CN112180934B - Control method, system and device of loading robot and readable storage medium - Google Patents
Control method, system and device of loading robot and readable storage medium Download PDFInfo
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- G—PHYSICS
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0238—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors
- G05D1/024—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors in combination with a laser
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- G—PHYSICS
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
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- G—PHYSICS
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
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- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
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- G—PHYSICS
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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Abstract
The invention provides a control method, a control system, a control device and a readable storage medium of a loading robot, wherein the control method comprises the steps of acquiring cargo information and container information; generating a first placement scheme according to cargo information and container information; determining the space utilization rate of the first placement schemes, and selecting a scheme with the maximum space utilization rate from a plurality of first placement schemes as a second placement scheme; and planning a path according to the second placement scheme, and controlling the robot to load cargoes according to the planned path. The method not only improves the cargo transportation efficiency of the container, but also greatly improves the working efficiency of the loading robot, reduces repeated carrying, saves a great amount of labor cost and has high reliability; the carrying path of the loading robot is planned according to the optimal placement scheme, the shortest path and the optimal time are taken as optimization targets, the collision is avoided, meanwhile, the cargo loading efficiency is further improved, and the loading robot can be widely applied to the technical field of material distribution.
Description
Technical Field
The invention belongs to the technical field of material distribution, and particularly relates to a control method, a control system, a control device and a readable storage medium of a loading robot.
Background
In recent years, the logistics industry develops very rapidly, and cargo distribution is an important link which is in line with the development of the logistics industry, so that improvement of various working indexes of the distribution industry plays a vital role in prospecting the logistics industry. Cargo loading is an unavoidable task when cargo is delivered, however, this task is currently commonly performed in a manual handling or forklift-assisted manual manner, which is poor in working environment, high in strength and low in efficiency. Therefore, the automation of cargo loading has important significance.
Along with the rapid development of the world economy and the mass circulation of commodity goods, the importance of the logistics transportation field is increasingly outstanding, and the logistics transportation field is an artery and basic industry for national economy development, and is a 'accelerator' for promoting the economy development, and meanwhile, the development, optimization and application of related technologies of the industry are increasingly valued. The realization of automated loading and reasonable boxing planning algorithm is a key in the field of logistics distribution. In the boxing algorithm, the problem of boxing is taken as a classical problem of difficult combination, and can be generally divided into the following steps according to the space dimension: the one-dimensional, two-dimensional and three-dimensional boxing problem is more complex than the one-dimensional and two-dimensional boxing problem. However, the existing three-dimensional boxing algorithm has certain defects or limitations, for example, a related solving or searching strategy is established for a solving target, only one feasible solution can be obtained, and the effect is unstable; or only consider the obstacle avoidance on the robot moving path, and neglect the planning of the optimal path.
Disclosure of Invention
In view of the above, in order to partially solve one of the above-mentioned technical problems, an object of an embodiment of the present invention is to provide a control method of a loading robot capable of reliably providing an optimal movement path; meanwhile, the embodiment of the invention also provides a system, a device and a computer readable storage medium which can realize the corresponding method.
In a first aspect, an embodiment of the present invention provides a control method of a loading robot, including the steps of:
acquiring cargo information and container information;
generating a first placement scheme according to cargo information and container information;
determining the space utilization rate of the first placement schemes, and selecting a scheme with the maximum space utilization rate from a plurality of first placement schemes as a second placement scheme;
and planning a path according to the second placement scheme, and controlling the robot to load cargoes according to the planned path.
It will be appreciated that in some embodiments, the step of determining the space utilization of the first placement scheme and determining the second placement scheme according to the maximum value of the space utilization specifically includes:
generating a first constraint condition according to the container information;
determining that the goods placement positions in the first placement scheme meet a first constraint condition, and obtaining an initial population according to the first placement scheme;
Obtaining a plurality of second populations through simulated annealing inheritance according to the initial populations;
and obtaining a population with the maximum space utilization rate in a plurality of second populations as a second placement scheme.
It may be appreciated that in some embodiments, the step of determining the space utilization of the first placement scheme, selecting, as the second placement scheme, a scheme with a maximum space utilization value from the plurality of first placement schemes, specifically includes:
generating an initial space according to container information;
coding the placing position of the goods according to the goods information to obtain coding information; the encoded information includes: container location, start coordinate point, and end coordinate point;
determining that the coding information meets a first constraint condition, and dividing an initial space to obtain a first space and a second space;
space utilization is generated from the first space and the second space.
It will be appreciated that in some embodiments, the step of generating the space utilization from the first space and the second space specifically includes:
determining that a first constraint condition is met in the termination coordinate point, and dividing the cargo position of the first space;
determining that the residual space of the first space is smaller than the difference value of the initial coordinate point and the final coordinate point, and dividing the cargo position of the second space;
And determining that the residual space of the second space is smaller than the difference value of the initial coordinate point and the final coordinate point, and generating space utilization rate according to the divided cargo position and the initial space.
It may be appreciated that in some embodiments, the path planning is performed according to the second placement scheme, and the step of controlling the robot to load the cargo according to the planned path specifically includes:
determining a target point in an initial space; determining a candidate point according to the target point;
determining the candidate points as feasible points according to the first constraint and the second constraint; the second constraint comprises a boxing constraint, a cargo loading feasible point constraint, a height constraint and a boundary constraint;
determining a second target point from the plurality of feasible points; and planning a path according to the target point and the second target.
It will be appreciated that the step of determining the second target point from a number of possible points specifically comprises:
determining a first path of a feasible point and a target point, and determining the pheromone quantity concentration of the first path;
determining a shortest path in the first path, and enhancing the pheromone quantity concentration of the shortest path; and determining a second target point from a plurality of feasible points according to the enhanced pheromone quantity concentration.
In a second aspect, the technical solution of the present invention further provides a software system for controlling a loading robot, including an information acquisition unit, an initialization unit, a space optimization unit, and a path planning unit, where:
The information acquisition unit is used for acquiring cargo information and container information;
the initialization unit is used for generating a first placement scheme according to cargo information and container information;
the space optimization unit is used for determining the space utilization rate of the first placement schemes, and selecting a scheme with the maximum space utilization rate from a plurality of first placement schemes as a second placement scheme;
and the path planning unit is used for planning a path according to the second placement scheme and controlling the robot to load goods according to the planned path.
In a third aspect, the present invention further provides a control device for a loading robot, including:
at least one processor;
at least one memory for storing at least one program;
the at least one program, when executed by the at least one processor, causes the at least one processor to implement the method of controlling the loading robot in the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium in which a processor-executable program is stored, which when executed by a processor is adapted to carry out the method as in the first aspect.
Advantages and benefits of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention:
According to the technical scheme, the optimal placement scheme of the cargoes is obtained by planning the maximum space utilization rate and screening, so that the cargo transportation efficiency of the container is improved, the working efficiency of the loading robot is greatly improved, the carrying of repeated machinery is reduced, a large amount of labor cost is saved, and the reliability is high; according to the optimal placement scheme, the carrying path of the loading robot is planned, the shortest path and the optimal time are used as optimization targets, and the efficiency of cargo loading is further improved while collision is avoided.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a front view of a loading robot in an embodiment of the present application;
FIG. 2 is a top view of a loading robot according to an embodiment of the present application;
FIG. 3 is a schematic view of the structure of a loading robot end clamp assembly according to an embodiment of the present application;
FIG. 4 is a flow chart of steps of a method for controlling a loading robot according to an embodiment of the present invention;
FIG. 5 is a flowchart of an embodiment of the present invention employing a simulated annealing genetic algorithm to obtain an optimal cargo placement scheme;
FIG. 6 is a schematic illustration of an embodiment of the present invention for placing a piece of cargo in an initial space;
FIG. 7 is a schematic illustration of two items of merchandise placed in an initial space according to an embodiment of the present invention;
FIG. 8 is a schematic view of another embodiment of the present invention for placing two items of merchandise in an initial space;
FIG. 9 is a schematic illustration of three items of cargo placed in an initial space according to an embodiment of the present invention;
FIG. 10 is a schematic illustration of another embodiment of the present invention for placing three items in an initial space;
fig. 11 is a flowchart of a step of obtaining an optimal planned path by an ant colony algorithm according to an embodiment of the present invention;
fig. 12 is a schematic view of a palletizing small circulation path of the loading robot in the present embodiment;
fig. 13 is a graph showing comparison of the optimal path trajectory and the average path curve generated by the ant colony algorithm before modification in the present embodiment;
fig. 14 is a graph showing comparison of the optimal path trajectory and the average path curve generated by the adaptive ant colony algorithm according to the present embodiment.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention. The step numbers in the following embodiments are set for convenience of illustration only, and the order between the steps is not limited in any way, and the execution order of the steps in the embodiments may be adaptively adjusted according to the understanding of those skilled in the art.
In the first aspect, in order to more clearly describe the technical solution of the present application, a loading robot for completing loading of goods in the present embodiment will be described in detail first. As shown in fig. 1 and 2, in this embodiment, the body portion of the loading robot is responsible for positioning the end clamps, and includes a full stack input roller way 100, an empty deck output roller way 101, a full stack unstacking component 102, a full stack lifting component 103, a box separating and conveying component 104, a large arm support 105, a basic arm 106, a base 107, an amplitude variation support 108, a telescopic arm 109, and a stacking tool component 110. As shown in fig. 3, the clamp part is responsible for goods placement and pushing, and can push out the boxes with an air cylinder once after the boxes are fully paved; the tail end clamp can be adjusted within a certain range according to the width of the container, so that the container can be transported in a row efficiently, the working efficiency is greatly improved, and a large amount of labor cost is saved. The automatic loading function is realized through the accurate matching of the body part and the clamp.
The working principle of the loading robot is distinguished according to the main body part and the clamp, and the working principle of the main body part is described first, wherein the direction X is parallel to the length of a carriage, Y is perpendicular to X, and Z is perpendicular to the bottom of the carriage:
(1) When the carriage is positioned, the rear end is driven to horizontally move so that the mechanical arm is parallel to the carriage, then two horizontal movements are driven simultaneously so that the mechanical arm is positioned in the middle of the carriage, horizontal information comes from a distance measuring sensor on the pitching amplitude variation mechanism, and the acquisition of the middle position information comes from laser side distance sensors at two ends of the clamp. In addition to the loading cart front auxiliary positioning function, the main body part is mainly responsible for adjusting the position of the loading clamp in the Y direction in the carriage, which depends on the functional design of the end clamp and the specific stacking plan.
(2) The telescopic arm consists of a fixed arm, a first extending arm and two extending arms, and the position of the loading clamp in the X direction of the carriage is adjusted by the forward and backward telescopic movement of the telescopic arm through the transmission of an internal helical rack.
(3) The amplitude-variable support is positioned at the tail end of the fixed arm, the frequency converter drives the motor to drive the self-locking screw rod to rotate, and the rotary motion is converted into linear motion so as to drive the mechanical arm to amplitude up and down, and the position of the loading clamp in the Z direction in the carriage is adjusted.
(4) The telescopic belt structure is embedded in the mechanical arm in a multi-layer mode, the belt driving roller is located at the front ends of the two extending arms, the driving roller rotates to drive the belt to operate, and goods are conveyed to the loading clamp through the operation of the belt.
The following is a description of the principle of the jig of the loading robot in the embodiment:
(5) The clamp horizontal rotating mechanism keeps the loading clamp and the bottom of the carriage horizontal all the time.
(6) The stacking tool part orderly arranges cargoes conveyed by the belt on the clamp.
(7) The width of the clamp is adjusted according to requirements by the telescopic joint structure in the Y direction of the clamp.
(8) The goods pushing mechanism and the tray are matched with each other to finish goods stacking from the clamp to the carriage.
Along with the width change of the clamp, the stacking mode can also be changeable, for example, single multi-cargo stacking, single cargo stacking and the like, and depends on the stacking layout mode in the carriage.
In a second aspect, as shown in fig. 4, an embodiment of the present invention provides a control method for the loading robot in the first aspect, which mainly includes steps S01-S04:
and S01, acquiring cargo information and container information. Specifically, importing basic data of the container and the cargoes, including but not limited to the number of kinds of cargoes, the total number of the cargoes, the total weight of the cargoes and the length, width and height of single cargoes; and the number of the container, the length, width and height of the loadable space of the container, etc.
S02, generating a first placement scheme according to cargo information and container information. The first placement scheme is a placement scheme which is randomly generated according to information (particularly size information) of goods and size information of a container. Due to the characteristic of row-wise transportation of the loading robots in the embodiment, when the whole row of cargoes are the same cargoes, the three-dimensional problem can be simplified into two-dimensional problem. Even in a two-dimensional plane, the loading robot loads goods in a different order, and the track of the loading robot is quite different from the path taken. For a single cargo, the single cargo is a strong heterogeneous box or a weak heterogeneous box, and the boxes in the same row are of the same type and the same direction; for cuboid goods, the goods have six faces, and only three faces are considered because six faces are symmetrical. In the embodiment, through adopting intelligent algorithm optimization, different arrangement combinations are obtained on a two-dimensional plane for single goods or heterogeneous boxes according to the special constraint of a robot of the single goods or heterogeneous boxes, and the arrangement combinations are used as a placement scheme. Specifically, in this embodiment, as shown in fig. 5, the simulated annealing genetic algorithm is used to obtain the optimal cargo placement scheme. Firstly, constructing a database of a cargo box body and a container according to the cargo information and the container information imported in the step S01 so as to facilitate subsequent read-write operation. Secondly, setting parameters simulating annealing inheritance, for example, selecting the length, width and height and bearing capacity of cargoes, and generating an initial population according to a random function, wherein the individuals (arrangement and combination mode) of the initial population are the first placement scheme. And obtaining population individuals with highest fitness through cross mutation and simulated annealing operation later to be used as an optimal solution of a placement scheme.
S03, determining the space utilization rate of the first placement schemes, and selecting a scheme with the maximum space utilization rate from the plurality of first placement schemes as a second placement scheme. Specifically, the first placement scheme comprises a plurality of different placement modes, namely the arrangement and combination of cargoes, and the space utilization rate of different arrangement and combination is also different; for example: the same cuboid goods can obtain the maximum plane utilization rate through different emission combinations of three surfaces; the heterogeneous cuboid cargoes are arranged and combined with different planes through different cargoes, so that the maximum plane utilization rate is obtained. In this embodiment, the fitness in the simulated annealing genetic algorithm is set as the space utilization, and the optimal solution is the goods placement mode with the highest space utilization, which is denoted as the second placement scheme. In the step S03 of obtaining the second placement scheme, the space utilization rate of the placement scheme (the first placement scheme or the second placement scheme) is calculated, which may be further subdivided into steps S031-S034, wherein:
s031, generating an initial space according to container information. Specifically, an initial space in which goods can be stacked is determined according to size information of the container.
S032, coding the placement position of the goods according to the goods information to obtain coding information; the encoded information includes: container location, start coordinate point, and end coordinate point. Encoding the position space occupied by the goods to be put:
restspace＝[numbox,x a ，y a ，x b ，y b ]
Wherein, number is the number of the container, (x) a ，y a ) A starting coordinate point for goods placement, (x) b ，y b ) A termination coordinate point for the placement of the cargo. For example: in the encoding of goods, (x) a ，y a ) Representing the two-dimensional coordinates of this space where the reference point can be placed, i.e. the lower left vertex coordinates of the space. When the two-dimensional coordinates of the placeable reference point are the origin, the coordinates of the space are (x b ，y b ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein x is b ＝L，y b =w; when the two-dimensional coordinates of the placeable reference point are not the origin, the length of the space is expressed as: long, l=max (x b -x a ，y b -y a ) The method comprises the steps of carrying out a first treatment on the surface of the Width: min (x) b -x a ，y b -y a )。
S033, determining that the coding information meets a first constraint condition, and dividing an initial space to obtain a first space and a second space. Specifically, the first constraint condition is that the size of the goods to be placed into the container and the maximum bearing capacity of the container are judged, and the length and the width of the goods on a two-dimensional plane are smaller than those of the residual space on the two-dimensional plane of the container; the sum of the weights of the goods is less than the maximum load capacity of the container. When the goods meet the loading requirements, calculating the residual space of the initial space according to the goods codes to be placed. The remaining space division rule generates a first and a second subspace, in the embodiment, a left space and an upper space, respectively, wherein the length of the left space is the ending coordinate x of the container door to the goods b Is a distance of (2); the upper space is higher than the top to end coordinate y of the container b Is a distance of (3). The constraint that the robot must load from inside to outside is met, the cargo is ensured to load from bottom to top, and the coded data of the cargo are sequentially added until the cargo sequence set is empty; when the remaining space of the container cannot meet the cargo loading, the next container is jumped to be loaded.
For example, the container initial available space is expressed as:
restspace＝[numbox,0,0,L,W]
when an article is placed in the space, the length of the article is a, the width of the article is b, two new upper spaces and a left space are generated, wherein the upper spaces are as follows: retspace= [ number, 0, b, l=a, w=w-b ]; the left space is: retspace= [ number, a,0, l=l-a, w=b ]. When the goods are added continuously, the left space and the upper space are updated continuously until the container is filled or the goods to be loaded are not contained. As shown in fig. 6, reference numeral 1 is a simulation of a first load, which is smaller than the first constraint of a container being filled, a left space and a top space are created.
When other cargoes are subsequently simulated to be loaded into the container, as shown in fig. 7, if the width of the current cargoes is the same as that of the front cargoes, the upper spaces of the two cargoes are combined; as shown in fig. 8, the width of the current cargo is different from that of the front cargo, so that the two cargoes have respective upper spaces; as shown in fig. 9, if the length of the current cargo is the same as the length of the front cargo, the left space of the current cargo is taken as the left space of the whole cargo; as shown in fig. 10, if the length of the current cargo is different from the length of the front cargo, the left space of the current cargo and the left space of the front cargo are combined to form an integral left space.
S034, generating space utilization rate according to the first space and the second space. Specifically, the space utilization rate is calculated from the remaining space (including the left space and the upper space) in the loaded container and the initial space of the container.
In some embodiments, during the loading of the left and upper spaces, the simulated placing process of the post-cargo includes steps S0331-S0333:
and S0331, determining that the first constraint condition is met in the termination coordinate point, and dividing the cargo position of the first space.
S0332, determining that the residual space of the first space is smaller than the difference value of the initial coordinate point and the end coordinate point, and dividing the cargo position of the second space.
S0333, determining that the residual space of the second space is smaller than the difference value of the initial coordinate point and the final coordinate point, and generating space utilization rate according to the divided cargo position and the initial space.
Specifically, the left space is preferably filled, and the width of the post-cargo should be smaller than that of the pre-cargo, so as to avoid the space waste caused by shielding when the post-cargo is higher than the pre-cargo in the real container space.
S04, carrying out path planning according to the second placement scheme, and controlling the robot to carry out cargo loading according to the planned path. After determining the second placement scheme, i.e. the packing sequence and the cargo position, in step S03, a path planning is required for the packing robot. In the actual boxing process of the robot, an efficient and practical algorithm is needed to carry out path planning on the tail end clamp of the robot, so that the length and time of a moving path can be reduced as much as possible while the actual boxing constraint is met, and the working efficiency of the robot is greatly improved. In this embodiment, the set objective function of the binning shortest path is:
Wherein D is the shortest distance between the target points, N is all the target points, n=g×s, g container length/cargo length, s container height/cargo height (g, s are integers for taking down and rounding). i is the target point of the load, i=1, 2,3. X and Y are the X and Y axis coordinates of the target point, respectively. As shown in fig. 11, the embodiment solves the objective function through the ant colony algorithm to generate an optimal path of the loading robot, and the process includes steps S041-S043:
s041, determining a target point in an initial space; determining a candidate point according to the target point;
s042, determining candidate points as feasible points according to the first constraint and the second constraint; the second constraint comprises a boxing constraint, a cargo loading feasible point constraint, a height constraint and a boundary constraint;
s043, determining a second target point from a plurality of feasible points; and planning a path according to the target point and the second target.
Specifically, the ant colony algorithm is inspired intelligent behavior by ant colony foraging in the nature, and ants can gradually regularize the ant colony behavior by releasing pheromone and transmitting information, so that an optimal path can be found. The set of feasible schemes for solving the problem is represented by the set of paths traversed by ants, and the ant optimizing process gradually gathers on the optimal path, namely the optimal path scheme under the action of positive feedback. In this embodiment, first, the first article of goods in the goods placement sequence according to the optimal placement scheme obtained in step S03 is used as the initial target point, and then the goods to be loaded are located as the candidate points. When the loading robot is controlled to finish goods placement of the placement target point, two candidate points are newly added according to the target point, a feasible table is built at the same time, and the reached target point is deleted from the feasible table. Judging whether the candidate point is a feasible point according to the first constraint condition and the second constraint condition, adding the feasible point into a feasible table, selecting a new target point from the feasible table, controlling the loading robot to reach the new target point, and loading cargoes until the feasible table is empty. The first constraint condition is size information of the container and a loading value of the container; the second constraint then includes a bin packing constraint, a cargo loading feasible point constraint, a height constraint, and a boundary constraint.
The boxing constraint is that the boxing sequence must be from bottom to top to inside to outside, for example, cargoes cannot be high in front and low in back, so that a robot cannot enter a shielded space; the bottom layer goods are not loaded, and the upper layer goods cannot be loaded in a high way. According to loading constraint, each new cargo row is loaded, one or two new feasible points are generated when one feasible point is reached, two candidate points are required to be added upwards and outwards when one feasible point is reached, and then whether the two candidate points can be the feasible points is judged, so that the cargo loading is related to each other between the feasible points.
Feasible point constraint: at X i In the coordinates, X i ＝min{X w -a }; in Y i In the coordinates, Y i ＝min{Y w Allowances = { (X) i ,Y i ) Where w is the target point that has not yet been loaded with cargo and allowances is the set of feasible points.
Height constraint: defining the height difference of two adjacent rows of goods not exceeding the height of four goods, Y i+3 -Y i <4h, wherein h is cargo height; if the difference in height between the two columns is too large, this will risk collapsing of the box.
Boundary constraints-the end effector of the robot cannot exceed the maximum width of the container, otherwise collisions may occur. The boundary constraint of the two-dimensional space of the loading robot is 0-X i L is more than or equal to 0 and Y is more than or equal to 0 i H is less than or equal to H; where L is the container length and H is the container height.
In some embodiments, the second constraint may further include a weight constraint and a load bearing constraint, the weight constraint defining an amount by which the weight of the row of cargo cannot exceed a significant load bearing of the loading robot; the load-bearing constraint defines that the load placed on the underlying load cannot exceed the load-bearing range of the underlying.
In the process of determining a new target point from a plurality of feasible points according to the embodiment, the method may specifically include steps S0431-S0432:
s0431, determining a first path of a feasible point and a target point, and determining the pheromone quantity of the first path;
s0432, determining the shortest path in the first path, and enhancing the pheromone quantity of the shortest path; and determining a second target point from a plurality of feasible points according to the enhanced pheromone quantity.
Specifically, c= { C1, C2, C3, the.. } is a set of n target points, l= { L ij C is the set of element pairwise connections in set C, d ij Is the distance between the target points i and j, usingThe state transition probability of ant k from node i to node j is represented at time t, and there are:
wherein τ ij (t) is the amount of pheromone, allowances on the path (i, j) at time t k Allowing selection for ant k next Node, alpha is the pheromone factor of ant, beta is heuristic factor, and represents the influence degree of the next node on the current node, if the beta value is bigger, the following isThe closer to greedy rule, η ij (t) is a heuristic function, the expression of which is:
ants can continuously release pheromone in the whole cycle, meanwhile, the prior ants can also continuously disappear, and a parameter rho (0 < rho < 1) can be set to represent the volatilization degree of the pheromone. After the circulation is completed, the circulation needs to be updated in real time, and the updating mode is as follows:
the parameter ρ is defined as the pheromone volatilization coefficient, and the value is 0 to 1. (1- ρ) is the pheromone residual factor,for the concentration of pheromone released by ant k at two nodes, deltaτ ij Is the sum of the pheromone concentration released by ants on two nodes.
In summary, the number of ants is gradually increased within the preset cycle times, the state transition probability is calculated according to the target points, the feasible points are obtained through screening, the feasible point table is modified according to the feasible points, the residual pheromone of each ant crawling path is updated, the path with the highest probability is calculated according to the updated pheromone amount through the probability calculation formula of the state transition, namely, the path is used as the optimal planning path, and the new target point connected with the path is the second target point.
In the embodiment of the scheme, as the optimization in the ant colony algorithm influences the path planning of the robot according to the global pheromone, ants with shorter path distances in each iteration are selected as elite ants on the ant colony, and the pheromone concentration of the paths taken by the ants is added, so that the paths of the subsequent ant colony reach shorter distances faster, and the required iteration times are reduced. The improved residual pheromone meets the following formula:
wherein:
the method can effectively accelerate the iteration speed and find a better path by utilizing the reinforcement of the coefficients of ants found at a shorter distance and reducing the pheromone concentration of a worse path.
In addition, in an embodiment, in order to avoid premature trapping in local optimization and stopping the algorithm, the algorithm only updates the pheromone on the path of the algorithm after completing one round trip only by the best route, and the value of the pheromone is defined in a range.
The calculation method of the pheromone comprises the following steps:
pheromones on the division bar road are limited to [ tau ] min ，τ max ]In the interval, the concentration of a certain pheromone track is avoided from being larger than the range of the rest paths, and the pheromone track is limited to [ tau ] according to the increment direction min ，τ max ]The interval can avoid that the information on a certain path is possibly repeatedly strengthened due to the traversal of the subsequent ants, so that the information quantity of the path is far greater than that of other paths, the possibility of early stagnation is effectively reduced, and the problem of trapping local minima is further solved.
The conventional ant colony algorithm pheromone volatilization factors are constant, but the requirement based on path optimization shows that the dependence of ants on the pheromone is higher in the initial stage, and the algorithm can reduce the required iteration times when the initial volatilization factors are smaller, so the initial volatilization factors are relatively smaller. In the middle stage, the ant colony searches for the relatively better path pheromone volatilization factor, if the ant colony is too small, the algorithm is easy to fall into a local optimal solution, global searching is not facilitated, so that the volatilization factor needs to be increased to increase the randomness of the algorithm, and meanwhile, the ant colony needs to be gradually reduced in the later stage so as to facilitate the pheromone to be gathered in the better path. Based on the above requirements, the pheromone volatilization factor in the method can be modified into normal distribution:
for example, when δ=2, μ=0.5, the volatilization factor ρ for the x coordinate pair of the current iteration number is taken with the change of iteration, so that the change of the volatilization factor is similar to the normal distribution.
After the optimal path planning is obtained according to the self-adaptive ant colony algorithm constructed in the step S04, the method of the embodiment can also control the loading robot to carry out cargo loading according to the optimal path, the sequence of reaching the target points is sequentially reached according to the sequence obtained by the algorithm, but the actual robot track cannot go straight, otherwise collision exists. In the embodiment, the motion state of the robot is collected through the sensor, and the Path planning method is improved through the robot kinematics, so that a palletizing small cycle of the robot is generated, and as shown in fig. 12, the palletizing small cycle can comprise three paths, namely a Path1 which is contracted back to a safe position, a Path2 which is variable in amplitude up and down by the supporting arm and a Path3 which is extended to a locating point. The amplitude-changing stage of the support arm comprises horizontal rotation of a clamp, and the clamp needs to be parallel to the horizontal plane. Path1 indicates that when anchor clamps accomplish a pile up neatly and fix a position to next pile up neatly point, in order to prevent anchor clamps to become a scratch goods, the arm needs shrink one section safe position. Path2 refers to adjusting the support amplitude to the corresponding amplitude of the next palletizing point. Path3 is the last step of stacking and positioning of the mechanical arm, and the mechanical arm is moved to a preset absolute position by means of the two extending arms, and after positioning is completed, the next identical positioning cycle is continued after receiving a goods stacking completion instruction. Path1 to Path3 are small stacking cycles, and an integral loading cycle is formed according to the total stacking Path.
And finally, according to the self-adaptive ant colony algorithm constructed in the step S04, generating an optimal loading running path, namely a shortest path, of the loading robot, and according to the shortest path, completing the goods placement scheme with the highest space utilization rate generated in the step S03 by the loading robot.
The simulation results of the present embodiment will be described in detail below:
taking the international common container size as an example, experiments are carried out on MATLAB simulation software according to the flow and the constraint in the embodiment, and initial parameters are set: the number of ants is 38, the initial parameters are original ant colony standard parameters, ρ=0.3, simulation experiments are carried out in the environment model 1, and the average path curve and the optimal path track of the experiments are compared, as shown in fig. 13. It can be seen from fig. 13 that if the robot is allowed to randomly encase only with normal encasement constraints, the average distance in the ant colony algorithm fluctuates around 24000cm, whereas the shortest distance in the ant colony algorithm is only 19187cm. Each 1297 x 280 long and high-specification container is filled with 60 x 30 boxes, more than 20% of journey can be saved, and the actual boxing efficiency of the robot can be greatly improved.
In the embodiment, an ant colony algorithm is improved, elite strategy is introduced, the pheromone quantity concentration of the shortest path is enhanced, and meanwhile, the pheromone concentration of the poor path is reduced; introducing the maximum and minimum informativeness; and improving the pheromone volatilization factor to increase the randomness of the ant colony algorithm. Then simulation is carried out through MATLAB, and initial parameters are set as follows: the number of ants is 38, the initial parameters are the standard parameters of the original ant colony, and the environment is simulated for a certain number of times. Other conditions are the same as those of the environment model 1, the obtained comparison results of the average path curve and the optimal path track are shown in fig. 14, the comparison results have certain advantages for the iteration speed and the global optimization, and the improvement on the average distance and the shortest distance of each generation before and after the improvement is improved to a small extent, so that the efficiency of the loading palletizing robot can be improved better.
In a third aspect, an embodiment of the present invention further provides a software system for controlling a loading robot, including an information acquisition unit, an initialization unit, a space optimization unit, and a path planning unit, where:
the information acquisition unit is used for acquiring cargo information and container information;
the initialization unit is used for generating a first placement scheme according to cargo information and container information;
the space optimization unit is used for determining the space utilization rate of the first placement schemes, and selecting a scheme with the maximum space utilization rate from a plurality of first placement schemes as a second placement scheme;
and the path planning unit is used for planning a path according to the second placement scheme and controlling the robot to load goods according to the planned path.
In a fourth aspect, an embodiment of the present invention further provides a control device for a loading robot, including at least one processor; at least one memory for storing at least one program; the at least one program, when executed by the at least one processor, causes the at least one processor to implement the method of controlling a loading robot as in the first aspect.
The embodiment of the present invention also provides a storage medium having a program stored therein, the program being executed by a processor as in the second aspect.
From the above specific implementation process, it can be summarized that, compared with the prior art, the technical solution provided by the present invention has the following advantages or advantages:
1. the control method provided by the invention is combined with the industrial robot and applied to actual production, and has important practical significance in improving the efficiency of the robot, saving resources and innovation of the application of a boxing algorithm.
2. The control method provided by the invention has the advantages that a path planning is effectively and practically performed on the end effector of the robot, the length and time of a moving path are reduced as much as possible while the actual boxing constraint is met, and the working efficiency of the robot is greatly improved.
In some alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flowcharts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed, and in which sub-operations described as part of a larger operation are performed independently.
Furthermore, while the invention is described in the context of functional modules, it should be appreciated that, unless otherwise indicated, one or more of the functions and/or features may be integrated in a single physical device and/or software module or may be implemented in separate physical devices or software modules. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary to an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be apparent to those skilled in the art from consideration of their attributes, functions and internal relationships. Accordingly, one of ordinary skill in the art can implement the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative and are not intended to be limiting upon the scope of the invention, which is to be defined in the appended claims and their full scope of equivalents.
Wherein the functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain or store the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium may even be paper or other suitable medium upon which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the application, the scope of which is defined by the claims and their equivalents.
While the preferred embodiment of the present application has been described in detail, the present application is not limited to the above embodiments, and various equivalent modifications and substitutions can be made by those skilled in the art without departing from the spirit of the present application, and these equivalent modifications and substitutions are intended to be included in the scope of the present application as defined in the appended claims.
Claims (8)
1. The control method of the loading robot is characterized by comprising the following steps:
acquiring cargo information and container information;
generating a first placement scheme according to the cargo information and the container information;
determining the space utilization rate of the first placement scheme, and selecting a scheme with the maximum space utilization rate from a plurality of first placement schemes as a second placement scheme;
planning a path according to the second placement scheme, and controlling the robot to load cargoes according to the planned path;
The step of planning the path according to the second placement scheme and controlling the robot to load the goods according to the planned path specifically comprises the following steps:
determining a target point in an initial space, wherein the initial space is generated by container information, and determining a candidate point according to the target point;
determining the candidate points as feasible points according to a first constraint and a second constraint, wherein the first constraint is that the length and the width of the goods to be placed into the container on a two-dimensional plane are smaller than the sum of the length, the width and the weight of the goods in the residual space on the two-dimensional plane of the container is smaller than the maximum bearing capacity of the container, and the second constraint comprises a boxing constraint, a goods loading feasible point constraint, a height constraint and a boundary constraint;
determining a second target point from a plurality of the feasible points;
planning a path according to the target point and the second target point;
wherein the step of determining a second target point from a plurality of the feasible points specifically comprises:
determining a first path of the feasible point and the target point, and determining the pheromone quantity concentration of the first path;
determining a shortest path in the first path, and enhancing the pheromone quantity concentration of the shortest path;
Determining a second target point from a plurality of feasible points according to the enhanced pheromone quantity concentration;
wherein, the enhanced pheromone volume concentration satisfies the following formula:
wherein τ ij (t+1) represents the enhanced pheromone concentration, ρ is the pheromone volatilization coefficient, (1- ρ) is the pheromone residual factor, τ ij (t) represents the pheromone amount concentration between node i and node j at time t, Δτ ij For the sum of the pheromone amount concentrations released by ants on nodes i and j,the concentration of the pheromone quantity released by the ant k on the node i and the node j is given, and n is the number of the ants;
wherein Deltaτ ij *k ＝n * ·Q/L * k N satisfies:wherein Q is a pheromone constant, < >>Is the path distance ant k has taken in the current iteration.
2. The method according to claim 1, wherein the step of determining the space utilization rate of the first placement scheme, selecting a scheme of the maximum value of the space utilization rate from among the plurality of first placement schemes as the second placement scheme, comprises:
generating a first constraint condition according to the container information;
determining that the goods placement positions in the first placement scheme meet the first constraint condition, and obtaining an initial population according to the first placement scheme;
Obtaining a plurality of second populations through simulated annealing inheritance according to the initial populations;
and obtaining a plurality of populations with the maximum space utilization rate in the second population as a second placement scheme.
3. The method according to claim 2, wherein the step of obtaining a population having the maximum space utilization rate among the plurality of second populations as the second placement scheme comprises:
generating an initial space according to the container information;
coding the placing position of the goods according to the goods information to obtain coding information; the encoded information includes: container location, start coordinate point, and end coordinate point;
determining that the coding information meets the first constraint condition, and dividing the initial space to obtain a first space and a second space;
and generating the space utilization rate according to the first space and the second space.
4. A control method of a loading robot according to claim 3, wherein the step of generating the space utilization ratio from the first space and the second space specifically includes:
determining that the first constraint condition is met in the termination coordinate point, and dividing the cargo position of the first space;
Determining that the residual space of the first space is smaller than the difference value of the initial coordinate point and the end coordinate point, and dividing the cargo position of the second space;
and determining that the residual space of the second space is smaller than the difference value of the initial coordinate point and the final coordinate point, and generating the space utilization rate according to the divided cargo position and the initial space.
5. The method of claim 1, wherein the step of planning a path according to the second placement scheme and controlling the robot to load cargo according to the planned path further comprises:
constructing a palletizing cycle of the robot, wherein the palletizing cycle comprises a second path, a third path and a fourth path;
determining that the robot runs in the second path, and controlling a mechanical arm of the robot to shrink;
determining that the robot runs in the third path, and increasing or decreasing the upper and lower amplitude of the supporting arm of the robot;
and determining that the robot runs in the fourth path, and controlling a mechanical arm of the robot to extend to the target point or the second target point.
6. The control system of the loading robot is characterized by comprising
The information acquisition unit is used for acquiring cargo information and container information;
the initialization unit is used for generating a first placement scheme according to the cargo information and the container information;
the space optimization unit is used for determining the space utilization rate of the first placement schemes, and selecting a scheme with the maximum space utilization rate from a plurality of first placement schemes as a second placement scheme;
the path planning unit is used for planning a path according to the second placement scheme and controlling the robot to load goods according to the planned path;
the path planning is carried out according to the second placement scheme, and the cargo loading is carried out by the robot according to the planned path control specifically comprises the following steps:
determining a target point in an initial space, wherein the initial space is generated by container information, and determining a candidate point according to the target point;
determining the candidate points as feasible points according to a first constraint and a second constraint, wherein the first constraint is that the length and the width of the goods to be placed into the container on a two-dimensional plane are smaller than the sum of the length, the width and the weight of the goods in the residual space on the two-dimensional plane of the container is smaller than the maximum bearing capacity of the container, and the second constraint comprises a boxing constraint, a goods loading feasible point constraint, a height constraint and a boundary constraint;
Determining a second target point from a plurality of the feasible points;
planning a path according to the target point and the second target point;
wherein, the determining the second target point from the plurality of feasible points specifically comprises:
determining a first path of the feasible point and the target point, and determining the pheromone quantity concentration of the first path;
determining a shortest path in the first path, and enhancing the pheromone quantity concentration of the shortest path;
determining a second target point from a plurality of feasible points according to the enhanced pheromone quantity concentration;
wherein, the enhanced pheromone volume concentration satisfies the following formula:
wherein τ ij (t+1) represents the enhanced pheromone concentration, ρ is the pheromone volatilization coefficient, (1- ρ) is the pheromone residual factor, τ ij (t) represents the pheromone amount concentration between node i and node j at time t, Δτ ij For the sum of the pheromone amount concentrations released by ants on nodes i and j,the concentration of the pheromone quantity released by the ant k on the node i and the node j is given, and n is the number of the ants;
wherein Deltaτ ij *k ＝n * ·Q/L * k N satisfies:wherein Q is a pheromone constant, < >>Is the path distance ant k has taken in the current iteration.
7. Control device of loading robot, its characterized in that includes:
At least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one processor is caused to implement the control method of the loading robot as claimed in any one of claims 1 to 5.
8. A computer-readable storage medium having stored therein a program executable by a processor, characterized in that: the processor-executable program when executed by a processor is for implementing a control method of a loading robot according to any one of claims 1-5.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101381033A (en) * | 2008-10-15 | 2009-03-11 | 江苏科技大学 | Container loading method based on ant colony algorithm |
| CN103870893A (en) * | 2014-04-09 | 2014-06-18 | 沈阳工业大学 | Optimization method for solving encasement problem under multiple weight restrictions based on three-dimensional space |
| CN104680237A (en) * | 2015-03-10 | 2015-06-03 | 西南科技大学 | Three-dimensional encasement novel genetic algorithm model under multi-constrain condition |
| CN106403950A (en) * | 2016-06-24 | 2017-02-15 | 电子科技大学 | A robotic palletizer path planning method based on reference information |
| WO2017215044A1 (en) * | 2016-06-14 | 2017-12-21 | 广东技术师范学院 | Automatic path planning method for mobile robot and mobile robot |
| CN109095059A (en) * | 2018-07-06 | 2018-12-28 | 青岛智能产业技术研究院 | unmanned loading system and its loading method |
| CN109345017A (en) * | 2018-10-08 | 2019-02-15 | 南京航空航天大学 | A kind of shop material dispatching optimization method considering vanning constraint |
| CN110210685A (en) * | 2019-06-12 | 2019-09-06 | 广东三维家信息科技有限公司 | Logistics packing method and device |
| CN111080034A (en) * | 2019-12-31 | 2020-04-28 | 芜湖哈特机器人产业技术研究院有限公司 | Robot path planning method of intelligent loading system |
| CN111152214A (en) * | 2019-12-13 | 2020-05-15 | 珠海格力电器股份有限公司 | Four-degree-of-freedom palletizing robot, control system and palletizing path planning method |
-
2020
- 2020-10-09 CN CN202011071532.6A patent/CN112180934B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101381033A (en) * | 2008-10-15 | 2009-03-11 | 江苏科技大学 | Container loading method based on ant colony algorithm |
| CN103870893A (en) * | 2014-04-09 | 2014-06-18 | 沈阳工业大学 | Optimization method for solving encasement problem under multiple weight restrictions based on three-dimensional space |
| CN104680237A (en) * | 2015-03-10 | 2015-06-03 | 西南科技大学 | Three-dimensional encasement novel genetic algorithm model under multi-constrain condition |
| WO2017215044A1 (en) * | 2016-06-14 | 2017-12-21 | 广东技术师范学院 | Automatic path planning method for mobile robot and mobile robot |
| CN106403950A (en) * | 2016-06-24 | 2017-02-15 | 电子科技大学 | A robotic palletizer path planning method based on reference information |
| CN109095059A (en) * | 2018-07-06 | 2018-12-28 | 青岛智能产业技术研究院 | unmanned loading system and its loading method |
| CN109345017A (en) * | 2018-10-08 | 2019-02-15 | 南京航空航天大学 | A kind of shop material dispatching optimization method considering vanning constraint |
| CN110210685A (en) * | 2019-06-12 | 2019-09-06 | 广东三维家信息科技有限公司 | Logistics packing method and device |
| CN111152214A (en) * | 2019-12-13 | 2020-05-15 | 珠海格力电器股份有限公司 | Four-degree-of-freedom palletizing robot, control system and palletizing path planning method |
| CN111080034A (en) * | 2019-12-31 | 2020-04-28 | 芜湖哈特机器人产业技术研究院有限公司 | Robot path planning method of intelligent loading system |
Non-Patent Citations (1)
| Title |
|---|
| 《改进蚁群算法在港口集装箱装载中的应用》;张洪军 等;《齐 齐 哈 尔 大 学 学 报（自然科学版）》;第36卷(第1期);第34-38页 * |
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