CN106527448A - Improved A* robot optimal path planning method suitable for warehouse environment - Google Patents
Improved A* robot optimal path planning method suitable for warehouse environment Download PDFInfo
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- 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
- G05D1/0219—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory ensuring the processing of the whole working surface
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- 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
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
Abstract
The invention discloses an improved A* robot optimal path planning method suitable for a warehouse environment. The improved A* robot optimal path planning method comprises the steps of: firstly, designing an effective warehouse model capable of flexible expansion, including the design of shelf distribution and a road operating rule; simplifying a path planning problem into path planning problems among nodes according to the road operating rule in the warehouse mode; then adopting an improved A* algorithm to search for an optimal path between two nodes, wherein the calculation of a heuristic function includes estimation of a steering price, a Manhattan distance and bypass distance; and finally, adding a path between an initial position of a robot and an initial node as well as a path between a target position and a target node into a previous node list to form a complete path list. The nodes in the path list correspond to positions in an actual warehouse, the optimal operating path of the robot is obtained, and the operating paths of the robot are extended into an operating state list of the robot.
Description
Technical field
The present invention relates to the optimum path planning problem in warehouse environment, for advising with DYNAMIC DISTRIBUTION shelf and road
Storehouse model then, the present invention propose the path planning algorithm for improving A*, solve the optimum road between 2 points in storehouse model
Footpath planning problem, it is ensured that storage mobile robot reaches target with optimal route.
Background technology
With technology of Internet of things, roboticses, the development of computer technology, multi-robot control system is applied to certainly
In the sorting link of dynamicization warehouse system, the development trend of logistics sorting is had become.Traditional commodity sort mode be by point
Pick the corresponding shelf of personnel's traversal and complete order packing work.In warehousing system, the layout of commodity shelf is using dynamic
The mode of distribution, and corresponding shelf are transported to into sorting office by robot, so as to complete the packing work of goods orders.Storehouse
The design of storehouse model and the path planning problem under this warehouse environment are the piths of warehousing system design.Cause
This, storehouse model reasonable in design and suitable path planning algorithm have important work for the efficiency for improving commodity sorting
With.
In traditional sort mode, sorting personnel can freely walk about so as to complete in warehouse on the premise of not colliding
Into sorting task.Traditional sort mode efficiency is low, and working strength is big, and labor cost is high, currently by complete by robot
Replace into the sort mode that shelf are carried.State of the shelf in DYNAMIC DISTRIBUTION under automated sorting pattern, and have in warehouse
Multiple robots run the carrying work for completing shelf simultaneously, so as to assist sorting personnel to complete commodity packing work.In order to protect
The normal operation of card automated warehouse storage system, needs make rational planning for operation rule and running status of the robot in warehouse.
The Automatic Warehouse adopted at present in this way has the Kiva system of Amazon, the Swisslog of Switzerland, domestic Geek+
Team.
In storage environment, path planning problem can be solved by global path planning algorithm.Global path planning algorithm
Mainly have based on the path planning algorithm of linear time temporal logic, evolution algorithm, particle swarm optimization algorithm, ant colony optimization algorithm, mould
Intend annealing method etc..In the case of given robot starting point with impact point, these algorithms can be cooked up most in running environment
Shortest path.
A* algorithms are a kind of didactic path planning algorithms, are provided based on the environmental information that robot runs and are suitably opened
Hairdo function, searches out the optimal path between 2 points.With the intelligence such as evolution algorithm, particle swarm optimization algorithm, ant colony optimization algorithm
Energy optimized algorithm is compared, and A* algorithms have real-time high, and algorithm complex is low, the characteristic that easy programming is realized, and appropriate
Under the conditions of ensure that the optimality of searching route.But the optimality in A* algorithm search path depends on suitable heuristic letter
Number.Path plannings of the A* in geometry networking adopts manhatton distance as heuristic function at present, but is advising with road
In geometry networking then, while it is also contemplated that during the steering cost of robot, it is desirable to provide more accurate heuristic information is caused
Searching route optimization.
The content of the invention
The present invention to be overcome the disadvantages mentioned above of prior art, there is provided a kind of improvement A* robots suitable for warehouse environment are most
Shortest path planing method.
The characteristic that the present invention is realized using the real-time and easy programming of A* algorithms, according to the operation rule of storehouse model and
On the premise of considering that robot turns to cost, there is provided suitable heuristic information causes searching route optimization, overcomes tradition
A* algorithms cannot obtain the shortcoming of optimal path.Design first effectively can flexible expansion storehouse model, including shelf distribution
And the design of road operation rule, storehouse model is as shown in Figure 1.According to road operation rule in storehouse model, path is advised
It is the path planning problem between each node to draw problem reduction.Then, using between two nodes of improved A* algorithm search
Optimal path.Wherein, the calculating of heuristic function include turn to cost, manhatton distance, around the estimation of row distance.It is improved
The list that the path of A* algorithm search is made up of several nodes.Finally, by the initial position of robot and start node it
Between and node listing before path between target location and destination node is added in, constitute complete path list.
Node in path list is corresponding with the position in actual warehouse, the optimum running route of robot is obtained, and is expanded to
The running status list of robot.Improved A* has search efficiency high, and easy programming is realized and cartographic information builds easily
Advantage.
The improvement A* robots optimum path planning method suitable for warehouse environment of the present invention, including：
Step 1：The distribution of design shelf and road, it is stipulated that the width of road only allows a robot to pass through, i.e., 1
Individual unit length.In storehouse model, positioned at left side is sorting office, and right side is shelf heap, and each shelf heap is by 2 × 5
Shelf are constituted, and the length of each shelf and wide are 0.9 unit length.The sum of shelf heap can be adjusted flexibly simultaneously according to demand
And be odd number.Have between any two shelf heap and only one road, and overall shelf heap periphery is apart 1 with two
The road of individual unit length, so as to ensure the completeness and effectiveness of path planning algorithm.If there is two road to intersect at storehouse
Certain point in storehouse, then using the point as node N, the x coordinate value of N=(x, y), wherein x for present node, y is present node
Y-coordinate value.Define Sp=[xb yb xs ys] represent shelf relative position, wherein xb,ybRepresent that current shelf are located respectively
The relative position of shelf heap, xs,ysRelative position of the current shelf inside shelf heap is represented respectively.
Step 2：The operation rule of road in design repository, it is stipulated that road is one-way traffic, and two of arbitrary neighborhood
The travel direction of road is contrary.Robot can only be entered in shelf heap from the road of cross direction profiles.Definition robot is on road
Running status RS=[xR yR dx dy], wherein xR,yRRepresent coordinate position of the robot in warehouse coordinate system, dx,dyPoint
Not Biao Shi robot feasible direction, i.e. the travel direction of robot place road, dx,dy∈{0,1,2,3,4}.Work as dxFor 0
When, represent that robot cannot cross running；If dxFor 3, then robot can be to right travel；If dxFor 4, then robot can be to
Left lateral is sailed.Work as dyFor 0 when, represent robot cannot longitudinal driving；If dyFor 1, then robot can be travelled upwards；If dyFor 2,
Then robot can be to downward driving.
Step 3：The original position of given robot and target location, if robot can be just reached without any node
Target location, then directly give final path list with target location according to the original position of robot.Otherwise, simplified
For the path planning problem between warehouse node.Robot is saved from first node of initial position arrival as initial
Point, robot reach last node passed through during target location as destination node.
Step 4：On the basis of step 3, for given start node and destination node, searched using improved A* algorithms
Rope optimal path.When heuristic function cost is calculated, improved A* algorithms need to calculate between present node and destination node
Manhatton distance, turn to number of times and around row distance.Assume that as the current node estimated be n, remember manhatton distance cost
For hm(n), hm(n)=| xb-xf|+|yb-yf|, wherein xb, coordinates of the yb for start node, xf,yfFor the coordinate of destination node.
It is h that note turns to costt(n), htN ()=q × turncost, wherein q represent the minimum steering between present node and destination node
Number of times, turncost represent the cost value of each steering.Note is h around row distance coste(n), by judging present node and mesh
The number of times that detours of mark node, can obtain specific h with reference to the information of storehouse modele(n) value.It is heuristic above three is obtained
After cost, it is h (n) that note improves the heuristic function of A* algorithms, for estimating the heuristic generation of present node n and destination node
Valency, h (n)=hm(n)+ht(n)+he(n).Using A* algorithm search node listings are improved, List is designated asj。
Step 5：Path list between the initial position and start node of note robot is Listb, the target of robot
Path list between position and destination node is Listf.By ListbAdd to ListjHead, by ListfAdd to Listj
Afterbody, constitute complete path list.Node coordinate position in list is relative with the position coordinateses in the warehouse of reality
Should, obtain the running route of robot.
Step 6：The running route of robot is expanded to into the running status list of robot, SList is designated asR, by one it is
The robotary of row is constituted.According to given robot running route, the operation rule of each point place road on route are judged
Then, traffic direction of the robot in current point is obtained, so as to the running route of robot to be expanded to the running status of robot.
The running status list of robot is sent to into robot, you can allow robot to complete the task of line walking.
It is an advantage of the invention that：The path planning problem in the warehouse of shelf DYNAMIC DISTRIBUTION is solved using A* algorithms are improved,
Rationally effective storehouse model is devised, and proposes suitable heuristic information and cause searching route optimization.Due to storehouse
The steering cost of the one-way traffic and consideration robot of storehouse road, the heuristic function that traditional A* algorithms are used cannot be solved
Certainly in this case the optimized problem of searching route.The invention on the basis of the heuristic information of traditional A* algorithms,
According to the particularity of road information, propose suitable heuristic information and calculate the algorithm of its cost, so as to solve path
The problem of optimality.Compared with dijkstra's algorithm, improve A* algorithms and have search efficiency high, set up cartographic information easily excellent
Gesture.Compared to the intelligent algorithm such as such as ant group algorithm, evolution algorithm, improve A* algorithms and there is easily programmable realization, amount of calculation be little,
The high advantage of real-time.For large-scale Automatic Warehouse and the warehouse environment with a fairly large number of robot, this
The real-time of the extensibility and path planning algorithm of the storehouse model of bright design, programs easy advantage and can be good at solution
Certainly corresponding problem, for the sort efficiency for improving warehouse has help.
Description of the drawings
Fig. 1 is the storehouse model design drawing of the present invention
Fig. 2 is the steering number of times calculation flow chart of the present invention
Fig. 3 is the Distance Judgment flow chart that detours of the present invention
Fig. 4 is the robot initial position of the present invention and target location
Fig. 5 is the search pattern of the improvement A* algorithms of the present invention
Specific embodiment
The improvement A* robots optimum path planning method suitable for warehouse environment of the present invention is led to below in conjunction with accompanying drawing
Cross simplified example to be further described.
Mainly there is herein below suitable for the improvement A* optimum path plannings method of warehouse environment：Design first and effectively may be used
The storehouse model of flexible expansion, including shelf distribution and the design of road operation rule, storehouse model are as shown in Figure 1.According to
Path planning problem is reduced to the path planning problem between each node by road operation rule in storehouse model.Then, adopt
With the optimal path between two nodes of improved A* algorithm search.Wherein, the calculating of heuristic function includes turning to cost, graceful
Hatton distance, around the estimation of row distance.The list that the path of improved A* algorithm search is made up of several nodes.Finally,
Before path between the initial position and start node of robot and between target location and destination node is added to
In node listing, complete path list is constituted.Node in path list is corresponding with the position in actual warehouse, obtain
The optimum running route of robot, and expand to the running status list of robot.Improved A* has search efficiency high, easily compiles
Cheng Shixian and cartographic information build easy advantage.Detailed process is as follows：
Step 1：The distribution of design shelf and road, it is stipulated that the width of road only allows a robot to pass through, i.e., 1
Individual unit length.In Fig. 1 storehouse models, positioned at the Shi Liangge sorting offices in left side, right side is 5 × 5 shelf heap, each shelf
Heap is made up of 2 × 5 shelf, the length of each shelf and wide is 0.9 unit length.The sum of shelf heap can be according to demand
It is adjusted flexibly and for odd number.Have between any two shelf heap and only one road, and overall shelf heap periphery has
Two at a distance of the road for 1 unit length.
Step 2：The operation rule of road in design repository, it is stipulated that road is one-way traffic, and two of arbitrary neighborhood
The travel direction of road is contrary.Robot can only be entered in shelf heap from the road of cross direction profiles.Definition robot is on road
Running status RS=[xR yR dx dy], wherein xR,yRRepresent coordinate position of the robot in warehouse coordinate system, dx,dyPoint
Not Biao Shi robot feasible direction, i.e. the travel direction of robot place road, dx,dy∈{0,1,2,3,4}.Work as dxFor 0
When, represent that robot cannot cross running；If dxFor 3, then robot can be to right travel；If dxFor 4, then robot can be to
Left lateral is sailed.Work as dyFor 0 when, represent robot cannot longitudinal driving；If dyFor 1, then robot can be travelled upwards；If dyFor 2,
Then robot can be to downward driving.
Step 3：The original position of given robot and target location, corresponding shelf coordinate are respectively A=[0330], B
=[2130], as shown in Figure 4.Its corresponding warehouse coordinate is respectively RA=(4,10), RB=(16,4).It is reduced to initial
Node NA=(0,9) and destination node NBPath planning problem between=(18,3).
Step 4：On the basis of step 3, for given start node and destination node, searched using improved A* algorithms
Rope optimal path.When heuristic function cost is calculated, improved A* algorithms need to calculate between start node and destination node
Manhatton distance, turn to number of times and around row distance.Assume that as the current node estimated be n, remember manhatton distance cost
For hm(n), hm(n)=| xb-xf|+|yb-yf|, wherein xb,ybFor the coordinate of start node, xf,yfFor the coordinate of destination node.
It is h that note turns to costt(n), htN ()=n × turncost, wherein n represent the minimum steering between start node and destination node
Number of times, turncost represent the cost value of each steering, and the flow process for calculating steering number of times is as shown in Figure 2.Note is around row distance cost
For heN (), the algorithm flow of the number of times that detours is as shown in figure 3, the information with reference to storehouse model can obtain specific he(n) value.
After obtaining the heuristic cost of above three, it is h (n) that note improves the heuristic function of A* algorithms, for estimating present node n and mesh
The heuristic cost of mark node, h (n)=hm(n)+ht(n)+he(n).Using A* algorithm search node listings are improved, it is designated as
Listj。ListjContain (0,9), (0,6), (0,3), (0,0), (6,0), (12,0), (18,0), (18,3).
Step 5：Path list between the initial position and start node of note robot is Listb, contain (4,10),
(4,9) two points.Path list between the target location of robot and destination node is Listf, contain (16,3), (16,
4).By ListbAdd to ListjHead, by ListfAdd to ListjAfterbody, constitute complete path list ListjBag
Contained (4,10), (4,9), (0,9), (0,6), (0,3), (0,0), (6,0), (12,0), (18,0), (18,3), (16,3)
(16,4).Node coordinate position in list is corresponding with the position coordinateses in the warehouse of reality, obtain the operation of robot
Route, as shown in Figure 5.
Step 6：The running route of robot is expanded to into the running status list of robot, SList is designated asR, by one it is
The robotary of row is constituted.According to given robot running route, the operation rule of each point place road on route are judged
Then, traffic direction of the robot in current point is obtained, so as to the running route of robot to be expanded to the running status of robot.
SListR[4 10 2 0] are contained, [4 90 4], [0 90 4], [0 62 0], [0 32 0], [0 02 0], [6 0
0 3], [12 00 3], [18 00 3], [18 31 0], [16 30 4], [16 41 0].By the operation shape of robot
State list is sent to robot, you can allow robot to complete the task of line walking.
The present invention solves the path planning problem in the warehouse of shelf DYNAMIC DISTRIBUTION using A* algorithms are improved, and devises rationally
Effective storehouse model, and propose suitable heuristic information and cause searching route optimization.Due to the list of depot road
To travelling and the steering cost of consideration robot, the heuristic function that traditional A* algorithms are used cannot solve such case
Under the optimized problem of searching route.The invention is believed according to road on the basis of the heuristic information of traditional A* algorithms
The particularity of breath, proposes suitable heuristic information and calculates the algorithm of its cost, so as to solve asking for path optimality
Topic.Compared with dijkstra's algorithm, improve A* algorithms and have search efficiency high, set up the easy advantage of cartographic information.With such as
The intelligent algorithms such as ant group algorithm, evolution algorithm are compared, and are improved A* algorithms and are had easily programmable realization, and amount of calculation is little, and real-time is high
Advantage.For large-scale Automatic Warehouse and the warehouse environment with a fairly large number of robot, present invention design
The real-time of the extensibility and path planning algorithm of storehouse model, programs easy advantage and can be good at solving accordingly
Problem, for the sort efficiency for improving warehouse has help.
Claims (3)
1., suitable for the improvement A* robots optimum path planning method of warehouse environment, comprise the following steps that：
Step 1：The distribution of design shelf and road, it is stipulated that the width of road only allows a robot to pass through, i.e., 1 list
Bit length；In storehouse model, positioned at left side is sorting office, and right side is shelf heap, and each shelf heap is by 2 × 5 shelf
Composition, the length of each shelf and wide is 0.9 unit length；The sum of shelf heap can be adjusted flexibly according to demand and be
Odd number；Have between any two shelf heap and only one road, and overall shelf heap periphery is apart 1 list with two
The road of bit length, so as to ensure the completeness and effectiveness of path planning algorithm；If there is two road to intersect in warehouse
Certain point, then using the point as node N, the x coordinate value of N=(x, y), wherein x for present node, y are sat for the y of present node
Scale value；Define Sp=[xb yb xs ys] represent shelf relative position, wherein xb,ybCurrent shelf place shelf are represented respectively
The relative position of heap, xs,ysRelative position of the current shelf inside shelf heap is represented respectively；
Step 2：The operation rule of road in design repository, it is stipulated that road is one-way traffic, and the two road of arbitrary neighborhood
Travel direction it is contrary；Robot can only be entered in shelf heap from the road of cross direction profiles；Define fortune of the robot on road
Row state RS=[xR yR dx dy], wherein xR,yRRepresent coordinate position of the robot in warehouse coordinate system, dx,dyDifference table
Show the feasible direction of robot, the i.e. travel direction of robot place road, dx,dy∈{0,1,2,3,4}；Work as dxFor 0 when, table
Show that robot cannot cross running；If dxFor 3, then robot can be to right travel；If dxFor 4, then robot can be to left lateral
Sail；Work as dyFor 0 when, represent robot cannot longitudinal driving；If dyFor 1, then robot can be travelled upwards；If dyFor 2, then machine
Device people can be to downward driving；
Step 3：The original position of given robot and target location, if robot can just reach target without any node
Position, then directly give final path list with target location according to the original position of robot；Otherwise, it is reduced to storehouse
Path planning problem between the node of storehouse；Using robot from first node of initial position arrival as start node,
Robot reaches last node passed through during target location as destination node；
Step 4：On the basis of step 3, for given start node and destination node, using improved A* algorithm search most
Shortest path；When heuristic function cost is calculated, improved A* algorithms need to calculate graceful between present node and destination node
Hatton's distance, turns to number of times and around row distance；Assume that as the current node estimated be n, note manhatton distance cost is hm
(n), hm(n)=| xb-xf|+|yb-yf|, wherein xb,ybFor the coordinate of start node, xf,yfFor the coordinate of destination node；Note turns
It is h to costt(n), htN ()=q × turncost, wherein q represent the minimum steering time between present node and destination node
Number, turncost represent the cost value of each steering；Note is h around row distance coste(n), by judging present node and target
The number of times that detours of node, can obtain specific h with reference to the information of storehouse modele(n) value；Obtaining above three heuristic generation
After valency, it is h (n) that note improves the heuristic function of A* algorithms, for estimating the heuristic cost of present node n and destination node, h
(n)=hm(n)+ht(n)+he(n)；Using A* algorithm search node listings are improved, List is designated asj；
Step 5：Path list between the initial position and start node of note robot is Listb, the target location of robot with
Path list between destination node is Listf；By ListbAdd to ListjHead, by ListfAdd to ListjTail
Portion, constitutes complete path list；Node coordinate position in list is corresponding with the position coordinateses in the warehouse of reality, obtain
To the running route of robot；
Step 6：The running route of robot is expanded to into the running status list of robot, SList is designated asR, by a series of machine
Device people state is constituted；According to given robot running route, judge the operation rule of each point place road on route, obtain
Traffic direction of the robot in current point, so as to the running route of robot to be expanded to the running status of robot；By machine
The running status list of people is sent to robot, allows robot to complete the task of line walking.
2. the improvement A* robots optimum path planning method suitable for warehouse environment according to claim 1, its feature
It is：In the step 1, the sum of shelf heap is necessary for odd number, it is ensured that the road quantity in transverse and longitudinal direction is even number, it is to avoid go out
The irremovable state of existing robot.In step 1, shelf location coordinate representation integrally sets up coordinate system with shelf heap, defines every
Individual shelf relative position in overall shelf heap, obtains the coordinate in warehouse coordinate system by coordinate transform.
3. the improvement A* robots optimum path planning method suitable for warehouse environment according to claim 1, its feature
It is：Manhatton distance is selected in the step 4, cost is turned to and the cost that detours is used as the heuristic letter for improving A* algorithms
Breath；In route searching, not only constrained by road direction, and considered the cost of robot steering；Design is suitable to calculate
Method calculates the cost of heuristic function so that searching route optimization.
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