at least one temporary waiting area within the task scheduling area comprises: build with robot park number Calculating a function for interference coefficients of a variable ; Configuring minimum number of parks ; Calculating a function using the interference coefficient Calculating the number of the parking points at each candidate parking point to be equal to the minimum number of the parking points In this case, the interference coefficient to the navigation path of the robot cluster , wherein, Is 1 to 1 A positive integer between the two, Number of candidate parking points; calculating attribute matching degree of each candidate parking point and tasks to be distributed in the task queue to be distributed ; To park the number of Interference coefficient at the time Less than the first threshold And matching degree with the attribute of the task to be distributed in the task queue to be distributed Greater than the second threshold Is determined as the temporary waiting area.
4.A method for collaborative scheduling and navigation of a robot cluster according to claim 3 wherein the number of parks is Interference coefficient at the time Less than the first threshold And matching degree with the attribute of the task to be distributed in the task queue to be distributed Greater than the second threshold After the step of determining the candidate parking point of the temporary waiting area, further comprising: determining a maximum number of stops for each temporary waiting area So that the first Interference factor of temporary waiting area Wherein Is 1 to 1 A positive integer between the two, Scheduling the number of temporary waiting areas in the area for the task in the current period; Setting the maximum number of parks Is determined as the first Number of parkable temporary waiting areas.
5. The method of collaborative scheduling and navigation of a robot cluster according to claim 4, wherein a maximum number of parks is determined for each temporary waiting area The method specifically comprises the following steps: For the first A temporary waiting area for determining a first number of parks And a second parking number So that the first parking number And the second parking number Simultaneously satisfies: ; Setting the maximum number of parks Is configured to: 。
6. A method of collaborative scheduling and navigation of a robot cluster according to claim 3, wherein the number of robot parks is configured to Calculating a function for interference coefficients of a variable The method specifically comprises the following steps: acquiring a task start-stop position coordinate of each task to be distributed in the task queue to be distributed; pre-planning a navigation path corresponding to each task to be distributed according to the start-stop position coordinates of the task; Determine the pass through the first Number of pre-planned navigation paths for each candidate parking spot ; Calculate the first Multiple candidate parking spots parking Radius of occupied area when robot is used ; Determination of the first Minimum pass width at each candidate parking spot ; Calculating the interference coefficient function The construction is as follows: 。
7. a method of collaborative scheduling and navigation of a robot cluster according to claim 3, wherein a degree of attribute matching of each candidate parking point to tasks to be allocated in the task to be allocated queue is calculated The method specifically comprises the following steps: Acquisition of the first Position coordinates of the candidate parking points; acquiring the position coordinates of the task starting points of each task to be distributed in the task queue to be distributed; Calculate the first Average distance between position coordinates of each candidate parking point and task starting point position coordinates of each task to be distributed in the task queue to be distributed ; Acquiring the scale parameters of the task scheduling area ; Calculate the first Attribute matching degree of the candidate parking points and the tasks to be distributed in the task queue to be distributed: 。
8. the method of collaborative scheduling and navigation of a robot cluster according to claim 4, further comprising, after the step of determining at least one temporary waiting area within the task scheduling area: Acquisition of the first Current robot parking number for temporary waiting areas ; Calculate the first Interference factor of temporary waiting area And matching degree of attribute of the task to be distributed in the task queue to be distributed ; Calculate the first Priority scoring of the temporary waiting areas: , Wherein the method comprises the steps of And Respectively a pre-configured attribute matching degree weight coefficient and an interference degree scoring coefficient.
9. The method for collaborative scheduling and navigation of a robot cluster according to claim 8, wherein navigating the robot to the temporary waiting area comprises: When any robot enters an idle state from a working state, determining the robot as an idle robot; Acquiring real-time position coordinates of the idle robot; Calculating the idle robot and the first robot according to the real-time position coordinates Distance of temporary waiting area ; Calculate the first Relative priority scores of the temporary waiting areas with respect to the idle robot: ; Scoring the relative priority Is determined as a target waiting area, the available temporary waiting area being the current number of robot parks Less than the maximum number of parks Is a temporary waiting area of (a); and navigating the idle robot to the target waiting area.
10. The robot cluster collaborative scheduling and navigation system is characterized by comprising a robot cluster formed by a plurality of robots and a scheduling navigation server, wherein the robot comprises an image sensing module, a communication module and a positioning navigation module, the image sensing module is used for acquiring an environment image around the position of the robot, the communication module is used for being in communication connection with the scheduling navigation server, the positioning navigation module is used for positioning and navigating the robot according to positioning or navigation instructions issued by the scheduling navigation server, and the scheduling navigation server is configured to realize the robot cluster collaborative scheduling and navigation method according to any one of claims 1-10 after the communication connection is established between the scheduling navigation server and the robots of the robot cluster.

Description

Robot cluster cooperative scheduling and navigation method and system Technical Field The invention relates to the technical field of autonomous mobile robots, in particular to a robot cluster collaborative scheduling and navigation method and system. Background An autonomous mobile robot (Autonomous Mobile Robot, AMR) is an intelligent robot capable of autonomous navigation, performing tasks in complex environments. The environment sensing and positioning are realized through multi-sensor fusion technologies such as laser radar, visual sensors, inertial Measurement Units (IMU) and the like, a fixed track or a magnetic stripe is not needed, and the environment sensing and positioning device has high flexibility and adaptability. The core technology comprises SLAM (Simultaneous Localization AND MAPPING, synchronous positioning and map construction), path planning, dynamic obstacle avoidance and other algorithms, and supports real-time adjustment of task routes. Since the advent of autonomous mobile robots, the autonomous mobile robots have been widely used in the field of logistics transportation, and in particular, their clustered cooperative transportation capability, so that the transportation efficiency in intensive transportation scenarios such as factories and logistics warehouses is greatly improved. With the rising and development of various novel business modes, the transportation task of the autonomous mobile robot gradually evolves from the traditional fixed-point transportation task to the non-fixed-point transportation task with the unfixed starting point, and as the scheduling modes of the existing robot clusters are designed for the fixed-point transportation task, when facing the non-fixed-point transportation task, the autonomous mobile robot clusters are difficult to exert the advantages of clustered transportation, and compared with the traditional independent control mode, the transportation efficiency is not obviously improved. Disclosure of Invention Based on the problems, the invention provides a robot cluster collaborative scheduling and navigation method and system, which can further improve the scheduling efficiency of robots. In view of the above, a first aspect of the present invention provides a method for collaborative scheduling and navigation of a robot cluster, including: periodically updating a task queue to be allocated, wherein the task queue to be allocated is composed of tasks to be allocated to the robots in the robot cluster for execution; judging whether the number of tasks to be distributed in the task queue to be distributed accords with a preset condition or not; When the number of tasks to be distributed in the task queue to be distributed accords with a preset condition, determining at least one temporary waiting area in a task scheduling area range, wherein the temporary waiting area is in the task scheduling area range, and when the number of robots parked is greater than or equal to 1, the interference coefficient on the navigation path of the robot cluster is smaller than a preset first threshold value And the attribute matching degree with the task to be distributed in the task queue to be distributed is larger than a preset second threshold valueIs a region of (2); and when any robot enters an idle state from a working state, navigating the robot to the temporary waiting area. Optionally, before the step of determining at least one temporary waiting area within the task scheduling area, the method further includes: Acquiring an environment image of the task scheduling area in a period of time by one or more robots in a robot cluster; performing an integration analysis on the environmental image to determine a dockable region within the task scheduling region; and selecting points in the parkable area to obtain a plurality of candidate parking points. Optionally, the step of determining at least one temporary waiting area within the task scheduling area specifically includes: build with robot park number Calculating a function for interference coefficients of a variable; Configuring minimum number of parks; Calculating a function using the interference coefficientCalculating the number of the parking points at each candidate parking point to be equal to the minimum number of the parking pointsIn this case, the interference coefficient to the navigation path of the robot cluster, wherein,Is 1 to 1A positive integer between the two,Number of candidate parking points; calculating attribute matching degree of each candidate parking point and tasks to be distributed in the task queue to be distributed ; To park the number ofInterference coefficient at the timeLess than the first thresholdAnd matching degree with the attribute of the task to be distributed in the task queue to be distributedGreater than the second thresholdIs determined as the temporary waiting area. Optionally, in the case of the number of parks beingInterference coefficient at the timeLess than