CN115877854B - Control system of unmanned fork type mobile robot - Google Patents
Control system of unmanned fork type mobile robot Download PDFInfo
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- CN115877854B CN115877854B CN202310194035.2A CN202310194035A CN115877854B CN 115877854 B CN115877854 B CN 115877854B CN 202310194035 A CN202310194035 A CN 202310194035A CN 115877854 B CN115877854 B CN 115877854B
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Abstract
The application provides a control system of unmanned fork mobile robot, include: the positioning unit acquires the specific positions of each robot and each tray material and displays the specific positions in a three-dimensional workshop topographic map; the image processing unit generates a two-dimensional map and performs grid division on the two-dimensional map; the wireless communication unit transmits the residual electric quantity data to the upper control system; the upper control system judges whether the information of the residual electric quantity of the robot is lower than a preset safety threshold value or not; the ant colony algorithm is adopted to plan a route through the scheduling unit, and the robot executes movement according to the planned route; judging the actual positions of the pallet fork and the pallet materials of the robot by a laser radar and video data fusion technology; in the process of carrying and moving cargoes, the route of each robot executing task is optimized through an intelligent route planning algorithm, so that the unmanned forklift mobile robot accurately acquires the position of a goods shelf, and the proper robot is selected for executing tasks according to each route and the monitoring management of the electric quantity of the robot.
Description
Technical Field
The application relates to the technical field of robot management control systems, in particular to a control system of an unmanned fork type mobile robot.
Background
The pallet is widely used for carrying goods in modern intelligent manufacturing or logistics storage, and the pallet is lifted and moved by a forklift to realize the carrying operation of the goods. The traditional forklift adopts manual operation, and operators can easily influence the working efficiency and quality due to subjective and objective factors such as emotion or physiology and the like due to long-time repeated and high-strength operation. The modern fork truck type unmanned robot can realize all functions of a manned fork truck, not only reduces the damage rate of cargoes, but also can realize 24-hour endless operation and automatic charging functions, thereby greatly saving time and improving the continuity of operation. Unmanned forklift systems are required to be upgraded and improved on traditional forklift trucks to form an automatic driving system, controllers and sensors are added, a dispatching system is arranged at the background, and task allocation and management of a robot system are realized through a wireless network. On the basis, unmanned intelligent operation and even cooperative operation of the forklift are realized. However, the forklift belongs to heavy load equipment, has strong carrying capacity, and has high dependence on power supply capacity.
In the prior art CN201710439213.8, a power management method and an intelligent robot are disclosed, the method is applied to the intelligent robot, the intelligent robot includes a plurality of functional components, the intelligent robot supplies power to each functional component through independent power supply channels, the method includes: determining a current use scene and the current residual electric quantity of the current use scene; comparing the residual electric quantity with a power-off electric quantity threshold corresponding to each functional component in the current use scene, wherein the power-off electric quantity threshold corresponding to each functional component is inversely related to the power supply demand priority in any use scene; and if the residual electric quantity is smaller than the power-off electric quantity threshold value corresponding to any functional component in the current use scene, stopping supplying power to the functional component. Although the existing unmanned forklift mobile robot can automatically work for 24 hours, if no work task exists, the forklift enters a waiting state, at the moment, standby current still consumes power of the forklift to influence the standby time of the forklift, each route is not reasonably designed and the proper robot is selected in the process of carrying and accurately positioning and moving goods, the robot possibly consumes electric quantity in the process of executing the task, the problem of overdischarge of a lithium battery possibly exists in use, and once the lithium battery is overdischarged, a protection circuit of the battery is started to lock the battery, and at the moment, the robot can not be charged, so that the unmanned forklift mobile robot control system is provided.
Disclosure of Invention
The purpose of this application is to provide a control system of unmanned fork mobile robot, aims at solving at current unmanned fork truck mobile robot and has not reasonable design each route and selects suitable robot to the accurate location of goods transport and the in-process that removes, and the robot probably will be with the electric quantity consumption in the in-process of executing the task, and the battery starts the dead function of lock, probably leads to the unable problem of charging of robot.
The present application also provides a control system for an unmanned forklift robot, comprising: the upper control system and the at least one unmanned fork type mobile robot; the upper control system generates task information and a planned route according to the task instruction;
the positioning unit acquires the specific positions of each robot and each tray material, and displays the specific positions in a three-dimensional workshop topographic map of the upper control system;
generating a two-dimensional map through an image processing unit according to ground information in a three-dimensional workshop topographic map and specific position information of each robot and each tray material, and performing grid division on the two-dimensional map;
the monitoring unit acquires the residual electric quantity data of each robot; transmitting the residual electric quantity data to an upper control system through a wireless communication unit;
the upper control system judges whether the information of the residual electric quantity of the robot is lower than a preset safety threshold value, if yes, a charging task instruction is dispatched; if not, dispatching a task for carrying the tray materials;
the upper control system carries out ant colony algorithm planning route through a scheduling unit according to the task information, the position information of the tray materials to be carried, corresponding to the task information, and the position information of each robot with the task information for carrying the tray materials, and the robot carries out movement according to the planning route;
the actual positions of the pallet fork and the pallet materials of the robot are judged through a laser radar and video data fusion technology, the pallet materials are carried after the automobile body is adjusted according to the actual positions, and the monitoring unit monitors the residual electric quantity information of the robot in real time.
Further, the upper control system judges whether the residual electric quantity information of the robot is lower than a preset safety threshold, if yes, the step of dispatching a charging task instruction comprises judging whether the preset safety threshold is at a safety guard threshold, if yes, the robot is immediately shut down to enter a dormant state to wait for manual intervention and then control the robot to enter a safety area and recharge; if not, the robot moves to the charging area by itself according to the charging task instruction.
Further, the step of dispatching the task of carrying the tray material includes judging whether the information of the residual electric quantity of the robot is lower than a task warning threshold; if yes, a normal task with longer predicted conveying time is not sent to the robot; if not, all tasks are dispatched normally.
Further, the upper control system performs an ant colony algorithm route planning according to the position information of the tray material to be carried corresponding to the task information and the position information of each robot with the dispatch of the tray material to be carried through a dispatching unit, and the robot performs movement according to the planned route, which comprises the following steps;
and determining a path after determining the position point of each robot and the position point of the tray material in the two-dimensional map gridding, calculating the path length after determining the path, determining the minimum value of the path length, and updating the pheromone according to different path lengths.
Further, the step of determining the path includes determining the paths of the m robots respectively by circularly calculating m times; for one robot, n-1 times are needed to be selected, and each time the robot selects, the point which has passed through cannot be used as the next point of the path; when determining the next point, calculating the selection probability of each point to be selected, and selecting by adopting a roulette method after determining the probability of each point to be selected; the probability of selecting a point to be selected can be determined by:
wherein: m is a pheromone matrix, alpha is a pheromone influence factor, beta is a heuristic function influence factor, D is a distance matrix, i is a starting point, and j is a target point.
In the roulette method, the number of selected points of the device is X, an X-dimensional vector X is used for representing the probability of being selected, normalization processing is carried out on the X, namely, X (i) =x (i)/sum (X (i)), the sum of the i items before the vector is calculated respectively, the sum can be used as new X (i) in Matlab and realized by x=cumsum (X), a random number r between 0 and 1 is finally generated, the first element larger than r in the vector X is found, and the represented alternative point is used as the next point.
Further, in the pheromone updating step, the amount of change in the pheromone caused by the difference in path length is:
wherein: and M represents a pheromone change matrix, is initially set as 0,S to be constant, and L (i) represents the path length of the ith robot in the iterative process.
Further, the actual positions of the pallet fork and the pallet material of the robot are judged through a laser radar and video data fusion technology, the pallet material is carried after the vehicle body is adjusted according to the actual positions, and in the step of monitoring the residual electric quantity information of the robot in real time by a monitoring unit, the robot carries the pallet material after the vehicle body is adjusted according to the positions of the fork holes, wherein the step comprises the steps of scanning the original data formed by the laser radar or a camera of the robot to the external environment, and the steps of image processing and pattern recognition are adopted to identify the positions of fork holes of the pallet and the pallet material after the vehicle body is adjusted according to the positions of the fork holes.
Further, the intelligent identification process of the robot to the position of the tray material jack is as follows:
driving a network port of the laser radar through a local network and reading data of the port; forming a point cloud three-dimensional image or a plane image by utilizing a laser radar with point cloud output to statically scan or dynamically scan the side surface of a shelf tray up and down;
analyzing the three-dimensional map or the plan map of the point cloud for feature recognition, judging the position of a fork hole of a fork, finding out the center point and the center line of the tray, if the position of the fork hole cannot be analyzed, reporting errors, and prompting failure of feature recognition; if the scan is successful, a side view of the tray is formed;
a coordinate system is established by taking a laser radar on the forklift as a reference, a measurement reference of the forklift and the pallet is formed, and a top view of the forklift system is established; measuring the position coordinate XY of the central point of the front tray in a coordinate system, and forming an angle A between the central line of the tray and the central line of the forklift according to the relation between the central line of the tray and the coordinate system; wherein the angle a and XY coordinates are signed numbers with signs;
and outputting X, Y, A three parameters to a local or background computing unit according to an interface protocol, and adjusting the position and the posture of the forklift by the three parameters to ensure that the picking operation of the forklift is started after the forklift is at the correct position.
The beneficial effects are that:
1. the intelligent route planning method has the advantages that the route of each robot executing task is optimized through the intelligent route planning algorithm in the process of carrying and moving goods, so that the unmanned forklift moving robot accurately obtains the position of a goods shelf, the robot executing task is selected according to each route and the monitoring management of the electric quantity of the robot, the electric quantity of a battery of the robot is monitored and protected, and the problem that the battery is dead and cannot be charged due to high battery electric quantity loss in the process of executing the task of the robot is solved.
2. Adopt laser radar or camera that takes on the fork truck to scan and discern the goods tray of high-rise position, the accurate tray jack position of confirming realizes the accurate transport of system automatic control regulation unmanned fork truck mobile robot to the tray under various circumstances from this.
3. Detecting a task state of the forklift robot by using an upper control system, and driving the robot to enter a corresponding standby power-down protection state according to the task idle degree of the robot; meanwhile, when the upper control system detects the task instruction again, a signal is sent to wake up the robot, so that the robot enters a charged standby state; therefore, the electric quantity of the robot is protected.
Drawings
Fig. 1 is a control flow diagram of a control system of an unmanned forklift robot according to an embodiment of the present application.
Fig. 2 is a point cloud of a tray according to an embodiment of the present application.
Fig. 3 is a schematic diagram of identifying a center point and a center line of a tray from a tray point cloud according to an embodiment of the present application.
Fig. 4 is a schematic diagram of a forklift and a pallet in a robot coordinate system according to an embodiment of the present application.
The implementation, functional features and advantages of the present application will be further described with reference to the accompanying drawings in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
Referring to fig. 1-4, a control system for an unmanned forklift robot, comprising: the upper control system and the at least one unmanned fork type mobile robot; the upper control system generates task information and a planned route according to the task instruction;
s1, a positioning unit acquires specific positions of each robot and tray material, and displays the specific positions in a three-dimensional workshop topographic map of an upper control system; the three-dimensional workshop topographic map comprises a storage rack for placing materials, each grid of each layer is provided with a number, the storage rack numbers, the case numbers and the layer numbers are divided into areas, storage rack numbers, case numbers and layer numbers in sequence, for example, the numbers of the 1 st layer of the 1 st storage rack of the A area are A1-1-5, and the specific positions of the target tray materials can be rapidly determined;
s2, generating a two-dimensional map through an image processing unit according to ground information in a three-dimensional workshop topographic map and specific position information of each robot and each tray material, and performing grid division on the two-dimensional map; specifically, the two-dimensional map is subjected to grid division according to the precision requirement, for example, 100 multiplied by 100; obtaining a starting point and a target point (i, j), if the starting point i is (21, 2), the target point j is (52,8), and positioning through the two-dimensional map after grid division;
s3, the monitoring unit acquires the residual electric quantity data of each robot; transmitting the residual electric quantity data to an upper control system through a wireless communication unit; the upper control system is a cloud server and is connected with the robot through wireless communication;
s4, the upper control system judges whether the information of the residual electric quantity of the robot is lower than a preset safety threshold, wherein the preset safety threshold is that the residual electric quantity is within 20%; if yes, sending a charging task instruction, specifically, when the residual electric quantity of the robot is lower than 20%, the robot only executes a charging task, and the upper control system charges the task robot which immediately sends out the charging task; if not, dispatching a task for carrying the tray materials, specifically, carrying out the task for carrying the tray materials and carrying out the task for charging when the residual electric quantity of the robot is more than 20%;
s5, the upper control system carries out ant colony algorithm planning route through a scheduling unit according to the position information of the tray materials to be carried, which corresponds to the task information, and the position information of each robot provided with the dispatching carrying tray materials, the scheduling unit generates a shortest route according to the position of each robot and the position of the material with the carrying tray in an adapting way, the shortest route can be intelligently generated, the route of the robot for executing the task can be selected, the route with the nearest moving distance is selected so as to improve the working efficiency and save the electric quantity, the robot executes movement according to the planning route, and particularly the robot moves to the target point according to route planning at the starting point;
s6, when the robot moves to a target point and then carries the tray materials, judging the actual positions of the forks of the robot and the tray materials through a laser radar and video data fusion technology, carrying the tray materials after adjusting the vehicle body according to the actual positions, specifically, firstly determining the positions between the forks of the robot and the tray materials through the laser radar and video data fusion technology, and judging whether the positions of fork holes of the forks of the robot and the tray materials are in an aligned state or not, and adjusting the robot body if the positions of fork holes of the forks are not aligned, so that the fork holes are aligned with the forks, and then carrying the tray materials by the robot after the forks are adaptively inserted into the fork holes; the monitoring unit monitors the residual electric quantity information of the robot in real time, specifically, the monitoring unit monitors the residual electric quantity information of the robot in real time in the process that the robot carries tray materials, when the electric quantity information is lower than a safety threshold, namely the residual electric quantity is lower than 20%, the robot reports the state to an upper control system, the robot stops tasks, the upper control system further allocates a robot to take over the tasks, the upper control system sends out a charging task to the robot, and the robot executes the charging task after waiting for the scheduling unit to plan a route of the robot, so that the battery protection of each robot is effectively ensured, and the battery is prevented from being excessively discharged and locked.
In this embodiment, the upper control system determines whether the remaining capacity information of the robot is lower than a preset safety threshold, if yes, a charging task instruction is dispatched, and if not, a tray material transporting task is dispatched, including;
judging whether a preset safety threshold is in a safety warning threshold or not, if so, immediately powering off the robot to enter a dormant state, waiting for manual intervention, and controlling the robot to enter a safety area and recharging; if not, the robot moves to the charging area according to the charging task instruction by itself; judging whether a preset safety threshold is within a safety warning threshold, wherein the safety warning threshold is that the residual electric quantity is within 10%, if the residual electric quantity is lower than 10%, immediately powering off the robot to enter a dormant state, waiting for manual intervention, and then controlling the robot to enter a safety area and recharging; if the residual electric quantity is higher than 10% and lower than 20%, the robot automatically moves to a charging area according to the charging task instruction; the method comprises the steps of dispatching a task for carrying the tray materials, wherein the step comprises the steps of judging whether the information of the residual electric quantity of the robot is lower than a task warning threshold value; if yes, a normal task with longer predicted conveying time is not sent to the robot; if not, all tasks are dispatched normally, specifically, if the residual electric quantity is more than 20%, the step of dispatching the task for carrying the tray materials comprises the steps of judging whether the information of the residual electric quantity of the robot is lower than a task warning threshold value, wherein the task warning threshold value is 30% of the residual electric quantity; if the remaining capacity of the robot is below 30%, a normal task with a longer expected conveying time is not dispatched to the robot, specifically, a situation that the moving distance is far away and the conveying tray material is at a higher layer of a rack is not dispatched to the robot; if the remaining power of the robot is 30%, all tasks are normally dispatched, and specifically, the robot can execute all tasks.
In the specific implementation, for all tasks, the electric quantity of any robot consumed by a single task is not more than 10%, 30% of the electric quantity is set as a task warning line of the robot, and normal tasks are not dispatched to the robot when the electric quantity is lower than the threshold; the 20% electric quantity is a safety warning line of the robot, and the charging task is started immediately when the electric quantity is lower than the threshold value; the 10% electric quantity is a life warning line of the robot, and when the electric quantity is lower than the threshold value, the robot is immediately powered off and enters a power-off sleep state; under normal conditions, the electric quantity of all robots is expected to be more than 50% as much as possible, namely when the robots do not have task dispatch, an upper control system dispatches a charging task to the robots, so that the electric quantity of the robots can be more than 50% as much as possible; the upper control system, namely the cloud scheduling system, has priority level on tasks of the robot, and the task with the highest monopolization level on a single robot cannot be occupied by other tasks and can be reassigned after the task is executed; the non-exclusive task priority level is relatively low, and the task can be occupied by other exclusive tasks, so that when the non-exclusive task is executed, a new exclusive task can be received, the current non-exclusive task is abandoned, the new exclusive task just received is executed, specifically, when the electric quantity of a certain standby mobile robot is monitored to be lower than 30%, an exclusive 'charging' task is generated for the robot, and the robot is dispatched to charge a charging pile from the current position; when the electric quantity is charged to more than 50%, the charging task of the robot is changed into a non-exclusive task and can be occupied by other higher-level tasks, such as other new transportation tasks; when the electric quantity of a certain mobile robot is monitored to be 30% -50%, allowing the temporary task of the robot to be idle, and enabling the robot to be in an idle standby state for a certain period of time, then giving a non-exclusive 'charging' task to the robot, and dispatching the robot to go to a charging pile from the current position for charging; in the process, once a higher level task appears, the task is executed instead; when no task in the task pool can be dispatched, the system is in an idle state for a long time, a batch of charging tasks are spontaneously generated at the moment, robots to be tested are charged, sorting is carried out according to the power shortage state of the robots, the robots with the lowest driving electric quantity are charged in priority, after the electric quantity is full, the charging tasks are finished, the robots leave a charging potential, and travel to enter a standby position for waiting tasks; at this time, the charging pile is idle, a new robot executes a charging task, and the robot enters a standby state again after the task is executed; when the robot is in a standby state, when the self electric quantity is monitored to be lower than 20% -30%, reporting the state to the cloud, then receiving a charging task issued by the cloud and executing the task, in the task execution process, when the self electric quantity is monitored to be between 10% -20%, reporting the state to the cloud, under the control of the cloud, transferring the current task to other robots meeting the task condition, then requesting a 'charging' task by the cloud, and executing the task after the cloud confirms and gives the task details; in any case, when the self electric quantity is monitored to be lower than 10%, immediately reporting the state to the cloud, rapidly entering a power-off protection state, controlling a program to cut off all power sources of the self, and stopping shutdown in situ to enter the power-off protection mode; after manual intervention, controlling the robot to enter a safe area and recharging; in the electrical design of the robot, a one-key start button is arranged, so that the robot entering the power-down protection state can be started by one key, and then the robot is controlled to enter the charging state.
In this embodiment, the upper control system performs an ant colony algorithm planning route through a scheduling unit according to task information and position information of a tray material to be carried and position information of each robot with a dispatch carrying tray material corresponding to the task information, and the step of performing movement of the robot according to the planning route includes determining a path after determining a position point (i.e., a starting point) of each robot and a position point (i.e., a target point) of the tray material in two-dimensional map gridding, calculating a path length after determining the path and determining a minimum value of the path length, updating pheromones according to different path lengths, and initializing parameters, i.e., the number m of robots, the number n of points on a two-dimensional map, an pheromone influence factor alpha, an heuristic function influence factor beta, an heuristic function gamma, and a combined action of both heuristic functions and pheromones, to influence the selection of the robot on a next target point; the pheromone matrix M is used for recording pheromones between two points, and each element can be initially set to be 1; a distance matrix D, recording the distance between two points, for example, D (i, j) represents the distance between the point i and the point j, and knowing that the corresponding element is 0;
the step of determining the path comprises the steps of respectively determining the paths of m robots through circularly calculating m times; for one robot, n-1 times are needed to be selected, and each time the robot selects, the point which has passed through cannot be used as the next point of the path; when determining the next point, calculating the selection probability of each point to be selected, and selecting by adopting a roulette method after determining the probability of each point to be selected; the probability of selecting a point to be selected can be determined by:
wherein: m is a pheromone matrix, alpha is a pheromone influence factor, beta is a heuristic function influence factor, D is a distance matrix, i is a starting point, and j is a target point; the above method is easy to understand, the higher the concentration of pheromone between the point to be selected and the current point is, the shorter the distance is, the larger the probability of being selected is, and after the probability of each alternative point is determined, the selection is carried out by adopting a roulette method;
in the roulette method, the number of selected points of equipment is X, an X-dimensional vector X is used for representing the selected probability, normalization processing is carried out on X, namely X (i) =X (i)/sum (X (i)), the sum of i items before the vector is calculated respectively, the sum is taken as new X (i), matlab can be realized by X=cumsum (X), matlab is a combination of two words of matrix & laboratory, namely a matrix factory, namely a matrix laboratory, and a plurality of powerful functions such as numerical analysis, matrix calculation, scientific data visualization, modeling and simulation of a nonlinear dynamic system are integrated in a window environment which is easy to use, so that a comprehensive solution is provided for scientific research, engineering design and a plurality of scientific fields which have to carry out effective numerical calculation, and the editing mode of a traditional non-interactive programming language (such as C, fortran) is eliminated to a great extent; finally, generating a random number r between 0 and 1, finding out the first element larger than r in the vector X, and taking the alternative point represented by the element as the next point; in the process of generating paths, an m×n matrix Path is needed for recording the Path of each robot;
considering the obstacle avoidance problem, the points with obstacles in the two-dimensional obstacle matrix O map can be added, the corresponding elements in O are set to 0, otherwise, the corresponding elements in O are set to 1, and meanwhile, the selection probability calculation formula of the candidate points is changed;
that is, the selection probability of the point where the obstacle is located is 0; after all paths of m robots are determined, calculating the length of each path, calculating whether the robot is at the same point in the moving process of the other robot or not through time and speed, excluding the collision route by adjusting the moving speed of the robot, finding out the shortest path L, and finally comparing the shortest path with the historical shortest path L (which can be initially set to a larger value) to determine whether to update the shortest path;
in the pheromone updating step, the amount of change in the pheromone caused by the difference in path length is:
wherein: m represents a pheromone change matrix, is initially set as 0,S and is constant, and L (i) represents the path length of the ith robot in the iterative process; considering the attenuation of the pheromone, the pheromone is updated as follows:
wherein weak represents the attenuation factor of the pheromone, and thus, an iteration process is completed, and a reasonable route is iterated in a reciprocating way.
In the embodiment, the actual positions of the pallet fork and pallet materials of the robot are judged through a laser radar and video data fusion technology, the pallet materials are carried after the automobile body is regulated according to the actual positions, and the monitoring unit monitors the residual electric quantity information of the robot in real time, wherein the step comprises the steps of scanning original data formed by the laser radar or a camera of the robot to the external environment, and the positions of fork holes matched with the pallet and the pallet fork are identified through image processing and pattern recognition post-processing; the intelligent identification flow of the robot to the position of the tray material jack is as follows:
driving a network port of the laser radar through a local network and reading data of the port; forming a point cloud three-dimensional diagram or a plane diagram as shown in fig. 2 by utilizing a laser radar with point cloud output to statically scan or dynamically scan the side surface (fork hole direction) of a goods shelf tray up and down;
analyzing the three-dimensional map or the plan map of the point cloud for feature recognition, judging the position of a fork hole of a fork, finding out the center point and the center line of the tray, if the position of the fork hole cannot be analyzed, reporting errors, and prompting failure of feature recognition; if the scan is successful, a side view of the tray is formed, as follows FIG. 3;
establishing a coordinate system by taking a laser radar on the forklift as a reference, forming a measurement reference of the forklift and the pallet, and establishing a top view of the forklift system according to the measurement reference, as shown in fig. 4; measuring the position coordinate XY of the central point of the front tray in a coordinate system, and forming an angle A between the central line of the tray and the central line of the forklift according to the relation between the central line of the tray and the coordinate system; wherein the angle a and XY coordinates are signed numbers with signs;
and outputting X, Y, A three parameters to a local or background computing unit according to an interface protocol, and adjusting the position and the posture of the forklift by the three parameters to ensure that the picking operation of the forklift is started after the forklift is at the correct position.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the scope of the claims, and all equivalent structures or equivalent processes using the descriptions and drawings of the present application, or direct or indirect application in other related technical fields are included in the scope of the claims of the present application.
Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the application, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. A control system for an unmanned forklift robot, comprising: the upper control system and the at least one unmanned fork type mobile robot; the upper control system generates task information and a planned route according to the task instruction;
the positioning unit acquires the specific positions of each robot and each tray material, and displays the specific positions in a three-dimensional workshop topographic map of the upper control system;
generating a two-dimensional map through an image processing unit according to ground information in a three-dimensional workshop topographic map and specific position information of each robot and each tray material, and performing grid division on the two-dimensional map;
the monitoring unit acquires the residual electric quantity data of each robot; transmitting the residual electric quantity data to an upper control system through a wireless communication unit;
the upper control system judges whether the information of the residual electric quantity of the robot is lower than a preset safety threshold value, if yes, a charging task instruction is dispatched; if not, dispatching a task for carrying the tray materials;
the upper control system carries out ant colony algorithm planning route through a scheduling unit according to the position information of the pallet materials to be carried, which corresponds to the task information, and the position information of each robot with the task information for carrying the pallet materials, and the robot carries out movement according to the planning route;
judging the actual positions of a pallet fork and pallet materials of the robot by a laser radar and video data fusion technology, carrying the pallet materials after adjusting a vehicle body according to the actual positions, and monitoring residual electric quantity information of the robot in real time by a monitoring unit;
the upper control system performs ant colony algorithm route planning through a scheduling unit according to the position information of the tray materials to be carried and the position information of each robot with the dispatching carrying tray materials, which correspond to the task information, and the robot performs moving according to the planned route;
the step of determining the path comprises the steps of respectively determining the paths of m robots through circularly calculating m times; for one robot, n-1 times are needed to be selected, and each time the robot selects, the point which has passed through cannot be used as the next point of the path; when determining the next point, calculating the selection probability of each point to be selected, and selecting by adopting a roulette method after determining the probability of each point to be selected; the probability of selecting a point to be selected can be determined by:
in the pheromone updating step, the amount of change in the pheromone caused by the difference in path length is:
2. The control system of an unmanned forklift robot according to claim 1, wherein the upper control system judges whether the information of the remaining capacity of the robot is lower than a preset safety threshold, if yes, the step of dispatching the charge task instruction includes judging whether the preset safety threshold is at a safety warning threshold, if yes, the robot is immediately shut down to enter a sleep state to wait for manual intervention and then control the robot to enter a safety area and recharge; if not, the robot moves to the charging area by itself according to the charging task instruction.
3. The control system of an unmanned forklift of claim 1, wherein said step of dispatching a task of handling pallet material comprises determining whether the robot residual capacity information is below a task alert threshold; if yes, a normal task with longer predicted conveying time is not sent to the robot; if not, all tasks are dispatched normally.
4. The control system of an unmanned forklift robot according to claim 1, wherein in the roulette method, the number of device selection points is X, the probability of selection is represented by an X-dimensional vector X, normalization processing is performed on X, that is, X (i) =x (i)/sum (X (i)), the sum of the i items before the vector is calculated respectively, as a new X (i), matlab can be implemented by x=cumsum (X), a random number r between 0 and 1 is finally generated, the first element greater than r in the vector X is found, and the candidate point represented by the first element is taken as the next point.
5. The control system of the unmanned forklift robot according to claim 1, wherein the steps of judging the actual positions of the pallet fork and the pallet material of the robot by a laser radar and video data fusion technology, carrying the pallet material after adjusting the vehicle body according to the actual positions, and monitoring the residual electric quantity information of the robot in real time by a monitoring unit comprise the steps of scanning the original data formed by the laser radar or a camera of the robot to the external environment, identifying the fork hole positions of the pallet and the pallet fork by adopting image processing and pattern recognition post-processing, and carrying the pallet material after adjusting the vehicle body according to the fork hole positions.
6. The control system of an unmanned forklift robot of claim 5, wherein the intelligent recognition process of the position of the pallet jack by the robot is as follows:
driving a network port of the laser radar through a local network and reading data of the port; forming a point cloud three-dimensional image or a plane image by utilizing a laser radar with point cloud output to statically scan or dynamically scan the side surface of a shelf tray up and down;
analyzing the three-dimensional map or the plan map of the point cloud for feature recognition, judging the position of a fork hole of a fork, finding out the center point and the center line of the tray, if the position of the fork hole cannot be analyzed, reporting errors, and prompting failure of feature recognition; if the scan is successful, a side view of the tray is formed;
a coordinate system is established by taking a laser radar on the forklift as a reference, a measurement reference of the forklift and the pallet is formed, and a top view of the forklift system is established; measuring the position coordinate XY of the central point of the front tray in a coordinate system, and forming an angle A between the central line of the tray and the central line of the forklift according to the relation between the central line of the tray and the coordinate system; wherein the angle a and XY coordinates are signed numbers with signs;
and outputting X, Y, A three parameters to a local or background computing unit according to an interface protocol, and adjusting the position and the posture of the forklift by the three parameters to ensure that the picking operation of the forklift is started after the forklift is at the correct position.
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