CN115237135A - Mobile robot path planning method and system based on conflict - Google Patents
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Abstract
The invention provides a mobile robot path planning method and a system based on conflict, which relate to the technical field of robot path planning and comprise the following steps: according to the task to be distributed and the state information of each robot, determining a target robot for completing the task, and updating the state information of the target robot; constructing a path planning model, wherein the path planning model comprises upper layer search and lower layer search; the upper layer search is to search a constraint tree, the constraint tree comprises a plurality of nodes, each node comprises a group of constraints and a path conforming to the constraints, and the path is obtained by the lower layer search; the lower layer searches for path planning solution by tasks according to the state information of each robot; and obtaining the picking path of the target robot by solving the path planning model. By the method, the efficient and conflict-free multi-robot picking path can be rapidly determined, and the picking efficiency of the mobile robot picking system is improved.
Description
Technical Field
The invention belongs to the technical field of robot path planning, and particularly relates to a mobile robot path planning method and system based on conflict.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art that is already known to a person of ordinary skill in the art.
With the rapid development of the logistics industry, the traditional sorting system of people to goods cannot meet the demand of market growth, the strategic position of intelligent storage is improved under the demand of the industrial automation and manufacturing industry, the sorting mode of people to goods is gradually replaced by the sorting mode of people to goods, and the intelligent storage and sorting system becomes a hot problem in the research field of intelligent systems.
In a global range, an intelligent warehousing system of multiple mobile robots is widely applied, and path planning is one of important optimization links. For the Multi-mobile robot Path planning problem, the Multi-Agent Path planning problem (MAPF) can be abstracted, the MAPF abstracts the warehouse layout into an undirected graph, and the mobile robot is abstracted into an Agent. A plurality of intelligent agents are distributed in an undirected graph, each intelligent agent is provided with a starting position and a target position, each intelligent agent needs to find a path, and the robot cannot collide in the moving process.
At present, robot path planning algorithms mainly include an a algorithm, a D algorithm, an ant colony algorithm, a cost search tree algorithm, a Conflict-based search algorithm (CBS), and the like. The inventor finds that the D-star algorithm and the ant colony algorithm are dynamic path planning algorithms, so that obstacles can be avoided, but a global optimal path cannot be found, an intelligent warehouse is a highly dynamic environment which is composed of multiple mobile robots and goods shelves and has multiple obstacles and real-time change, and the dynamic planning algorithm is more prone to traffic jam and oscillation in path planning when the number of robots is larger. The a-algorithm is a heuristic search algorithm and can find a global optimal path, but the a-algorithm may cause deadlock when multiple robots work in a dynamic environment, and the solving difficulty index is increased as the number of the robots is increased. The cost search tree algorithm and the conflict-based search algorithm and the A-algorithm are divided into a high-level search algorithm and a low-level search algorithm, and both algorithms are suitable for the multi-robot path planning problem with medium scale. In addition, the existing path planning method only considers the problems of path conflict and deadlock, and ignores the influence on the sorting efficiency due to path congestion.
Disclosure of Invention
In order to solve the above problems, a first aspect of the present invention provides a method for planning a path of a mobile robot based on collisions, which is capable of determining an efficient and collision-free multi-robot picking path and improving the picking efficiency of a mobile robot picking system.
In order to achieve the above object, the present invention mainly includes the following aspects:
in a first aspect, an embodiment of the present invention provides a mobile robot path planning method based on a conflict, including:
determining a target robot for completing the task according to the task to be distributed and the state information of each robot, and updating the state information of the target robot;
constructing a path planning model, wherein the path planning model comprises upper layer search and lower layer search; the upper-layer search is a search constraint tree which comprises a plurality of nodes, each node comprises a group of constraints and a path conforming to the constraints, and the path is obtained by the lower-layer search; the lower-layer search carries out path planning solution by tasks according to the state information of each robot;
and obtaining the picking path of the target robot by solving the path planning model.
In a possible embodiment, before determining a target robot for completing a task according to the task to be assigned and the state information of each robot, and updating the state information of the target robot, the method further includes:
determining state information corresponding to the robot according to the stage of the robot; the state information comprises an idle state, a shelf searching state, a goods carrying and picking point state, a goods position state of sending the goods carrying and picking completion to the nearest placeable shelf, a task completion and return state to a charging area state, an electric quantity shortage state and a charging state.
In one possible embodiment, the nodes include child nodes, parent nodes, and root nodes, the root node includes a set of empty constraints, and the child nodes inherit the constraints of the parent node and add a new constraint to the robot.
In a possible embodiment, the specific manner of searching the constraint tree includes:
when initializing, the root node of the constraint tree does not contain any constraint, a solution is generated, and the cost value of the solution is solved;
after initialization is finished, acquiring a node with the lowest cost value, and judging whether solutions in the node have conflict or not; if so, expanding a child node by taking the node as a father node, wherein the child node inherits the constraint of the father node and adds a new constraint which is determined according to the conflict; and calling lower-layer search to replan the path of the child node, updating the cost value of the child node after updating the path, judging whether the solution in the child node has conflict, and if so, expanding the child node by taking the child node as a parent node until the node without conflict is returned.
In one possible implementation mode, when whether the solutions in the nodes have conflict or not is judged, the priority of the conflict is determined according to the state information of the robot; and determining a conflict resolution strategy according to the conflict priority.
In a possible implementation mode, when the lower layer search is used for path planning and solving, a global state table of the robot is introduced, and the time and the position of the robot are constrained; the cost function from the initial point to the target point of the robot is the sum of the cost of the position node where the robot is located from the starting point, the cost of the position node where the robot is located from the end point and the steering cost of the robot.
In one possible embodiment, the path planning solving process of the lower layer search includes:
acquiring a grid map and inputting grid map data; the method comprises the steps of initializing an initial position s, a target position g, a node s with a generation time state of t and containing coordinates and f values, and initializing a priority queue OPEN and CLOSE; wherein, OPEN represents the nodes to be traversed, CLOSE represents the traversed nodes, and the nodes of the OPEN queue are arranged in ascending order according to the f value;
putting the node s into an OPEN queue;
if the OPEN queue is not empty, taking out a head node H, storing the head node H in a CLOSE queue, checking whether the current node is a target position g, and if so, terminating the algorithm;
if the target position is not the target position g, traversing the adjacent nodes of the head node H;
if the adjacent node L is an obstacle or the adjacent node L is already stored in the CLOSE list, ignoring the node;
if the adjacent node L is not in the OPEN list and is not constrained, calculating a cost value of the adjacent node L; adding the adjacent node L into an OPEN list, and marking a father node of the adjacent node L as a head node H;
if the adjacent node L is not in the OPEN list but has the constraint that the position of the node can not be searched at the time; searching whether the node H is in the CLOSE list or not, and traversing a new adjacent node if the node H is not in the CLOSE list; if the node H is deleted from the CLOSE list, the node H 'with the updated time state is put into the OPEN list to represent waiting, and the father node of the node H' is marked as the node H;
if the adjacent node L is in the OPEN list, checking the original cost from the starting node s to the node L and the cost of reaching the adjacent node L through the head node H, and updating the node cost value and the parent-child relationship;
and backtracking to the starting node according to the node dependency relationship to obtain a path.
In a second aspect, an embodiment of the present invention further provides a mobile robot path planning system based on collision, including:
the state updating module is used for determining a target robot for completing the task according to the task to be distributed and the state information of each robot and updating the state information of the target robot;
the model building module is used for building a path planning model, and the path planning model comprises upper layer search and lower layer search; the upper-layer search is a search constraint tree which comprises a plurality of nodes, each node comprises a group of constraints and a path conforming to the constraints, and the path is obtained by the lower-layer search; the lower layer search carries out path planning solution by tasks according to the state information of each robot;
and the path acquisition module is used for solving the path planning model to obtain the picking path of the target robot.
In a third aspect, an embodiment of the present invention provides a computer device, including: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating via the bus when a computer device is running, the machine-readable instructions when executed by the processor performing the steps of the collision-based mobile robot path planning method as described in any one of the possible embodiments of the first aspect and the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored, and the computer program is executed by a processor to perform the steps of the collision-based mobile robot path planning method as described in any one of the possible implementation manners of the first aspect and the first aspect.
Based on the technical scheme, the invention has the following beneficial effects:
1. considering the dynamic characteristics of the application scene of logistics sorting, the invention expands the static multi-robot path planning problem and provides a mobile robot path planning method based on conflict. And moreover, by constructing a path planning model comprising upper-layer search and lower-layer search and solving to obtain the picking path of the target robot, the efficient and conflict-free multi-robot picking path can be quickly determined, and the picking efficiency of the mobile robot picking system is improved.
2. And determining the priority of the conflict according to the state information of the robot, and determining a conflict resolution strategy according to the priority of the conflict, so that the conflict can be resolved more efficiently.
3. When the lower-layer search is used for path planning and solving, a robot global state table is introduced to constrain the time and the position of the robot, and the steering cost is added to the cost function from an initial point to a target point of the robot, so that the path of the robot can be smoother.
4. The method is suitable for path planning of a Kiva-like mobile robot goods-to-person picking system, and has reference function for other mobile robot picking systems.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a diagram of a warehouse layout provided by an embodiment of the present invention;
FIG. 2 is a flow chart of a method for collision-based mobile robot path planning according to an embodiment of the present invention;
FIG. 3 is a task flow diagram provided by an embodiment of the invention;
FIGS. 4 (a) -4 (b) are schematic diagrams of conflicts provided by embodiments of the present invention;
FIG. 5 is a graph of an experimental path provided by an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a collision-based mobile robot path planning system according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Reference numerals: 1-a sorting table; 2-crossroad; 3-a charging zone; 4-a passageway; 5-a mobile robot; 6-shelf.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The invention mainly aims at multi-robot path planning under the scene of a sorting task of a picking center, and is shown in figure 1 (schematic diagram, based on reality). A large number of magnetic nails are paved under the picking center, and the mobile robot 5 conducts navigation by sensing the position of the mobile robot. The work flow is that the mobile robot waits for tasks in the charging area 3, when a group of orders arrive, the tasks are distributed by the system, the path is calculated for the robot through the central computer and then transmitted to the robot, and then the robot moves according to the designated path. The specific moving process is that the robot receives a command and immediately moves to the target goods shelf 6 from the current position, moves to the target sorting table 1 after bearing the target goods shelf 6, and finally sends the goods shelf after being sorted to the goods shelf area. For the problem of planning paths by multiple robots, if a single robot can complete a task more quickly, the single robot can be put into the next round of tasks more quickly, so that the sorting efficiency can be improved, and the aim is to minimize the average cost of all paths. According to the application characteristics, the existing multi-robot path planning model is selected as a problem model, and the minimized path cost sum is taken as a target function.
Referring to fig. 2, fig. 2 is a schematic flowchart of a mobile robot path planning method based on collision according to an embodiment of the present invention, and as shown in fig. 2, the mobile robot path planning method based on collision according to the embodiment specifically includes the following steps:
s201: and determining a target robot for completing the task according to the task to be distributed and the state information of each robot, and updating the state information of the target robot.
In specific implementation, in consideration of the dynamic characteristic of the application scenario of logistics sorting, the embodiment expands the static multi-robot path planning problem, and provides a dynamic multi-robot path planning method. In the dynamic problem, the number of robots executing tasks in the environment and the change of the tasks are considered, different stages of the robots are divided into different states respectively, a target robot for completing the tasks is determined according to the tasks to be distributed and the state information of the robots, and the state information of the target robot is updated.
As an optional embodiment, before determining a target robot for completing a task according to the task to be assigned and the state information of each robot, and updating the state information of the target robot, the method further includes:
determining state information corresponding to the robot according to the stage of the robot; the state information comprises an idle state, a goods shelf searching state, a goods shelf carrying state and then sending to a picking point state, a goods position state of sending the goods shelf to which the goods shelf can be placed after picking is completed, a task completion return charging area state, an electric quantity shortage state and a charging state.
In a specific embodiment, the robot is divided into 7 states: the idle state mark is 0, the searching shelf state mark is 1, the state mark of the goods shelf after being carried is 2, the state mark of the goods shelf after being carried is 3, the state mark of the goods shelf which can be placed nearest after being picked is 4, the state mark of the task after being returned to the charging area is 5, and the charging state is 6. As shown in fig. 3, the robot specifically works as follows:
(1) Updating the state of the robot, issuing the task if the available robot can complete the task, or not issuing the task;
(2) The system issues the tasks to the target robots with the states of 0 and 4, and updates the states of the corresponding robots to 1;
(3) After finding the load of the goods shelf, the robot updates the state to 2 and conveys the goods shelf to a sorting table;
(4) After the goods shelf conveyed by the robot is sorted, the state of the robot is updated to 3, and the goods shelf is conveyed to a corresponding position of a goods shelf area;
(5) After the robot finishes the task of returning the goods shelf, the state is updated to 4, and the state of returning to the charging area is updated to 0; if the task is received midway, returning to the step (2);
(6) In the robot state 4, the state is updated to 5 by the shortage of electric power, the robot returns to the charging area to be charged, the charging state is updated to 6, and the charging completion state is updated to 0.
The multi-robot path planning problem can be described as: given an undirected graph space G = (V, E), where V is the set of vertices in the graph and E is the set of edges connecting the vertices, in this space a certain number of robots are distributed, assuming n robots, with the set R = (R, E) 1 ,r 2 ,…,r n ) To indicate that k robots are assigned a task at a time, with the set TR = (r) 1 ,r 2 ,…,r k ) And (4) showing. Each robot needs to perform a task from a starting point to a destination point, the path of which is tau i ＝{s i ,g i Denotes wherein s is i E.v denotes r i Initial position of (g) i E.v denotes r i A destination location at which the task needs to be performed. T = {0,1,2 … } is a discretized time series, r i Path τ of i T → V is a mapping from the time series T to the set of vertices V,
τ i (t) denotes the time t of the robot r i The position of (a). The robot may choose to stay at the current location or move to an adjacent location at any time. cost (tau) i ) Defined as path τ i The cost of the path is calculated in time, assuming t s For the start times of all robot paths,
is a path τ i Of (d), i.e. tau i (t s )＝s i ，
Then
The problem of multi-robot path planning is to find a feasible solution for each robot to a path from a starting position to a destination position, wherein the feasible solution means that no conflict exists between paths of the multiple robots. The collision-free definition among all robot paths is as follows:
the first constraint shows that node conflict is not allowed to occur on the path, namely any node at any one moment is only allowed to be occupied by one robot; the second constraint states that no transposition conflict is allowed on the path, i.e. two robots are not allowed to interchange positions at any time, as shown in fig. 4 (a is a node conflict; b is a transposition conflict).
S202: constructing a path planning model, wherein the path planning model comprises upper layer search and lower layer search; the upper-layer search is a search constraint tree which comprises a plurality of nodes, each node comprises a group of constraints and a path conforming to the constraints, and the path is obtained by the lower-layer search; and the lower layer search carries out path planning solution by tasks according to the state information of each robot. Optionally, the nodes include child nodes, parent nodes, and root nodes, where the root node includes a group of empty constraints, and the child nodes inherit the constraints of the parent nodes and add a new constraint to the robot.
In specific implementation, this embodiment is an improved two-phase algorithm based on the collision search algorithm (CBS) optimization, and the improved algorithm can implement multi-robot multitask dynamic computation, called as LCBS (Loop conflicted-based search), and first introduces the upper search:
the upper layer search is to search for constraint tree nodes (CT nodes), each Node contains 2 pieces of information:
(1) A set of constraints (n.constraints). Each constraint belongs to a single robot. The root of the CT contains a set of empty constraints, and child nodes of nodes in the CT inherit the constraints of a parent node and add a new constraint to a robot.
(2) Solution (n.solution). A set of k-bar robot paths, one path for each robot. The proxy's path must comply with the constraints. These paths can be found by a lower level search.
The objective function for multi-robot path optimization is the sum of the path costs and the minimum, total cost (n.cost) of the current solution (totaling all single robot path costs), this cost being called the cost of node N, i.e. the cost of node N
Upon initialization, a root node of a constraint tree is created, the node not containing any constraints, then a solution is generated and its cost value is found, the path of each robot of the solution in the initial node being found ignoring the presence of other robots. And after the initialization is finished, the algorithm continuously takes out a node with the lowest cost from the queue of the OPEN, supposing N, and judges whether the solution in the node has conflict. Node collisions are represented as a 4-tuple (r) i ,r j V, t) means r i And r j Occupying the node v at the same time at the moment t; transposition conflict is expressed as a 5-tuple (r) i ,r j ,v j ,v i T) means r i And r j The node locations are exchanged at time t. Any conflict found in the node, assumed to be conflit = (r) i ,r j V, t), we extend the left and right child nodes from the node, both of which inherit the constraints of the parent node. Adding constraints (r) to the left and right child nodes respectively i V, t) and (r) j V, t) (meaning that robot r cannot occupy node v at time node t), due to the constraint set for r i And r j The number of constraints of (a) is changed, so that the lower-layer improved A search needs to be called to r i And r j And re-planning the path, and updating the cost value of the node after the path is updated. Transposition punchIn a similar manner, a set of constraints are ultimately generated for the nodes. When a plurality of robots are running, if a new task is suddenly issued, the path of the previous task is unchanged, the path of the robot is calculated according to the lower improved A-algorithm, the paths of the two tasks are combined to form a new node, and the new node is solved after a conflict is found until the node without the new conflict is returned.
As an optional implementation, when the solutions in the nodes are judged to have conflicts, the priority of the conflicts is determined according to the state information of the robot; and determining a conflict resolution strategy according to the conflict priority. For example, conflicts that are easy to resolve are resolved first, reducing the total number of conflicts, and conflicts that are relatively difficult to resolve last. The robot has 7 states, wherein 5 kinds can be moved, and the five kinds are divided into two types to distinguish whether the robot is carried or not. When two robots conflict, the empty robot does not have an obstacle when planning a path, the conflict can be solved by basically waiting for or moving once when the conflict occurs, the robot bearing the goods shelf has the highest difficulty in solving the transposition conflict in the aisle, and finally the conflict is solved, so that the conflict solving efficiency can be improved.
In order to realize dynamic path planning, a robot state global table is introduced, positions and states of the robot at all time nodes between the current time and the end of all tasks can be recorded on the table, and starting positions and target positions of tasks at the time nodes are also recorded, and a schematic table is shown in table 1. After a new task is sent to the corresponding robot, the system can recalculate according to the static multi-robot path planning method, the node of the current robot is taken as the starting position, the target position is unchanged, the priority of obstacle distribution and conflict is judged according to the state, two-stage solution is carried out, a new solution is obtained, and dynamic path solution is realized.
TABLE 1 robot State Global Table
The lower layer search adopts an improved A-x algorithm, a time state is introduced into nodes of the path, the steering cost is considered, the steering cost is added to enable the walking path of the robot to be smoother, unnecessary steering is avoided, and the requirements of a real scene are met.
The cost function from the initial point to the target point of the robot is as follows:
f(n)＝g(n)+h(n)+c(n)
in the formula: f (n) represents the total cost value of the starting point passing through the current point and reaching the target point; g (n) is the cost of the distance between the node n where the robot is located and the starting point; h (n) is the estimated cost of the distance between the position node n where the robot is located and the end point; c (n) is the steering cost of the robot.
g(n)＝∣x s -x n ∣+∣y s -y n ∣；
h(n)＝∣x g -x n ∣+∣y g -y n ∣；
The study model is a grid map and the location of each node can be represented in coordinates, i.e., (x, y). The robot can only move towards four directions, so g (n) and g (n) are Manhattan distances, c (n) needs to judge whether the point is an inflection point, and only needs to compare the coordinates of the previous node with the adjacent nodes, the inflection point is obtained if the coordinates are different from the coordinates of the previous node, and otherwise, the robot is a straight line. If the walking is straight or turning to the terminal point, the cost is 0, and if the walking is turning, the cost is 1.
The flow of the improved a algorithm is as follows:
step 1: inputting grid map data G = (V, E), an initial position s and a target position G; generating a node s with a time state t and containing coordinates and a total cost f value, and initializing priority queues OPEN and CLOSE (respectively representing nodes to be traversed and nodes already traversed), wherein the nodes of the OPEN queue are arranged in ascending order of the f value.
Step 2: put node s into the OPEN list.
Step 3: if the OPEN is not empty, a head node H (the node with the highest priority) is taken out, the head node H is stored in a CLOSE list, whether the current node is the target node g or not is checked, if the current node is the target node g, the algorithm is terminated, and the Step 5 is skipped.
Step 4: the neighbors of node H are traversed.
Step 4.1: if the neighboring node L is an obstacle or the node L is already stored in the CLOSE list, the node is ignored.
Step 4.2: if the node L is not in the OPEN list and has no constraint, calculating the cost value of the node L. And adding the node L into the OPEN list, and marking the parent node of the node L as the node H.
Step 4.3: if node L is not in the OPEN list but there is a constraint that the node cannot search for the location at that time. Searching whether the node H is in the CLOSE list or not, and traversing a new adjacent node if the node H is not in the CLOSE list; if the node H is deleted from the CLOSE list, the node H 'with the updated time state is put into the OPEN list to indicate waiting, and the parent node of the node H' is marked as the node H.
Step 4.4: if the node L is in the OPEN list, the original cost from the starting node s to the node L and the cost reaching the node L through the node H are checked, and the node cost value and the parent-child relationship are updated.
Step 4.4: and after the traversal of the adjacent nodes is finished, jumping to Step 3.
Step 5: if the algorithm is successfully searched, backtracking to the starting node according to the node subordination relation to obtain a path; otherwise, the algorithm has no solution.
S203: and obtaining the picking path of the target robot by solving the path planning model.
Here, in Step 3, open is empty indicating that all nodes have traversed to the end, at which point an end point has been found. OPEN cannot be empty until an end point is found, which is actually an infinite loop. And taking out the head node to take out the point with the highest priority to traverse the adjacent nodes, starting the next cycle if the adjacent nodes meet the constraint, or deleting the node from the CLOSE and putting the node into the OPEN again to wait.
The embodiment of the application provides a mobile robot path planning method based on conflict, in the dynamic problem, the consideration of the number of robots executing tasks in the environment and the change of the tasks is added, the robots are divided into different states in different stages, a target robot for completing the tasks is determined according to the tasks to be distributed and the state information of the robots, and the state information of the target robot is updated. And moreover, by constructing a path planning model comprising upper-layer search and lower-layer search and solving to obtain the picking path of the target robot, the high-efficiency and conflict-free multi-robot picking path can be quickly determined, and the picking efficiency of the mobile robot picking system is improved.
The experimental map is shown in fig. 1. The method of the invention is used for solving the paths of four tasks of five robots. The operation flow of the robot is that the goods shelves are corresponding from the charging area to the goods shelf area, the goods shelves are corresponding to the sorting platform from the goods shelf area, then the goods shelves are sent to the corresponding positions of the goods shelf area from the sorting platform, finally the robot is moved back to the corresponding charging area, the task is automatically issued by the system, and the method only realizes path planning. The robot task table is shown in table 2:
TABLE 2 robot task Table
The results of the experiments are shown in the following table:
TABLE 3 robot Path Table
The cost of the robot 1 is 90, the cost of the robot 2 is 85, the cost of the robot 3 is 87, the cost of the robot 4 is 94, the cost of the robot 5 is 90, and the total cost is 446. The schematic diagram of the walking path of the robot is drawn by using a matplotlib library, as shown in fig. 5. The setup is such that when each task group is completed, the color is changed, the other four robots have completed before the color change and have started the next task.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a mobile robot path planning system based on collision according to an embodiment of the present invention, and as shown in fig. 6, the embodiment further provides a mobile robot path planning system based on collision, where the mobile robot path planning system 600 includes:
a state updating module 610, configured to determine a target robot for completing a task according to the task to be allocated and state information of each robot, and update the state information of the target robot;
a model construction module 620, configured to construct a path planning model, where the path planning model includes an upper search and a lower search; the upper-layer search is a search constraint tree which comprises a plurality of nodes, each node comprises a group of constraints and a path conforming to the constraints, and the path is obtained by the lower-layer search; the lower layer search carries out path planning solution by tasks according to the state information of each robot;
a path obtaining module 630, configured to obtain a picking path of the target robot by solving the path planning model.
The collision-based mobile robot path planning system provided in this embodiment is used to implement the foregoing collision-based mobile robot path planning method, and therefore, a specific implementation of the collision-based mobile robot path planning system may be found in the foregoing embodiment of the collision-based mobile robot path planning method, and is not described herein again.
Referring to fig. 7, fig. 7 is a schematic structural diagram of a computer device according to an embodiment of the present invention. As shown in fig. 7, the computer device 700 includes a processor 710, a memory 720, and a bus 730.
The memory 720 stores machine-readable instructions executable by the processor 710, when the computer device 700 runs, the processor 710 and the memory 720 communicate through the bus 730, and when the machine-readable instructions are executed by the processor 710, the steps of the conflict-based mobile robot path planning method in the embodiment of the method shown in fig. 2 may be executed.
Based on the same inventive concept, embodiments of the present invention further provide a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program performs the steps of the collision-based mobile robot path planning method in the foregoing method embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A mobile robot path planning method based on conflict is characterized by comprising the following steps:
according to the task to be distributed and the state information of each robot, determining a target robot for completing the task, and updating the state information of the target robot;
constructing a path planning model, wherein the path planning model comprises upper layer search and lower layer search; the upper-layer search is a search constraint tree which comprises a plurality of nodes, each node comprises a group of constraints and a path conforming to the constraints, and the path is obtained by the lower-layer search; the lower layer search carries out path planning solution by tasks according to the state information of each robot;
and obtaining the picking path of the target robot by solving the path planning model.
2. The collision-based path planning method for mobile robots according to claim 1, wherein before determining the target robot for completing the task based on the task to be assigned and the state information of each robot, updating the state information of the target robot, further comprises:
determining state information corresponding to the robot according to the stage of the robot; the state information comprises an idle state, a shelf searching state, a goods carrying and picking point state, a goods position state of sending the goods carrying and picking completion to the nearest placeable shelf, a task completion and return state to a charging area state, an electric quantity shortage state and a charging state.
3. The collision-based path planning method for mobile robots of claim 1, wherein the nodes include child nodes, parent nodes and root nodes, the root node includes a set of empty constraints, the child nodes inherit the constraints of the parent nodes and add a new constraint to the robot.
4. The collision-based mobile robot path planning method according to claim 3, wherein the specific way of searching the constraint tree comprises:
when initializing, the root node of the constraint tree does not contain any constraint, a solution is generated, and the cost value of the solution is solved;
after initialization is finished, acquiring a node with the lowest cost value, and judging whether a solution in the node has conflict or not; if so, expanding a child node by taking the node as a father node, wherein the child node inherits the constraint of the father node and adds a new constraint which is determined according to the conflict; and calling lower-layer search to replan the path of the child node, updating the cost value of the child node after updating the path, judging whether the solution in the child node has conflict, and if so, expanding the child node by taking the child node as a parent node until the node without conflict is returned.
5. The collision-based path planning method for mobile robots according to claim 4, wherein when it is judged whether there is a collision in the solutions in the nodes, the priority of the collision is determined according to the state information of the robot; and determining a conflict resolution strategy according to the conflict priority.
6. The collision-based mobile robot path planning method according to claim 1, wherein the lower layer search introduces a global state table of the robot to constrain the time and position of the robot when performing path planning solution; the cost function from the initial point to the target point of the robot is the sum of the cost of the position node where the robot is located from the starting point, the cost of the position node where the robot is located from the end point and the steering cost of the robot.
7. The collision-based mobile robot path planning method according to claim 6, wherein the path planning solution process of the lower search includes:
acquiring a grid map and inputting grid map data; the method comprises the steps that an initial position s, a target position g, a node s with a generation time state of t and containing coordinates and a total cost f value, and a priority queue OPEN and CLOSE are initialized; the OPEN represents a node to be traversed, the CLOSE represents a node which has been traversed, and the nodes of the OPEN queue are arranged in ascending order according to the f value;
putting the node s into an OPEN queue;
if the OPEN queue is not empty, taking out a head node H, storing the head node H in a CLOSE queue, checking whether the current node is a target position g, and if so, terminating the algorithm;
if the target position is not the target position g, traversing the adjacent nodes of the head node H;
if the adjacent node L is an obstacle or the adjacent node L is already stored in the CLOSE list, ignoring the node;
if the adjacent node L is not in the OPEN list and is not constrained, calculating a cost value of the adjacent node L; adding the adjacent node L into an OPEN list, and marking a father node of the adjacent node L as a head node H;
if the adjacent node L is not in the OPEN list but has the constraint that the position of the node can not be searched at the time; deleting the head node H from the CLOSE list, putting the node H 'with the updated time state into the OPEN list to represent waiting, and marking the father node of the node H' as the head node H;
if the adjacent node L is in the OPEN list, checking the original cost from the starting node s to the node L and the cost of reaching the adjacent node L through the head node H, and updating the node cost value and the parent-child relationship;
and backtracking to the starting node according to the node dependency relationship to obtain a path.
8. A collision-based mobile robot path planning system, comprising:
the state updating module is used for determining a target robot for completing the task according to the task to be distributed and the state information of each robot and updating the state information of the target robot;
the model building module is used for building a path planning model, and the path planning model comprises upper layer search and lower layer search; the upper-layer search is a search constraint tree which comprises a plurality of nodes, each node comprises a group of constraints and a path conforming to the constraints, and the path is obtained by the lower-layer search; the lower layer search carries out path planning solution by tasks according to the state information of each robot;
and the path acquisition module is used for solving the path planning model to obtain the picking path of the target robot.
9. A computer device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating over the bus when a computer device is run, the machine-readable instructions when executed by the processor performing the steps of the conflict-based mobile robot path planning method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that a computer program is stored thereon, which computer program, when being executed by a processor, performs the steps of the collision-based mobile robot path planning method according to any one of claims 1 to 7.
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