CN113256035A - Machining path planning method, intelligent terminal and storage device - Google Patents
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Abstract
The invention provides a machining path planning method, an intelligent terminal and a storage device, wherein the machining path planning method comprises the following steps: s101: selecting nodes in the processing path according to an initialization strategy to construct a loop; s102: solving the problem of the traveling salesman based on the loop, and loading the current optimal solution according to the solving result, wherein the current optimal solution is the current optimal path; s103: modifying the node connection mode in the current optimal path to form a new loop; s104: judging whether the maximum iteration number is reached, if so, executing S105, and if not, executing S102; s105: and outputting the optimal solution of the processing path according to the current optimal solution. The invention can continuously try to solve the problem of the traveling salesman by modifying the path, is not easy to fall into the local optimal solution, has simple frame structure, reduces the calculation amount and the calculation capability requirement of equipment, saves the calculation cost, can further improve the accuracy of the search efficiency of the algorithm by different initialization strategies, and has strong expandability.
Description
Technical Field
The invention relates to the field of machining path planning, in particular to a machining path planning method, an intelligent terminal and a storage device.
Background
In CAM processing, generally, the whole processing object is partitioned, and then a tool path planning algorithm is used to plan the processing mode inside each partition. However, how to transfer between different processing partitions requires planning and calculation for the design of this path. Regarding the processing area as a node, regarding the transfer path between the processing areas as an edge, the processing area transfer path problem can be considered as how to find a shortest path to access all nodes, and all nodes are only accessed once, so the whole processing problem can be considered as a traveler problem (TSP).
The traveling salesman problem is a well-known problem in graph theory and is expressed as "given a complete graph of n points, each edge having a length, finding a closed loop that passes through each vertex exactly once and has the shortest total length". The solution space scale of the problem is very large, if there are N nodes, the combination of all paths has N | so that the calculation amount is large when path planning is performed, the requirement on the calculation energy capacity of the equipment is high, and the calculation cost is increased. Moreover, some heuristic solving algorithms such as ant colony algorithm, genetic algorithm and the like are very easy to fall into local optimal solution, so that the solving precision is low, and the path planning cannot be well carried out.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a processing path planning method, an intelligent terminal and a storage device, a node is selected according to an initialization strategy to construct a loop, after the loop is used for solving a TSP problem to obtain an optimal solution, a new loop is formed by modifying the path of the optimal solution, the current optimal solution is jumped out by utilizing the mode of solving the TSP problem by the new loop, the problem of a traveler is continuously tried to be solved by modifying the path, the problem is not easy to fall into a local optimal solution, the frame structure is simple, the calculation amount and the calculation capability requirement of equipment are reduced, the calculation cost is saved, the accuracy of the search efficiency of the algorithm can be further improved through different initialization strategies, and the expandability is strong.
In order to solve the above problems, the present invention adopts a technical solution as follows: a machining path planning method, comprising: s101: selecting nodes in the processing path according to an initialization strategy to construct a loop; s102: solving the problem of the traveling salesman based on the loop, and loading a current optimal solution according to a solving result, wherein the current optimal solution is a current optimal path; s103: modifying the node connection mode in the current optimal path to form a new loop; s104: judging whether the maximum iteration number is reached, if so, executing S105, and if not, executing S102; s105: and outputting the optimal solution of the processing path according to the current optimal solution.
Further, before the step of selecting a node in the machining path according to the initialization strategy to construct the loop, the method further includes: judging whether a transfer path between two nodes is asymmetric or not; if so, preprocessing the data of the nodes in the processing path; and if not, selecting the nodes in the processing path according to the initialization strategy to construct a loop.
Further, the step of preprocessing the data of the nodes in the machining path specifically includes: constructing an alternative distance matrix for nodes in the processing path
Wherein D is a distance matrix between nodes in the processing path,
and N is the number of nodes.
Further, the step of preprocessing the data of the nodes in the processing path further includes:
and adding an auxiliary node to each original node in the processing path, defining the original node and the auxiliary node of the original node as mutually fixed nodes, and acquiring the distance between the nodes in the processing path.
Further, the distance between each node in the processing path is obtained through a distance equation, wherein the distance equation is as follows:
wherein P is the set of all auxiliary nodes, and the auxiliary nodes are
Q is the set of all original nodes, the original Node is Nodei≤nAnd M is the maximum value of the distances among all the nodes.
Further, the step of solving the traveler problem based on the loop specifically includes:
s201: traversing nodes in the initial loop, searching a preset number of nodes and adjacent nodes of the nodes by taking the accessed nodes as starting points, and forming a preset number of connecting edges through the nodes and the adjacent nodes;
s202: exchanging the nodes of the connecting edge, constructing a new loop according to the connecting edge after the nodes are exchanged, judging whether a better solution is obtained or not according to the total distance of the new loop, if so, executing S203, and if not, executing S204; s203: determining the processing path corresponding to the new loop as the current optimal solution, and executing S201; s204: and judging whether the traversal is finished, if so, outputting the current optimal solution, and if not, executing S201.
Further, the step of loading the current optimal solution according to the solution result specifically includes: judging whether the solving result is superior to the current optimal solution or not; if yes, determining the solving result as the current optimal solution; if not, the current optimal solution is not modified.
Further, the step of modifying the node connection mode in the current optimal path to form a new loop specifically includes: and exchanging the intersection points in part or all of the connection edges in the current optimal path to form a new connection edge, and forming a new loop according to the new connection edge.
Based on the same inventive concept, the invention further provides an intelligent terminal, which comprises a processor and a memory, wherein the processor is in communication connection with the memory, the memory stores a computer program, and the processor executes the machining path planning method according to the computer program.
Based on the same inventive concept, the invention further provides a storage device, which stores program data, which are used to execute the machining path planning method described above.
Compared with the prior art, the invention has the beneficial effects that: the method comprises the steps of selecting nodes according to an initialization strategy to construct a loop, solving a TSP problem of the loop to obtain an optimal solution, modifying a path of the optimal solution to form a new loop, jumping out the current optimal solution by utilizing a new loop solving TSP problem mode, continuously trying to solve a traveler problem by modifying the path, not easily trapping in a local optimal solution, having a simple frame structure, reducing the calculation amount and the calculation capacity requirement of equipment, saving the calculation cost, further improving the accuracy of the search efficiency of the algorithm through different initialization strategies, and having strong expandability.
Drawings
FIG. 1 is a flow chart of a method for planning a machining path according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating solving an embodiment of the TSP in the processing path planning method according to the present invention;
FIG. 3 is a flowchart illustrating a process of another embodiment of a method for planning a machining path according to the present invention;
fig. 4 is a flowchart illustrating a TSP solving another embodiment of the processing path planning method according to the present invention;
FIG. 5 is a block diagram of an embodiment of an intelligent terminal according to the present invention;
FIG. 6 is a block diagram of a memory device according to an embodiment of the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and the detailed description, and it should be noted that any combination of the embodiments or technical features described below can be used to form a new embodiment without conflict.
Referring to fig. 1-4, fig. 1 is a flow chart illustrating a processing path planning method according to an embodiment of the present invention; fig. 2 is a flowchart illustrating solving an embodiment of the TSP in the processing path planning method according to the present invention; FIG. 3 is a flowchart illustrating a process of another embodiment of a method for planning a machining path according to the present invention; fig. 4 is a flowchart illustrating solving of the TSP in the processing path planning method according to another embodiment of the present invention. The machining path planning method of the present invention will be described in detail with reference to fig. 1 to 4.
In this embodiment, the device to which the machining path planning method is applied may be an intelligent terminal having computing capability and planning a machining path, such as a computer, a mobile phone, a machining tool control system, and the like.
In one embodiment, a machining path planning method includes:
s101: and selecting nodes in the machining path according to the initialization strategy to construct a loop.
In the machining path planning Problem, the transfer path between each two machining areas may be Asymmetric, that is, the distance from a to B is not equal to the distance from B to a, and this kind of Problem is called Asymmetric traveler Problem (Asymmetric Traveling Salesman project), and when solving this kind of Problem, it needs to be converted into traveler Problem processing.
Therefore, the step of selecting the nodes in the machining path according to the initialization strategy to construct the loop further comprises the following steps: judging whether a transfer path between two nodes is asymmetric or not; if so, preprocessing the data of the nodes in the processing path; and if not, selecting the nodes in the processing path according to the initialization strategy to construct a loop.
In this embodiment, the step of preprocessing the data of the processing path specifically includes: constructing an alternate distance matrix for a processing path
Wherein D is a distance matrix between nodes in the processing path,
and N is the number of nodes.
Because the alternative distance matrix is a 2N-scale square matrix, in order to realize the alternative distance matrix, an auxiliary node needs to be added for each node pair, and the number of the nodes is expanded from N to 2N.
Therefore, the step of preprocessing the data of the machining path further comprises: adding an auxiliary node to each original node in the machining path, defining the original node and the auxiliary node of the original node as mutually fixed nodes, and acquiring the distance between the nodes in the machining path.
In a particular embodiment of the present invention,
the extended path is Node1→Node1+n→Node2→Node2+n→...→Noden→Noden+n. After expanding the Node, define the Nodei，Nodei+nThe nodes are fixed mutually, and the subsequent TSP solving process cannot be damaged
The connection relationship between them.
In this embodiment, the distance between each node in the processing path is obtained through a distance equation, where the distance equation is:
wherein P is the set of all auxiliary nodes, and the auxiliary nodes are
Q is the set of all original nodes, the original Node is Nodei≤nAnd M is the maximum value of the distances among all the nodes.
Equation (1) represents the maximum value of the distance in the return path if both nodes are original nodes or extended nodes.
Equation (2) shows that if the numbers of two nodes just differ by N, the pair of nodes is a binding node, and the distance is returned to 0.
Equation (3) represents the distance between the return nodes if one between two nodes belongs to the original node and one belongs to the extended node.
In this embodiment, there are various initialization strategies for constructing loops, and the initialization strategy may be selected according to the path planning problem corresponding to the node of different processing paths, and different initialization strategies may produce different results for different types of problems. The method for selecting the initialization strategy according to the node of the machining path may be set according to user requirements and actual conditions, and is not limited herein.
In the present embodiment, the initialization policy includes any one of a greedy method, a nearest neighbor method, and a minimum spanning tree method.
Among them, greedy method: a node is randomly selected and then a node closest to the node is selected as a next hop node.
Nearest neighbor method: and randomly selecting a node, then selecting N neighbor nodes nearest to the node, and randomly selecting one as a next hop node, wherein N is a preset value.
Minimum spanning tree method: a minimum spanning tree is constructed, and then a loop is constructed according to the tree shape.
S102: solving the problem of the traveling salesman based on the loop, and loading the current optimal solution according to the solving result, wherein the current optimal solution is the current optimal path.
In this embodiment, the step of solving the traveler problem based on the loop specifically includes:
s201: and traversing the nodes in the initial loop, searching a preset number of nodes and adjacent nodes of the nodes by taking the accessed nodes as starting points, and forming a preset number of connecting edges through the nodes and the adjacent nodes.
In this embodiment, the preset number of numerical values is configured before the machining path planning, and the specific numerical values of the preset number are adjusted according to the precision of the machining path planning and the running time.
In this embodiment, the step of forming a preset number of connecting edges by the nodes and the adjacent nodes specifically includes: and connecting the nodes with the adjacent nodes of the nodes to form connecting edges.
In this embodiment, the preset number is K, K is 2, the processing path has N nodes, and the initial loop constructed according to the initialization strategy is
All nodes of the initial loop are traversed, and each node is used as a starting point of edge searching. Then with the node
As a starting point, K nodes and adjacent nodes thereof are searched in total. And connecting the searched node with the adjacent node of the node to form K groups of connecting edges.
In a specific embodiment, K is 2, and two nodes are searched by taking one node in the processing path as a starting point
Two groups of connecting edges can be obtained
。
S202: and exchanging nodes of the connection edge, constructing a new loop according to the connection edge after the nodes are exchanged, judging whether a better solution is obtained or not according to the total distance of the new loop, if so, executing S203, and if not, executing S204.
In this embodiment, the step of exchanging nodes of the connection edge and constructing a new loop according to the connection edge after exchanging the nodes specifically includes: and detaching part or all of the connecting edges in the loop, connecting nodes in different connecting edges to form a new connecting edge, and constructing a new loop comprising the new connecting edge.
In a particular embodiment, the connecting edge is
The connecting edge is broken and reconstructed to obtain a new connecting edge of
. To obtain this connection, the nodes between i +1 and j in the path may be flipped to obtain a new loop as Node1→Node2→...→Nodei-1→Nodej→...→Nodei→Nodej+1→...→Noden→Node1。
In this embodiment, the length of each connecting edge is calculated from the direction of the new loop, and the total distance of the processing path is obtained based on the length. And judging whether the total distance is smaller than the total distance of the current optimal solution acquired before, if so, determining to acquire a more optimal solution, and if not, determining not to acquire a more optimal solution.
S203: and determining the processing path corresponding to the new loop as the current optimal solution, and executing S201.
In this embodiment, after determining to obtain a better solution, the machining path of the current optimal solution is replaced with the machining path corresponding to the new loop. And re-traverse each node in the loop based on the current optimal solution in an attempt to obtain a more optimal path combination.
S204: and judging whether the traversal is finished, if so, outputting the current optimal solution, and if not, executing S201.
In this embodiment, after each node in the loop has been traversed, it is determined that no more optimal path combination is found, and the solution is ended once.
In this embodiment, the step of loading the current optimal solution according to the solution result specifically includes: judging whether the solving result is superior to the current optimal solution or not; if yes, determining a solving result as a current optimal solution; if not, the current optimal solution is not modified. And judging whether the total distance of the loop corresponding to the solving result is better than the current optimal solution, and if the total distance is larger than the total distance of the current optimal solution, determining that the total distance is better than the current optimal solution.
S103: and modifying the node connection mode in the current optimal path to form a new loop.
The step of modifying the node connection mode in the current optimal path to form a new loop specifically includes: and exchanging the intersection points in part or all of the connection edges in the current optimal path to form a new connection edge, and forming a new loop according to the new connection edge.
In this embodiment, the node connection mode in the current optimal path is modified through a kick operation.
In one specific embodiment, the kick operation uses double bridge swap to form a new loop. For example, a pair of sides in the current optimal path is A- > B, C- > D, and after the double-bridge transformation is performed, the connection relationship becomes A- > C, B- > D.
In another specific embodiment, the solution results of multiple TSP solutions are retained, and the solution results are crossed according to a genetic algorithm to obtain a descendant path, which is used as a loop of the next TSP solution.
S104: and judging whether the maximum iteration number is reached, if so, executing S105, and if not, executing S102.
And the iteration times are the times of TSP solution, whether the times reach the preset maximum iteration times or not is judged, and the TSP solution is stopped after the maximum iteration times are reached.
S105: and outputting the optimal solution of the processing path according to the current optimal solution.
And taking the current optimal solution as the optimal solution of the processing path, and outputting the optimal solution.
The method has high search accuracy, can obtain ideal results very quickly, and tests 64 data in all symmetrical data sets through the TSPLIB, wherein 28% of the data obtains the optimal solution, the error between 40% of the data and the optimal solution is within 1%, the error between 25% of the data is between 1% and 3%, and the error between the rest 7% of the data sets is more than 3%. The invention can simultaneously deal with TSP and ATSP problems and can switch by configuration at any time.
Please look at Table I, which is a comparison table for solving the problem of TSPLIB data set with Lin-Kernighanalgorithm.
Table one, the present invention and Lin-Kernighanalgorithm solve the problem of TSPLIB data set.
In the above table, cities are the number of nodes, times is the solution time, and Gap is the difference between the optimal solution, wherein the optimal solution is obtained by the HK-replay method.
It can be seen from the above table that the present invention can obtain a better result in a very short time, is more suitable for practical CAM applications, can balance accuracy and computational cost, and maximizes benefits. The invention provides the configuration parameter K, and the requirement on higher precision can be met by modifying the parameter. The calculation process of the invention is lighter, the whole realization and the resource consumption are very small, the occupation of the calculation resources is reduced while the stronger function is realized, and the reduction of the computer performance caused by solving can be avoided.
Has the advantages that: the processing path planning method selects nodes according to the initialization strategy to construct a loop, after solving the TSP problem of the loop to obtain the optimal solution, the path of the optimal solution is modified to form a new loop, the current optimal solution is jumped out by utilizing the mode of solving the TSP problem by utilizing the new loop, the problem of a traveler can be continuously tried to be solved by modifying the path, the traveler is not easy to fall into the local optimal solution, the frame structure is simple, the calculation amount and the calculation capacity requirement of equipment are reduced, the calculation cost is saved, the accuracy of the searching efficiency of the algorithm can be further improved through different initialization strategies, and the expandability is strong.
Based on the same inventive concept, the present invention further provides an intelligent terminal, please refer to fig. 5, fig. 5 is a structural diagram of an embodiment of the intelligent terminal of the present invention, and the intelligent terminal of the present invention is described with reference to fig. 5.
In this embodiment, the intelligent terminal includes a processor and a memory, the processor is in communication connection with the memory, the memory stores a computer program, and the processor executes the machining path planning method according to the computer program.
Has the advantages that: the intelligent terminal selects the nodes according to the initialization strategy to construct the loop, after solving the TSP problem of the loop to obtain the optimal solution, the path of the optimal solution is modified to form a new loop, the current optimal solution is jumped out by utilizing the mode of solving the TSP problem by the new loop, the traveler problem can be continuously tried to be solved by modifying the path, the local optimal solution is not easy to fall into, the frame structure is simple, the calculation amount and the calculation capability requirement of equipment are reduced, the calculation cost is saved, the accuracy of the search efficiency of the algorithm can be further improved through different initialization strategies, and the expandability is strong.
Based on the same inventive concept, the present invention further provides a memory device, please refer to fig. 6, and fig. 6 is a structural diagram of an embodiment of the memory device according to the present invention.
In the present embodiment, the storage device stores program data used to execute the machining path planning method according to the above-described embodiment.
In the several embodiments provided in the present application, it should be understood that the disclosed terminal, system and method can be implemented in other manners. For example, the above-described terminal embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for executing all or part of the steps of the method described in the embodiments of the present application through a computer device (which may be a personal computer, a server, or a network device). And the aforementioned storage medium includes: a U-disk, a mobile hard disk, a Read-Only Memory (ROM for Read-Only machining path planning method), a Random Access Memory (RAM for Random machining path planning method), a magnetic disk or an optical disk, and other media capable of storing program codes.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. A machining path planning method is characterized by comprising the following steps:
s101: selecting nodes in the processing path according to an initialization strategy to construct a loop;
s102: solving the problem of the traveling salesman based on the loop, and loading a current optimal solution according to a solving result, wherein the current optimal solution is a current optimal path;
s103: modifying the node connection mode in the current optimal path to form a new loop;
s104: judging whether the maximum iteration number is reached, if so, executing S105, and if not, executing S102;
s105: and outputting the optimal solution of the processing path according to the current optimal solution.
2. The method of machine path planning of claim 1 wherein said step of selecting a node in the machine path to construct a loop according to an initialization strategy further comprises:
judging whether a transfer path between two nodes is asymmetric or not;
if so, preprocessing the data of the nodes in the processing path;
and if not, selecting the nodes in the processing path according to the initialization strategy to construct a loop.
3. The method for planning a machining path according to claim 2, wherein the step of preprocessing the data of the nodes in the machining path specifically includes:
constructing an alternative distance matrix for nodes in the processing path
Wherein D is a distance matrix between nodes in the processing path,
n is the number of nodes, j belongs to N, and i, j is a positive integer.
4. A method for machine path planning as claimed in claim 3 wherein the step of preprocessing the data for the nodes in the machine path further comprises:
and adding an auxiliary node to each original node in the processing path, defining the original node and the auxiliary node of the original node as mutually fixed nodes, and acquiring the distance between the nodes in the processing path.
5. The processing path planning method according to claim 4, wherein the distance between the nodes in the processing path is obtained by a distance equation:
wherein P is the set of all auxiliary nodes, i and j are positive integers, i is more than or equal to 1 and less than or equal to 2n, j is more than or equal to 1 and less than or equal to 2n, n is the number of original nodes, and NodeiRepresenting the ith node in the processing path, D (i, j-n) is the distance between the ith node and the jth node, D (j, i-n) is the distance between the jth node and the ith node, ELSE represents that otherwise, the auxiliary node is
Q is the set of all the original nodes, the original nodes are
And M is the maximum value of the distances among all the nodes.
6. The method for machine path planning according to claim 1, wherein the step of solving the traveler problem based on the loop specifically comprises:
s201: traversing nodes in the initial loop, searching a preset number of nodes and adjacent nodes of the nodes by taking the accessed nodes as starting points, and forming a preset number of connecting edges through the nodes and the adjacent nodes;
s202: exchanging the nodes of the connecting edge, constructing a new loop according to the connecting edge after the nodes are exchanged, judging whether a better solution is obtained or not according to the total distance of the new loop, if so, executing S203, and if not, executing S204;
s203: determining the processing path corresponding to the new loop as the current optimal solution, and executing S201;
s204: and judging whether the traversal is finished, if so, outputting the current optimal solution, and if not, executing S201.
7. The method for planning a machining path according to claim 1, wherein the step of loading the current optimal solution according to the solution result specifically includes:
judging whether the solving result is superior to the current optimal solution or not;
if yes, determining the solving result as the current optimal solution;
if not, the current optimal solution is not modified.
8. The method for planning a processing path according to claim 1, wherein the step of modifying the node connection mode in the current optimal path to form a new loop specifically comprises:
and exchanging the intersection points in part or all of the connection edges in the current optimal path to form a new connection edge, and forming a new loop according to the new connection edge.
9. An intelligent terminal, characterized in that the intelligent terminal comprises a processor and a memory, the processor is connected with the memory in a communication way, the memory stores a computer program, and the processor executes the processing path planning method according to any one of claims 1-8 according to the computer program.
10. A storage device, characterized in that the storage device stores program data for performing a method of machining path planning according to any of claims 1-8.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106060886A (en) * | 2016-05-20 | 2016-10-26 | 河海大学常州校区 | Acoustic sensor network route protocol construction method based on asymmetric links |
| CN107932505A (en) * | 2017-11-15 | 2018-04-20 | 广东工业大学 | Optimal polishing task path planning method and device based on articulated arm robots |
| US20180164775A1 (en) * | 2016-12-14 | 2018-06-14 | The Boeing Company | Robotic Task System |
| CN110519721A (en) * | 2019-08-28 | 2019-11-29 | 中南大学 | The method for optimizing route of robot replacement failure sensor in a kind of WSRNs |
| CN110674978A (en) * | 2019-09-10 | 2020-01-10 | 大连理工大学 | Task allocation and path planning method for unmanned workshop transportation system |
-
2021
- 2021-07-12 CN CN202110784095.0A patent/CN113256035B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106060886A (en) * | 2016-05-20 | 2016-10-26 | 河海大学常州校区 | Acoustic sensor network route protocol construction method based on asymmetric links |
| US20180164775A1 (en) * | 2016-12-14 | 2018-06-14 | The Boeing Company | Robotic Task System |
| CN107932505A (en) * | 2017-11-15 | 2018-04-20 | 广东工业大学 | Optimal polishing task path planning method and device based on articulated arm robots |
| CN110519721A (en) * | 2019-08-28 | 2019-11-29 | 中南大学 | The method for optimizing route of robot replacement failure sensor in a kind of WSRNs |
| CN110674978A (en) * | 2019-09-10 | 2020-01-10 | 大连理工大学 | Task allocation and path planning method for unmanned workshop transportation system |
Non-Patent Citations (1)
| Title |
|---|
| 王迪: "电商仓储联合订单批次分配排序和拣选路径问题", 《中国优秀硕士学位论文全文数据库经济与管理科学辑》 * |
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