Cleaning robot work task scheduling method and system

Abstract

The invention belongs to the technical field of robot scheduling, and particularly relates to a cleaning robot work task scheduling method and system, and the method comprises the steps: determining the global competitiveness of a cleaning task according to the area of the cleaning task and the distance between the cleaning robot and the cleaning task, determining an exclusive tendency degree according to the global competitiveness and the distance from the cleaning robot to the cleaning task, and determining a task allocation suppression factor of the cleaning robot to the cleaning task according to the exclusive tendency degrees of other cleaning robots to the cleaning task; and according to the task allocation suppression factor and the distance from the cleaning robot to the cleaning task, determining a total social gravitation vector suffered by the cleaning robot, performing vector superposition on the total social gravitation vector and a repulsive force vector to obtain a total driving force of the cleaning robot, generating a motion instruction, and scheduling the cleaning robot to perform cleaning. According to the invention, the intelligent balanced distribution of the cleaning tasks among the plurality of cleaning robots is realized, and the operation efficiency of the tippler system is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of robot scheduling. More particularly, the present application relates to a cleaning robot work task scheduling method and system. BACKGROUND

[0002] In the bulk material transfer scene of mines, ports and the like, the dumper system as the key equipment to realize efficient unloading of the train compartment undertakes an important material transfer task. After the compartment passes through the turnover unloading, a part of the residual coal blocks will often adhere or freeze in the interior, especially in the corners and wallboards. These residual materials not only affect the subsequent loading efficiency, but also can cause the compartment to be overloaded or affect the safety of train operation. In order to ensure the complete emptying of the compartment and improve the operation efficiency of the dumper system, multiple cleaning robots are usually used to cooperatively enter the interior of the compartment for automatic cleaning operation.

[0003] In the traditional multi-robot cooperative path planning method, the artificial potential field method is widely used due to its simple calculation and good real-time performance. In the artificial potential field method, the movement of the robot in the workspace is virtually a movement in an abstract force field. The target point generates an attractive force on the robot, while the obstacle generates a repulsive force. The robot finally moves along the direction of the combined force of the attractive force and the repulsive force. In practical applications, the artificial potential field method is used to guide the movement of the cleaning robot in the interior of the compartment, so that it can avoid obstacles and move towards the area that needs to be cleaned.

[0004] However, the artificial potential field method mainly focuses on the physical distance relationship between the robot and the target point, and lacks consideration of the task competition characteristics in the multi-robot system. In the artificial potential field method, the attractive force is only related to the target attribute and the distance, which causes all cleaning robots to be attracted by the residual coal block area with the closest distance or the largest area, thereby causing the cleaning robots to rush to the same task area, resulting in local congestion. This congestion not only causes the cleaning robots to generate a strong repulsive force due to the close distance between them, which interferes with each other's cleaning path and even causes the movement to stop, but also causes the cleaning tasks in other areas of the compartment to be unattended, which affects the cleaning efficiency. SUMMARY

[0005] To solve the technical problems of uneven distribution of cleaning tasks and low efficiency caused by the clustering of multiple cleaning robots in the traditional artificial potential field method, the present application provides solutions in the following aspects.

[0006] In a first aspect, the present application provides a cleaning robot work task scheduling method, comprising: Obtain the cleaning task information of the car interior; determine the global competitiveness of the cleaning task according to the area size of the cleaning task and the distance of each cleaning robot to the cleaning task; determine the exclusive tendency of each cleaning robot to the cleaning task according to the global competitiveness of the cleaning task and the distance of each cleaning robot to the cleaning task; determine the task allocation inhibition factor of the cleaning robot to the cleaning task according to the exclusive tendency of other cleaning robots to the cleaning task; determine the total social attractive force vector received by the cleaning robot according to the task allocation inhibition factor and the distance of the cleaning robot to the cleaning task; determine the repulsive force vector received by the cleaning robot from the obstacle, superimpose the total social attractive force vector and the repulsive force vector to obtain the total driving force of the cleaning robot, generate a motion instruction according to the total driving force, and dispatch the cleaning robot to clean.

[0007] The application identifies the position and size of each residual coal block area in the car, determines the global competitiveness of the cleaning task based on the area size of the cleaning task and the distance relationship, can identify the popular task that is easy to cause congestion, calculates the exclusive tendency of the cleaning robot to the cleaning task and the task allocation inhibition factor, integrates the social relationship between the cleaning robots into the path planning, automatically reduces the attraction of the cleaning task to the specific cleaning robot when the cleaning task is concerned by multiple cleaning robots, promotes the cleaning robot to turn to the cleaning task with less competition, and avoids the phenomenon that all cleaning robots rush to the same cleaning task in the traditional method; the application combines the social attractive force field and the repulsive force field, enables the cleaning robot to autonomously avoid collision while pursuing the cleaning task target, ensures the orderly distribution in the limited car space, enables multiple cleaning robots to efficiently cooperate in different areas of the car, improves the cleaning efficiency, and shortens the overall operation time.

[0008] Preferably, it further comprises: when the cleaning task is not selected within a continuous preset period, reducing the task allocation inhibition factor of all cleaning robots to the cleaning task to enhance the attraction of the cleaning task to the cleaning robot.

[0009] The application effectively solves the task deadlock problem that may occur in the cooperative cleaning of multiple cleaning robots by dynamically adjusting the task allocation inhibition factor, automatically reduces the task allocation inhibition factor of all cleaning robots to the task when the cleaning task is not selected by any cleaning robot within a continuous multiple period, ensures that even the most remote or smallest task can obtain sufficient attention, promotes the cleaning robot that may ignore the cleaning task to reconsider and select to execute the cleaning task, avoids the phenomenon that the popular task is excessively concerned and the unpopular task is ignored for a long time in the traditional multiple cleaning robot system, and guarantees the balanced allocation and timely completion of all cleaning tasks.

[0010] Preferably, the step of obtaining the cleaning task information inside the carriage includes: using a sensor array installed above the tipper structure to scan the inside of the carriage after it has been tipped over and returned to its original position, identifying the residual coal areas that need to be cleaned, obtaining the geometric center coordinates and area size of each residual coal area, and establishing a cleaning task for each residual coal area.

[0011] Preferably, the global competitiveness satisfies the expression: In the formula, For the first The overall competitiveness of a cleaning task; For the first The size of the area to be cleaned; The size of the largest cleaning task among all cleaning tasks; For the first The geometric center position vector of each cleaning task; For the first The real-time position vector of the cleaning robot; For vectors The modulus length; This represents the total number of cleaning robots; This is the length of the diagonal of the carriage; This indicates normalization.

[0012] This invention assesses the size of the cleaning task and the average distance from the task to all cleaning robots, giving larger cleaning tasks a higher base attractiveness, while considering cleaning tasks located closer to the center of the cleaning robot cluster as more competitive and popular tasks. This makes large-area cleaning tasks located in the center of the carriage accurately identified as highly competitive cleaning tasks.

[0013] Preferably, the exclusivity tendency satisfies the expression: In the formula, For the first Taiwan cleaning robot for the first Exclusivity of a cleaning task; For the first The geometric center position vector of each cleaning task; For the first The real-time position vector of the cleaning robot; For vectors The modulus length; This is the length of the diagonal of the carriage; For the first The overall competitiveness of a cleaning task; For hyperparameters; For normalization.

[0014] This invention assesses the distance relationship between cleaning robots and cleaning tasks, assigning higher weight to the correlation between nearby cleaning robots and tasks. Simultaneously, it considers the global competitiveness of cleaning tasks, giving higher exclusivity to tasks with lower global competitiveness. This allows the robot to gain priority in subsequent scheduling, automatically guiding it to avoid popular cleaning tasks already being targeted by multiple robots and instead focus on less competitive, closer tasks. This solves the localized congestion problem caused by all robots simultaneously rushing to the same cleaning task area in traditional multi-robot systems.

[0015] Preferably, the task allocation inhibition factor satisfies the expression: In the formula, For the first Taiwan cleaning robot for the first Task allocation inhibition factor for cleaning tasks; To exclude the first The serial numbers of all cleaning robots other than the one from Taiwan. Indicates the first Taiwan cleaning robot for the first Exclusivity of a cleaning task; This represents the total number of cleaning robots.

[0016] This invention assesses the level of attention other cleaning robots pay to a cleaning task, classifying highly-attracted tasks as popular tasks. Through a task allocation inhibition mechanism, the attractiveness of popular tasks to specific cleaning robots is automatically reduced. When multiple cleaning robots are simultaneously focused on a task, the system dynamically senses and strengthens its inhibition effect, prompting robots that might otherwise choose that task to turn to other, less competitive cleaning task areas. Simultaneously, for less popular cleaning tasks with low attention, the system correspondingly weakens its inhibition effect, increasing their attractiveness to nearby cleaning robots and guiding them to perform these neglected tasks. This avoids the localized congestion caused by all cleaning robots simultaneously rushing to the same cleaning task area in traditional multi-robot systems, enabling multiple cleaning robots to work efficiently and collaboratively in different areas of the vehicle.

[0017] Preferably, the total social gravitational vector satisfies the expression: In the formula, For the first The total social gravitational vector acting on the Taiwan cleaning robot; For the first Taiwan cleaning robot for the first Task allocation inhibition factor for cleaning tasks; For the first an area size of the i-th cleaning task; an area size of the cleaning task with the largest area among all cleaning tasks; a total number of cleaning tasks; a geometric center position vector of the i-th cleaning task, a real-time position vector of the i-th cleaning robot, a position vector of the i-th obstacle, a real-time position vector of the i-th cleaning robot, a module of the vector a module of the vector a gravitational gain coefficient.

[0018] The present application multiplies the task allocation inhibition factor complement with the cleaning task area normalized value, so that the cleaning task with lower competition degree and larger area obtains higher basic attraction, combines the attraction with the unit directional vector pointing to the cleaning task, forms social attraction with clear directionality, guides the cleaning robot to move towards the target area, so that when one cleaning task is focused on by multiple cleaning robots, the task allocation inhibition factor increases, resulting in weakening of the social attraction of the task to the specific cleaning robot, prompting the cleaning robot to automatically turn to other cleaning tasks with smaller competition, at the same time, the larger area cleaning task can obtain appropriate attention due to its stronger basic attraction, avoiding being completely ignored, realizing intelligent allocation of cleaning tasks among multiple cleaning robots, ensuring sufficient processing of large-area cleaning tasks, avoiding local congestion caused by simultaneous rush of all cleaning robots to the same cleaning task, improving the overall collaborative efficiency of the multi-robot system, shortening the total cleaning time, and ensuring the efficiency and stability of the multi-robot collaborative cleaning process.

[0019] Preferably, the repulsive force vector satisfies the expression: ; in the expression, a repulsive force vector of the i-th cleaning robot from the i-th obstacle; a repulsive force vector of the i-th cleaning robot from the i-th obstacle; a repulsive force gain coefficient; an Euclidean distance between the i-th cleaning robot and the i-th obstacle; a repulsive force action range; a real-time position vector of the i-th cleaning robot; a position vector of the i-th obstacle, a module of the vector a module of the vector a real-time position vector of the i-th cleaning robot; a position vector of the i-th obstacle, a module of the vector a module of the vector a module of the vector

[0020] Preferably, further comprising: when the module value of the total driving force of the cleaning robot exceeds the maximum driving force of the cleaning robot, scaling the module value of the total driving force to within the range of the actual power limit of the cleaning robot.

[0021] In a second aspect, the present application provides a cleaning robot work task scheduling system, comprising a processor and a memory, the memory storing computer program instructions which, when executed by the processor, implement the above-mentioned cleaning robot work task scheduling method.

[0022] By adopting the above technical solution, the above-mentioned cleaning robot work task scheduling method is generated into a computer program and stored in the memory to be loaded and executed by the processor, so that a terminal device is made according to the memory and the processor, and use is facilitated.

[0023] The present application has the following beneficial effects: The present application identifies the position and size of each residual coal block area in the car body, determines the global competitiveness of the cleaning task based on the size of the cleaning task area and the distance relationship of the cleaning robot, and can identify popular tasks that are easy to cause congestion. The present application calculates the exclusive tendency of the cleaning robot to the cleaning task and the task allocation inhibition factor, integrates the social relationship between the cleaning robots into the path planning, automatically reduces the attraction of a cleaning task to a specific cleaning robot when the cleaning task is concerned by multiple cleaning robots, and promotes the cleaning robot to turn to a smaller cleaning task, thereby avoiding the phenomenon of all cleaning robots rushing to the same cleaning task in the traditional method. The present application combines the social attractive field and the repulsive field, so that the cleaning robot can autonomously avoid collision while pursuing the cleaning task target, ensures orderly distribution in the limited car body space, and enables multiple cleaning robots to efficiently cooperate in different areas of the car body, thereby improving the cleaning efficiency, shortening the overall operation time, realizing intelligent and balanced distribution of the cleaning task among multiple cleaning robots, and improving the operation efficiency and reliability of the car dumper system. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 is a flowchart schematically showing a cleaning robot work task scheduling method in the present application; Figure 2 is a schematic diagram of an initial state of simulated car dumper cleaning task allocation; Figure 3 is a schematic diagram of an intermediate execution state of simulated car dumper cleaning task allocation; Figure 4 is a schematic diagram of a final completion state of simulated car dumper cleaning task allocation. DETAILED DESCRIPTION

[0025] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0026] The specific implementation of the present application will be described in detail below with reference to the drawings.

[0027] The embodiment of the present application discloses a cleaning robot task scheduling method, referring to Figure 1 , comprising steps S1-S3: S1, obtaining the cleaning task information inside the car body.

[0028] It should be noted that after the car body unloading and returning to the normal position by the car dumper system, there will be some residual coal blocks inside the car body, which constitute the tasks that need to be cleaned. In the actual car dumper working environment, due to the adhesion characteristics of coal blocks and the complexity of the car body structure, the residual coal blocks are usually distributed in the bottom, side wall and corner of the car body, and the distribution is uneven. In order to effectively allocate tasks and plan paths, it is necessary to accurately obtain all the area information of the car body that needs to be cleaned, including the position and size of each area. The present application identifies the residual coal block area inside the car body through the sensor array installed on the car dumper structure.

[0029] Specifically, through the sensor array installed above the car dumper structure, the inside of the car body is scanned after the car body is completed to unload and return to the normal position, and the residual coal block area that needs to be cleaned is identified. For each residual coal block area, the geometric center coordinates and the area size are obtained, and a cleaning task is established for each residual coal block area. At the same time, the real-time position of all cleaning robots in the unified coordinate system of the car body is obtained.

[0030] S2, determining the global competitiveness of the cleaning task according to the area size of the cleaning task and the distance of each cleaning robot to the cleaning task, determining the exclusive tendency degree of each cleaning robot to the cleaning task according to the global competitiveness of the cleaning task and the distance of each cleaning robot to the cleaning task, and determining the task allocation inhibition factor of the cleaning robot to the cleaning task according to the exclusive tendency degree of other cleaning robots to the cleaning task.

[0031] It should be noted that in the scenario of cleaning the tipper truck compartment, due to the limited space and regular shape of the compartment, path conflicts and task competition are prone to occur when multiple cleaning robots work simultaneously. For example, when multiple cleaning robots are attracted to the same cleaning task at the same time, it will not only cause local congestion, but also generate additional motion consumption due to mutual avoidance, reducing the overall cleaning efficiency. The traditional artificial potential field method only considers the distance relationship between the cleaning robot and the cleaning task, ignoring the dynamic characteristics of task competition in a multi-cleaning robot system, resulting in uneven distribution of cleaning tasks. Therefore, this invention introduces a cleaning task competition perception mechanism. By acquiring the global competitiveness of the cleaning task, the exclusive tendency of the cleaning robot to the cleaning task, and the task allocation inhibition factor, a social gravity field that can reflect the group's cooperative relationship is constructed. In this gravity field, the attraction of the cleaning task to the cleaning robot will be dynamically adjusted according to the real-time competitive situation. When a cleaning task is being watched by multiple cleaning robots, its attraction to a specific cleaning robot will be weakened accordingly, thereby prompting the cleaning robot to turn to other cleaning tasks with less competition, achieving a dynamic and balanced distribution of cleaning tasks.

[0032] Specifically, for any given cleaning task, the overall competitiveness of the cleaning task is determined based on the size of the area to be cleaned and the distance of each cleaning robot to the task. ; In the formula, For the first The overall competitiveness of a cleaning task; For the first The size of the area to be cleaned; The size of the largest cleaning task among all cleaning tasks, used for... Perform normalization; For the first The geometric center position vector of each cleaning task; For the first The real-time position vector of the cleaning robot; For vectors The modulus length reflects the first The cleaning task and the first The Euclidean distance between the cleaning robots; The total number of cleaning robots, This indicates that all cleaning robots have reached the [number]th [number]. The average distance of each cleaning task; The length of the diagonal of the carriage. Used for Perform normalization; This indicates normalization; the present invention employs linear normalization.

[0033] When the area of ​​the cleaning task The larger the area, the larger the task; large areas of coal residue are more attractive to the cleaning robot, thus increasing its overall competitiveness in the cleaning task. When the cleaning task is closer to the center of the robot cluster, the average distance... A smaller overall competitiveness leads to greater competition among robot vacuums, resulting in a higher overall competitiveness for the cleaning task. When the value is close to 1, it indicates that the cleaning task is highly likely to become a target of competition among multiple cleaning robots, resulting in a very high inherent risk of congestion. When the overall competitiveness... When the value is close to 0, it indicates that the cleaning task is not competitive and the cleaning robot pays little attention to the cleaning task.

[0034] Furthermore, for any given cleaning task, based on the overall competitiveness of the task and the distance of each cleaning robot to that task, the exclusivity preference of each cleaning robot for that task is determined: ; In the formula, For the first Taiwan cleaning robot for the first Exclusivity of a cleaning task; For the first The geometric center position vector of each cleaning task; For the first The real-time position vector of the cleaning robot; For vectors The modulus length reflects the first The cleaning task and the first The Euclidean distance between the cleaning robots; The length of the diagonal of the carriage. Used to eliminate Dimensions; For the first The overall competitiveness of a cleaning task; These are hyperparameters used to prevent When the value is 0, the denominator is 0. This invention In other embodiments, implementers may set the appropriate parameters according to the actual implementation situation. However, in order to reduce the impact on the degree of exclusionary tendency, Must meet ; For normalization, this invention employs linear normalization.

[0035] When the Taiwan cleaning robot approaches the first When assigning a cleaning task, I prefer to assign it to the first one. The cleaning machine performs the first A cleaning task, at this time Decrease Increase, so that the first Taiwan cleaning robot for the first The exclusivity tendency for the first cleaning task increases, but if the second task is more exclusive... The cleaning task is a popular cleaning task, and its overall competitiveness is... Very high, even if the cleaning task is far from the first Even when the cleaning robot is nearby, its tendency to exclude other cleaning robots remains low. Conversely, for remote or minor, less popular cleaning tasks, its overall competitiveness... The initial sensitivity is low; once a cleaning robot approaches, the robot's exclusivity towards that cleaning task increases dramatically.

[0036] Furthermore, for any given cleaning robot, based on the exclusivity of other cleaning robots towards the cleaning task, a task allocation inhibition factor for that cleaning robot is determined: ; In the formula, For the first Taiwan cleaning robot for the first Task allocation inhibition factor for cleaning tasks; To exclude the first The serial numbers of all cleaning robots other than the one from Taiwan. Indicates the first Taiwan cleaning robot for the first Exclusivity of a cleaning task; This represents the total number of cleaning robots.

[0037] In the formula, To exclude the first All cleaning robots other than the first cleaning robot are for the first The sum of the exclusivity tendencies for each cleaning task reflects the... Taiwan's cleaning robot competitors are the first The sum of intentions for each cleaning task; when the positions of other cleaning robots or the overall competitiveness of the cleaning task changes, causing other cleaning robots to affect the first cleaning task... When the sum of the exclusivity of each cleaning task increases, the task allocation inhibition factor... Increase, thus affecting the first Taiwan cleaning robot goes to the first The motivation for a cleaning task produces a stronger inhibitory effect; conversely, when When the value is small, the task allocation inhibition factor is low. Decrease, for the first Taiwan cleaning robot goes to the first The weaker the inhibition of a cleaning task, the better. This dynamic inhibition mechanism can prevent congestion caused by multiple cleaning robots rushing to the same cleaning task at the same time.

[0038] S3. Determine the total social gravitational force vector acting on the cleaning robot based on the task allocation inhibition factor and the distance from the cleaning robot to the cleaning task. Determine the repulsive force vector acting on the cleaning robot from the obstacle. Superimpose the total social gravitational force vector and the repulsive force vector to obtain the total driving force of the cleaning robot. Generate motion commands based on the total driving force and schedule the cleaning robot to perform cleaning.

[0039] It should be noted that in the actual working environment of cleaning the tipper truck compartment, the cleaning robots need to work efficiently and collaboratively within a limited space. They need to complete the cleaning task quickly while avoiding collisions and path conflicts. Traditional path planning methods often only focus on task completion efficiency and ignore the cooperative relationship between cleaning robots, resulting in frequent clustering of cleaning robots in actual operations. Therefore, this invention combines social gravitational fields with traditional repulsive fields, enabling cleaning robots to autonomously avoid collisions or congestion with other cleaning robots while pursuing task objectives. This achieves efficient and safe collaborative operation, ensuring that multiple cleaning robots are orderly distributed within a limited space, maximizing parallel operation efficiency, and avoiding motion stagnation caused by mutual interference.

[0040] Specifically, based on the task allocation inhibition factor and the distance between the cleaning robot and the cleaning task, the total social gravitational vector acting on the cleaning robot is determined: ; In the formula, For the first The total social gravitational vector acting on the Taiwan cleaning robot; For the first Taiwan cleaning robot for the first Task allocation inhibition factor for cleaning tasks; For the first The size of the area to be cleaned; The size of the largest cleaning task among all cleaning tasks, used for... Perform normalization; The total number of cleaning tasks; For the first The geometric center position vector of each cleaning task. For the first The real-time position vector of the cleaning robot. For vectors The length of the mold, Indicates the first Taiwan cleaning robot points to the first The unit direction vector for each cleaning task; is the attraction gain coefficient, used to control the strength of social attraction.

[0041] wherein, is the attraction gain coefficient, used to control the strength of social attraction, as a proportional coefficient, can adjust the size of social attraction, directly affects the driving force strength of the cleaning robot moving towards the cleaning task, The greater the value, the stronger the social attraction, the faster the cleaning robot tends to move towards the cleaning task, but it may cause system oscillation or increase the risk of collision, The smaller the value, the weaker the social attraction, the more conservative the cleaning robot moves, but it may cause the cleaning task to be completed inefficiently. To ensure that the system has enough driving force to complete the task and maintain the stability of the movement, The value of [0.1, 1.0] is in the range of [0.1, 1.0], and in the present application, is set to 0.5, which can balance the task completion efficiency and system stability, and avoid the cleaning robot moving too aggressively or too conservatively.

[0042] The present application dynamically modulates the fixed attraction in the traditional artificial potential field method according to the task competition situation, so that the cleaning robot can perceive the competition degree of the cleaning task. When the first The task allocation inhibition factor of the first The smaller the value, the smaller the competition of the cleaning task, and the greater the value, the greater the competition of the cleaning task. When the task allocation inhibition factor of the first The smaller the value, the smaller the competition of the cleaning task, and the greater the value, the greater the competition of the cleaning task. When the task allocation inhibition factor of the first The smaller the value, the smaller the competition of the cleaning task, and the greater the value, the greater the competition of the cleaning task. When the task allocation inhibition factor of the first When the task allocation inhibition factor of the first

[0043] Further, the repulsion vector of the first The cleaning robot from the first obstacle is determined as: ; wherein, is the repulsion vector of the first The cleaning robot from the first obstacle; is the repulsion gain coefficient, used to control the strength of repulsion; For the first Taiwan cleaning robot and the first The Euclidean distance between obstacles is calculated based on the coordinates of the geometric center of the cleaning robot and the geometric center of the obstacle. The range of the repulsive force; For the first The real-time position vector of the cleaning robot; For the first The position vectors of the obstacles For vectors The modulus length; Indicates the first The obstacle points to the first The unit direction vector of the cleaning machine.

[0044] Obstacles included the walls of the carriage and other cleaning robots, the first Taiwan's cleaning robots received from the first The magnitude of the repulsive force from an obstacle is inversely proportional to the distance between the robot and the obstacle. When the robot approaches the wall of the vehicle or other cleaning robots, the distance... Decrease, when distance Smaller than the range of repulsive force A repulsive force is generated at this distance; the smaller the distance, the greater the repulsive force, pushing the cleaning robot away from the vehicle wall or other cleaning robots. Greater than the range of repulsive force The repulsive force is zero.

[0045] In the formula, The repulsive gain coefficient controls the strength of the repulsive field, reflecting the system's sensitivity to obstacles and representing the degree of initiative in obstacle avoidance behavior. As a proportionality coefficient, it can adjust the magnitude of the repulsive force, directly affecting the strength of the cleaning robot's behavior in avoiding obstacles and avoiding each other. The larger the size, the stronger the repulsive force, and the more aggressive the obstacle avoidance behavior of the cleaning robot, but this may lead to an overly tortuous movement trajectory, increasing the total path length; The smaller the value, the weaker the repulsive force, resulting in a more conservative obstacle avoidance behavior for the cleaning robot, but this may increase the risk of collisions. To ensure the system effectively avoids collisions without excessively impacting task execution efficiency, The range of values ​​is In this invention, Setting it to 0.05 balances obstacle avoidance safety and task execution efficiency, ensuring that the cleaning robot can work safely and collaboratively in a limited space.

[0046] In the formula, The range of repulsive force is used to determine the distance threshold at which the repulsive force begins to take effect, when the cleaning robot is at a distance from the obstacle. Less than At that time, the repulsive force begins to act, when the distance... Greater than At that time, the repulsive force is zero. It reflects the system's perception range of obstacles and represents the size of the safety buffer zone. The larger the force, the wider the repulsive force range, and the earlier the cleaning robot will start to avoid it, but this may cause the cleaning task to detour excessively. The smaller the value, the narrower the repulsive force's range, and the more delayed the cleaning robot's avoidance behavior. However, this may lead to collisions due to untimely avoidance. To ensure the system has sufficient safety buffer without excessively impacting task execution efficiency, It needs to be larger than the maximum feature size of the end effector of the cleaning robot, and less than twice the maximum feature size of the end effector of the cleaning robot. In this invention, The size is set to 1.5 times the maximum feature size of the end effector of the cleaning robot to ensure safe and collaborative operation of the cleaning robot in a confined space.

[0047] Furthermore, by superimposing the total social gravitational force vector acting on the cleaning robot with the repulsive force vectors acting on the cleaning robot from all obstacles, we obtain the total driving force of the cleaning robot: ; In the formula, For the first The total driving force of the cleaning robot; For the first The total social gravitational vector acting on the Taiwan cleaning robot; For the first Taiwan's cleaning robots received from the first The repulsive force vector of each obstacle; Let be the total number of obstacles. The total driving force is the vector sum of social attraction and all repulsive forces, which together determine the direction and speed of the cleaning robot's movement. The total social gravitational vector acting on the Taiwan cleaning robot increases At that time, total driving force It tends to point in the direction of the cleaning task; when the cleaning robot approaches an obstacle or another cleaning robot... Increase, total driving force It may deviate from the direction of obstacles. Through this combined force calculation, the cleaning robot can maintain a safe distance from obstacles and other cleaning robots while pursuing its task objectives, ensuring safe and smooth movement. It is important to note that when... The modulus value exceeds the maximum driving force of the cleaning robot. When, the modulus is scaled to the range of to meet the actual power limit of the sweeping robot.

[0048] Further, according to the size and direction of the total driving force , the speed and angular velocity instructions of the first sweeping robot bottom layer motion controller are generated to drive the first sweeping robot to move.

[0049] When the system detects that any sweeping robot has reached and completed the cleaning task, the cleaning task is removed from the set of to-be-cleaned tasks. In the next control cycle, based on the updated set of to-be-cleaned tasks, all operations of steps S2 and S3 are repeated to recalculate the social gravitational field and generate new motion instructions for all sweeping robots until the set of to-be-cleaned tasks is empty, and the iteration is stopped.

[0050] In particular, to prevent the deadlock situation of ignoring the cleaning task, when a certain cleaning task is not selected by any sweeping robot for consecutive control cycles, the task assignment suppression factor of all sweeping robots for this cleaning task is multiplied by a decay coefficient ( ), reducing the suppression degree, thereby increasing the attraction of the cleaning task to the sweeping robots, ensuring that even the most remote or smallest cleaning task can be assigned and executed within a reasonable time, avoiding the deadlock phenomenon.

[0051] wherein, is the number of deadlock detection cycles, which is used to determine the judgment standard for ignoring the cleaning task. When a certain cleaning task is not selected by any sweeping robot for consecutive control cycles, the system starts the deadlock prevention mechanism. represents the maximum length of time for which the cleaning task is reasonably ignored. When is larger, the system has a higher tolerance for uneven distribution of cleaning tasks, which may cause remote or small cleaning tasks to be ignored for a long time; is smaller, the system has a higher sensitivity to uneven distribution of cleaning tasks, which may cause the deadlock prevention mechanism to be triggered frequently, interfering with normal cleaning task assignment. To ensure that the system can both timely detect ignored tasks and not excessively interfere with normal task assignment, the value range of is control cycles. In the present application, is set to 5 control cycles, ensuring that even the most remote or smallest task can be assigned and executed within a reasonable time.

[0052] is a decay coefficient used to adjust the strength of the deadlock prevention mechanism, in which, when a certain cleaning task is determined to be possibly deadlocked, the task assignment inhibition factor of all cleaning robots to the cleaning task is multiplied by to reduce the inhibition degree and enhance the task attraction. represents the aggressiveness of the deadlock prevention mechanism, The greater is, the smaller the decay degree of the inhibition factor is, the slower the attraction of the cleaning task is improved, and the deadlock prevention mechanism is more conservative. The smaller is, the greater the decay degree of the inhibition factor is, the faster the attraction of the cleaning task is improved, and the deadlock prevention mechanism is more aggressive. To ensure that the system can effectively prevent deadlock and will not interfere with normal task assignment, is set to 0.9, which ensures that the ignored task is re-assigned within a reasonable time, while avoiding excessive interference with normal task assignment.

[0053] Exemplarily, Figure 2 is an initial state diagram of the simulation of the assignment of the dumper cleaning task, Figure 3 is an intermediate execution state diagram of the simulation of the assignment of the dumper cleaning task, Figure 4 is a final completion state diagram of the simulation of the assignment of the dumper cleaning task.

[0054] The embodiment of the present application also discloses a cleaning robot work task scheduling system, comprising a processor and a memory, and the memory stores computer program instructions.

[0055] The above system also includes a communication bus and a communication interface and other components well known to those skilled in the art, the settings and functions of which are known in the art, and therefore will not be described here.

Claims

1. A method for scheduling the work tasks of a cleaning robot, characterized in that, The method comprises: obtaining cleaning task information of the interior of the car body; determining global competitiveness of the cleaning task according to the area size of the cleaning task and the distance of each cleaning robot to the cleaning task, determining exclusive tendency of each cleaning robot to the cleaning task according to the global competitiveness of the cleaning task and the distance of each cleaning robot to the cleaning task, and determining a task allocation inhibition factor of the cleaning robot to the cleaning task according to the exclusive tendency of other cleaning robots to the cleaning task; determining a total social attractive force vector received by the cleaning robot according to the task allocation inhibition factor and the distance of the cleaning robot to the cleaning task, determining a repulsive force vector received by the cleaning robot from an obstacle, superimposing the total social attractive force vector and the repulsive force vector to obtain a total driving force of the cleaning robot, generating a motion instruction according to the total driving force, and dispatching the cleaning robot to clean.

2. The method of claim 1, wherein, The method further comprises: when the cleaning task is not selected within a continuous preset period, reducing the task allocation inhibition factor of all cleaning robots to the cleaning task to enhance the attraction of the cleaning task to the cleaning robots.

3. The method of claim 1, wherein, The method further comprises: scanning the interior of the car body after the car body is returned to the normal position by a car dumper structure through a sensor array installed above the car dumper structure, identifying residual coal block areas that need to be cleaned, obtaining the geometric center coordinates and the area size of each residual coal block area, and establishing a cleaning task for each residual coal block area.

4. The method of claim 1, wherein, The global competitiveness satisfies the expression: ； wherein, is the global competitiveness of the i-th cleaning task; is the global competitiveness of the i-th cleaning task; is the area size of the i-th cleaning task; is the area size of the i-th cleaning task; is the area size of the largest area among all cleaning tasks; is the geometric center position vector of the i-th cleaning task; is the geometric center position vector of the i-th cleaning task; is the real-time position vector of the j-th cleaning robot; is the real-time position vector of the j-th cleaning robot; is the length of the vector is the length of the vector is the total number of cleaning robots; is the length of the diagonal line of the carriage; denotes normalization.

5. The method of claim 1, wherein, The exclusive tendency satisfies the expression: ； wherein, is the exclusive tendency of the i-th cleaning robot to the j-th cleaning task; is the geometric center position vector of the j-th cleaning task; is the real-time position vector of the i-th cleaning robot; is the modulus of the vector is the diagonal length of the vehicle cabin; is the global competitiveness of the j-th cleaning task; is the normalization.

6. The method of claim 1, wherein, The task allocation inhibition factor satisfies the expression: ； In the formula, For the first Taiwan cleaning robot for the first Task allocation inhibition factor for cleaning tasks; To exclude the first The serial numbers of all cleaning robots other than the one from Taiwan. Indicates the first Taiwan cleaning robot for the first Exclusivity of a cleaning task; This represents the total number of cleaning robots.

7. The method of claim 1, wherein, The total social attractive force vector satisfies the expression: ； In the formula, For the first The total social gravitational vector acting on the Taiwan cleaning robot; For the first Taiwan cleaning robot for the first Task allocation inhibition factor for cleaning tasks; For the first The size of the area to be cleaned; The size of the largest cleaning task among all cleaning tasks; The total number of cleaning tasks; For the first The geometric center position vector of each cleaning task. For the first The real-time position vector of the cleaning robot. For vectors The modulus length; This is the gravitational gain coefficient.

8. The method of claim 1, wherein, The repulsive force vector satisfies the expression: ； wherein, is the repulsive force vector from the first obstacle to the cleaning robot; is the repulsive force gain coefficient; is the Euclidean distance between the first obstacle and the cleaning robot; is the repulsive force action range; is the real-time position vector of the first cleaning robot; is the position vector of the first obstacle, is the module of the vector .​​ 9. The method of claim 1, wherein, The method further comprises: When the modulus of the total driving force of the cleaning robot exceeds the maximum driving force of the cleaning robot, the modulus of the total driving force is scaled to be within the range of 0 to 100% of the maximum driving force of the cleaning robot to comply with the actual power limit of the cleaning robot. of the maximum driving force of the cleaning robot to comply with the actual power limit of the cleaning robot.

10. A cleaning robot work task scheduling system characterized by, The method comprises: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a cleaning robot work task scheduling method according to any one of claims 1-9 is realized.

Images