CN106875090A - A kind of multirobot distributed task scheduling towards dynamic task distributes forming method - Google Patents
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Abstract
The present invention provides a kind of multirobot distributed task scheduling towards dynamic task and distributes forming method, and the method includes：Changed according to task dotted state in environmental map, analysis task distribution needs the factor and difficult point for considering, when task point occurs, based on multistage auction algorithm, generate task allocative decision, robot performs task according to the task allocative decision.The present invention solves the problems, such as dynamic task allocation in environment, traditional auction algorithm using primary distribution solve given task, there is significant limitations in face of dynamic task.The present invention passes through times bidding, for the purpose of time optimal, farthest using the resource of robot, the above method is emulated on VC++ and Csharp platforms, shown by substantial amounts of experiment simulation test result, the more traditional auction algorithm of improved auction algorithm more can be good at solving the dynamic task in environment, the demand of real-time be met by repeated dispensing, and can provide close optimal solution.
Description
Technical field
The present invention relates to intelligent robot auction algorithm technical field, more particularly, to a kind of towards dynamic task
Multirobot distributed task scheduling distributes forming method.
Background technology
As people go deep into artificial intelligence and complication system understanding, robot system is in building, military affairs, fire-fighting, work
There is good application prospect in the fields such as industry production.And task quantity is big in current multi-robot system, task point includes information
Complexity, the robot of participation is numerous, and the calculation cost of full search algorithm is presented exponential increase, and this causes full search algorithm very
The difficult optimal solution that Task Allocation Problem is searched out within the time of regulation.Therefore, full search algorithm and inapplicable extensive many
The solution of intelligent body dynamic task allocation problem.Task distribution must meet the requirement of real-time in large scale system, it is intended to
Obtain the solution of " good " within the time of regulation, and the solution for obtaining not necessarily optimal solution.Therefore for the multimachine of time-constrain
Device people's dynamic task allocation Study on Problems is significant.
Current task distribution much uses auction algorithm, and traditional auction algorithm oriented mission is static, is used
Auction algorithm.But there is very big defect more and in the case of dynamic change in face of task point quantity, it is impossible to carry out more
Secondary scheduling, it is impossible to the need for meeting real-time, more cannot get preferable effect.
The content of the invention
The present invention provide it is a kind of overcome above mentioned problem or solve the above problems at least in part towards dynamic task
Multirobot distributed task scheduling distributes forming method, and the method is based on improved auction algorithm, and combines A-star algorithms road
Footpath optimizing, multiple dynamic tasks in environment are solved the problems, such as by cooperative cooperating between robot.
In order to achieve the above object, the present invention provides a kind of multirobot distributed task scheduling towards dynamic task and distributes shape
Into method, the method includes：
S1：According to information in environmental map, factor and difficult point that Task Allocation Problem needs to consider are cleared；
S2：When task occurs, based on multistage auction algorithm, task allocative decision is generated, robot is according to the task
Allocative decision performs task.
Further, in the step S1, including：
S11：A target environment map is given, multiple machines with Mission Capability are distributed with the map
Task point and several barriers that people, attribute are changed over time；
S12：According to the state change of task point, factor and difficult point that task distribution needs to consider are cleared
Further, in the step S11, described environment is robot working environment, and the environmental map to giving is set up
Rectangular coordinate system, with x-axis to the right as positive direction, y-axis is upwards positive direction to coordinate system, and the working environment is divided into multiple grid
Lattice, environmental map is spatially distributed with N (N ∈ Z+) individual task point, M (M ∈ Z+) individual robot and B (B ∈ Z+) individual obstacle
Thing, and the coordinate of robot, task and barrier is determined respectively.
Wherein, task letter j represents that wherein j=1,2 ..., N, task are similar to a point on map.Robot
Represented with alphabetical i, wherein i=1,2 ..., M.Barrier letter b represents, wherein b=1,2 ..., B.
Coordinate of the task point on map beWherein j=1,2 ..., N；Coordinate of the robot on map beWherein i=1,2 ... M；Coordinate of the barrier on map beWherein k=1,2 ..., B.
The distance between task point and robot formula is：
It is assumed that task point j coordinates are respectively with task point j' coordinatesThen distance is public between the two
Formula is：
Further, in the step S12, task dotted state amount is presented exponential function form and changes with the time, task point j (j
=1,2 ..., N) quantity of state at (t+1) moment is expressed as with equation：
In formula：
Sj(t+1), Sj(t):Task j (j=1,2 ..., N) respectively in (t+1) and the task dotted state amount in t stages；
αj:J-th status variation rate of task point；
βi:The executive capability and β of robot ii＞ 0；
Δt:Time difference between t and (t+1)；
ε:Task amount threshold value, setting task amount threshold epsilon judges whether task completes, and works as sjDuring (t) ＜ ε, task has been represented
Complete.
This is a distributed dynamic task allocation problem of task status.The state change of task point is continuous index
Functional form (formula three).Its task amount is Annual distribution, and by how many robot, the task is performed, (intelligent body is held for it
Row ability summation) influence.
The deadline of task point j (j=1,2 ..., N) is inversely proportional with task point j executive capability sums are acted on.When t ranks
The m robot executive capability of section performance objective task j withWhen, show that the m robot cannot complete the task
Point, sjT () is presented the trend for rising；WhenWhen, show that m robot can complete the task point, sjT () is in now
The trend of drop.
Further, in the step S12, the factor that system task distribution needs consider includes task point property parameters and machine
The humanized parameter of device.
The property parameters of task point j (j=1,2 ... N) include：Task amount (i.e. state value) S of task pointjT (), represents t
The task amount of moment task point, wherein j=1,2 ..., N represent the primary quantity S of task point as t=0j(0)；Task point changes
Rate αj；Task point j is spatial distribution, task point j locations on environmental map
The property parameters of robot i (i=1,2 ..., M) include：Movement velocity vi, represent robot i within the unit interval
The path length walked；Robot capability value βi；Robot location on environmental map
With the increase of goal task point and robot quantity, the difficulty of Task Allocation Problem quickly increases, and each target is appointed
Cost, executive capability when significance level, status variation rate and each intelligent body that business is put perform task etc. are all to need to examine
The factor of worry.
It is assumed that going to the robot of j-th task point that other task points are not removed before next stage schemes generation, it is right
In given M (M ∈ Z+) individual robot, N (N ∈ Z+) individual task point, times bidding is carried out to task point and is hadPlant distribution
Scheme, its complexity is
In the step S2, further include：
S21：When detecting task, the nearest robot of chosen distance task point is used as broker machines people；If apart from several
The artificial same robot i (i=1,2 ..., M) of the nearest machine of task point, system can select chosen distance in several task points
The robot i of the nearest task point of rectangular coordinate system origin is its broker machines people；
S22：Broker machines people issue auction information waits to be feedback to the auction robot in communication radius；
S23：The auction robot for receiving auction information selects income highest task to click through according to self benefits function
Row is submitted a tender；
S24：Broker machines people collects bid information, target machine in target robot in being selected with pre-defined rule, and notice
Device people performs task；
S25：Not middle target auction robot performs nearest task point with the Robot Selection for being in communication blind zone.
Further, in the step S2, when detect completed task point and aimless idle machine people when, can be to sky
Not busy robot is auctioned again so that idle machine people regains new task point, so as to draw new allocative decision, directly
After all of task point has been performed into environment, stop auction.
The object-oriented task of the present invention is that, with time dynamic, task point is also distribution on the time point being done
, i.e., task point is inconsistent on the time being performed.
If continuing to use traditional auction algorithm flow, after allocative decision is generated, dynamic object is performed according to the program always
Task, then be difficult to adapt to the dynamic change and requirement of real-time of goal task.
The state change of the auction algorithm meeting real-time tracking task point after improvement, once in the presence of task point has been completed, can deposit
In aimless robot, algorithm can be redistributed to them, so as to draw new allocative decision, by times bidding
Realize, with time optimal to be oriented to, farthest using the resource of robot, realize the mesh of staged design allocative decision
Mark, to adapt to the change of dynamic task.
Further, in the step S22, broker machines people is responsible for issue auction information to the auction machine in communication radius
People, waits to be feedback；The broker machines people of each task point j (j=1,2 ..., N) grasps the relevant auction information of task point j,
The message includes：Suitable machine required for task point j positions, the rate of change of task point j, task point j quantity of states and task point j
Device people's quantity s.The quantity of state of task point is presented exponential function form with the time to be changed.
Suitable machine number s required for task point jjIt is defined as：It is assumed that there is w robot causing
AndRobot optimal number s values are required for task point j：
w≤sj≤ w+2 (formula four)
Robot i (i=1,2 ..., M) there is corresponding communication radius ri(the communication radius of robot can be set to difference
Value), broker machines people is responsible for issue auction information to the auction robot in communication radius.
Further, in the step S23, the auction machine that the individual broker machines people of z (0≤z≤N) issue auction information is received
Device people weighs to these information, and balance standard is：Auction robot is carried out to this z broker machines people issue auction information
Analysis, the present invention is improved traditional auction algorithm, has introduced dynamic economy benefit function, and robot i is calculated and performed
Financial value E after complete certain task point jij(EijIt is real number), and select income highest task point to be submitted a tender.Used is dynamic
State revenue function its be：
Eij=g (k1, Sj(t),αj,βi,n)-h(k2,dij) (formula five)
In formula：
g:Robot i is performed after task point j terminates and acquired an advantage, and is exponential function；
h:Robot i performs the cost of task point j consumption；
K1, k2:Changeable weight parameter, can be set to its corresponding value under different bad borders；
dij:Path length between robot i and task point j；
In above formula, k1 and SjT the product of () is the proportionality coefficient of exponential function, αj,βi, relational expression between n threeIndex in Composition index function, n represent be carrying out task point j robot quantity (in t=0, task point
Performed without robot, now n is for 0)；
H is dijFunction, given parameters k2, then the value of h is by dijDetermine.
There is corresponding executive capability value β in each robot of environmental map for givingi(i=1,2 ..., M), the machine
The ability value of device people i is constant, and the executive capability value between robot can be set to difference.
Further, in the step S24, after broker machines people receives the bid information of auction robot, its information is analyzed,
According to the target that overall efficiency is maximum, target auction robot in selection, and notify the auction robot result of all bids.
Further, in the step S25, the robot of communication blind zone is (due to communication radius in environmental map in map
Limitation there may be do not receive broker machines people issue auction information robot) and not middle target auction robot voluntarily go
Perform the task point away from its nearest neighbours.
Further, in the step S24, broker machines people can be according to target auction robot, tool in pre-defined rule selection
Hold in vivo and be：
The broker machines people of task point j receives njIndividual auction robot bidding message, if nj＞ sj, then it is big according to income
Small sequence, selects (s in the topj- 1) individual auction robot is used as middle scalar robot, and notifies the auction machine of all bids
Device people, if nj＜ sj, then njThe individual all middle scalar robots of auction robot.
Further, in the step S24, robot carries out optimizing by A-star algorithms during traveling to path,
Collision avoidance reasonably can be carried out with other robot collision prevention and with barrier, and search out robot to reach corresponding task point most
Excellent nothing touches path.
Generation task allocative decision, robot starts performance objective task point according to the scheme of distribution, and robot is going to
Position where in real time showing oneself in goal task stroke, while carrying out optimal nothing by A-star algorithms touches path
Search.
In the search procedure of A-star algorithms, using by a cost function come the side of search extension space nodes
Method, the general type of cost function is：
F (i)=g (i)+h (i) (formula six)
In formula:
f(i):Evaluation function of the node i from start node to destination node；
g(i):Actual cost in state space from start node to node i；
h(i):From node i to the estimate cost of destination node optimal path.
The element of searching that optimal nothing touches path is carried out by A-star algorithms, robot run into barrier can reasonably avoid and
Robot will not run into during traveling, and A-star is a kind of effective ways for solving shortest path, improves path planning
Real-time, environmental suitability efficiency.
Following rule is observed in whole implementation procedure by robot：
A) the optimal machine number required for task point j is sj；
B) going to the robot of j-th task point that other task points are not removed before next stage schemes generation；
C) robot will not remove the task point having had been carried out；
D) broker machines people can be according to target auction robot in the rule selection formulated in advance；
E) robot of not middle target auction robot or communication blind zone (does not receive broker machines human hair in communication radius
The robot of the auction information for going out) itself nearest task point can be performed；
F) broker machines people performs the task point of itself agency.
By above-mentioned technical proposal, the present invention ultimately produces carrying into execution a plan for complete set, and obtains all task point quilts
Time required for completing.Time needed for completing all task points is the time that last has been performed task point.
Based on above-mentioned technical proposal, the present invention proposes a kind of multistage auction algorithm towards dynamic task, solves
Dynamic task allocation problem in environment, traditional auction algorithm using primary distribution solve given task, in face of dynamic
There is significant limitations in state task, do not reach expected effect.By the realization of times bidding, for the purpose of time optimal, most
The resource of the utilization robot of big degree, the above method is emulated on VC++ and Csharp platforms, by substantial amounts of reality
Test the simulation results to show, the more traditional auction algorithm of improved auction algorithm more can be good at solving the dynamic in environment
Task, the demand of real-time is met by repeated dispensing, and can provide close optimal solution.
Brief description of the drawings
Fig. 1 is the environmental model figure according to the embodiment of the present invention.
Fig. 2 is the multistage auction algorithm flow chart according to the embodiment of the present invention.
Fig. 3 is the broker machines people's workflow diagram according to the embodiment of the present invention.
Fig. 4 is the auction robot workflow diagram according to the embodiment of the present invention.
Fig. 5 is traditional auction algorithm (single phase auction algorithm) analogous diagram according to the embodiment of the present invention.
Fig. 6 is improvement auction algorithm (multistage auction algorithm) analogous diagram according to the embodiment of the present invention.
Fig. 7 is the multistage auction algorithm and single phase auction algorithm elapsed time comparison diagram according to the embodiment of the present invention.
Specific embodiment
With reference to the accompanying drawings and examples, specific embodiment of the invention is described in further detail.Hereinafter implement
Example is not limited to the scope of the present invention for illustrating the present invention.
The present invention provides a kind of based on improved auction algorithm, and combines A-star algorithm optimum path searches, by machine
Cooperative cooperating solves the problems, such as multiple dynamic tasks in environment between people.
Multistage auction algorithm is improved on the basis of traditional auction algorithm, increases corresponding rule and dynamic is received
Beneficial function.Traditional auction algorithm is applied to the assignment problem for solving static object task, and by benefit and cost parameter point
Not Heng Liang goal task value and robot Executing Cost, design object task is to the auction algorithm of each robot, generation distribution
Scheme, whole system is only once auctioned.
Robot cooperative cooperating is presented as：When robot detects the generation of task point, broker machines people is auctioned by issuing
Information gives auction robot, and auction robot is weighed according to the information of receiving, and is submitted a tender；Broker machines people is according to feedback
Information with the prior rule selection allocative decision formulated, the information that robot can will be appreciated by the process of the task of execution sends
To the communication radius inner machine people of itself, resource-sharing is realized, once after the completion of having task point, idle robot can be by weight
New auction obtains goal task point, goes to assist other robots to perform task point, until all task points are completed.
According in one embodiment of the application, as shown in figure 1, environmental model figure of the present invention, comprises the following steps：
First, rectangular coordinate system is set up to target map, and target map is divided into the map block of multiple equidimensions, each
Map block length and width are equal.
Secondly, in embodiments of the present invention, given target map length and width are all 300 units, then contain ground in map
Segment number 89401.
Can be with specific setting M (M ∈ Z in given environmental map+) individual robot, N (N ∈ Z+) individual task point and B (B
∈Z+) individual barrier.
Distance (formula one) between distance (formula two) and task point and robot between calculating and store tasks point.
Ultimate analysis goal in research of the invention：For robot of the multiple with certain task executive capability, give many
The task that individual attribute is changed over time, sets up the dynamic task allocation strategy of multirobot, optimizes the deadline of all tasks,
Can be weighed with the deadline of last task.
When m (0≤m≤M) individual robot acts on task point j (j=1,2 ..., N), appoint at (t+1) (t >=0) moment
The task status amount formula of business point j is：
As shown in Fig. 2 the multistage auction algorithm flow chart that the present invention is used, details are as follows：
In S201, Detection task first determines the broker machines people of each task point when occurring, and task point j selection principles are
The robot nearest apart from task point j is used as broker machines people.
With reference to shown in Fig. 3, details are as follows for the workflow of broker machines people：
After the message of monitoring system is received, the robot i nearest apart from task point j (j=1,2 ..., N) (i=1,
2 ..., M) be the task point broker machines people.Robot i understands the task specifying information of task point j and it is carried out first
Arrange：Robot optimal number s required for mainly calculating completion task j, obtain task point rate of change αj, task point appoint
Geographical position residing for business quantity of state, task point etc..Then auction information is sent to the auction robot in communication radius, is waited
Feedback.The auction robot for receiving auction information selects income highest task point to be submitted a tender according to self benefits function,
Broker machines people is up to target auction robot in target selection with overall efficiency, and notifies the auction machine of all bids
People.If the quantity n of middle scalar robot reaches the condition of n ＞ s, selection income s robot in the top, machine of not getting the bid
People can reselect closest task point.
In S203, auction robot workflow refers to Fig. 4, and details are as follows：
It is not chosen as broker machines people and is then automatically converted to auction robot, the issue auction letters of broker machines people i first
Cease to the auction robot in communication radius, wait to be feedback.The auction robot of auction information is received according to self benefits letter
Number selection income highest task point is submitted a tender, and broker machines people collects bid information, and is up to system overall efficiency
Target auction robot in target selection.If auction robot successfully gets the bid, start execution task, if failing acceptance of the bid
Robot Selection go to perform nearest task point.If there is task point to be done, idle machine is redistributed according to present case
Device people, until task is completed.
Generation allocative decision, starts execution task, and robot carries out optimizing by A-Star algorithms in traveling to path,
Collision avoidance reasonably can be carried out with other robot collision prevention and with barrier, and search out robot to reach corresponding task point
Optimal nothing touches path.
Once in the presence of task point has been completed, can there is aimless idle machine people, system can be again clapped them
Sell, so as to draw new allocative decision, until all of task point is completed in environment.
Robot cooperated execution task test.
The method of the auction algorithm (multistage auction algorithm) of the improvements mentioned above, there is M robot, N number of task point
And B barrier.Under the constraints of (m is the robot quantity of execution task point j).In order to test
The auction algorithm for using is performing the improvement of dynamic task, is tested using the map of a width of 300 grids of length, contrasts single-order
The effect of section auction algorithm and multistage auction algorithm.
Fig. 5 and Fig. 6 are test case, wherein thick black small circle is broker machines people, robot 3 is the machine of communication blind zone
Device people, it is specific as follows：
Fig. 5 is certain test single phase auction algorithm analogous diagram, and robot 2,4 and 5 is respectively the generation of task point 2,3 and 1
Reason robot, the auction information of broker machines people issue correspondence task point is to the auction robot in communication radius；By the first run
Auction, auction robot 1,6 and 7 is successfully got the bid, and task point 2,3 and 1 is performed respectively.Robot 3 is the robot of communication blind zone
(not in broker machines people communication radius, it is impossible to receive the auction information of broker machines people issue) selection performs distance most
Near task point 1, robot starts performance objective task point after generation task allocative decision, is no longer clapped in implementation procedure
Sell, once there is task point to be performed, idle robot does not assist other robot to go to perform unfinished task point.
Fig. 6 is certain test multistage auction algorithm analogous diagram of identical, and the method for its first sub-distribution and single phase auction
Algorithm distribution method is identical, and multistage auction algorithm feature is to have completed task point once existing, and can there is aimless machine
Device people, algorithm can be redistributed to them, and task point 1 has first been performed in figure, and algorithm divides robot 3,5 and 7 again
Match somebody with somebody, the robot of free time is not wasted, by times bidding, farthest using the resource of robot, realize staged design
The target of allocative decision.
In order to verify the efficiency of the improvement auction algorithm, 8 groups of test datas are have chosen, communication radius is set as 200 lists
Position, k1=1, k2=0.0025；Statistics is as shown in table 1.
Table 1 is testing time statistical form
Simulation result above is depicted as broken line graph, as shown in fig. 7, carrying out comparing result, is introduced towards dynamic task
After corresponding rule and dynamic income function, the whole structure that robot performs dynamic task is greatly improved, by imitative
True test, it was demonstrated that use improved multistage auction algorithm validity towards dynamic task robot.
Finally, the present processes are only preferably embodiment, are not intended to limit the scope of the present invention.It is all
Within the spirit and principles in the present invention, any modification, equivalent substitution and improvements made etc. should be included in protection of the invention
Within the scope of.
Claims (10)
1. a kind of multirobot distributed task scheduling towards dynamic task distributes forming method, it is characterised in that including：
S1：According to information in environmental map, factor and difficult point that task distribution needs to consider are cleared；
S2：When task occurs, based on multistage auction algorithm, task allocative decision is generated, robot is distributed according to the task
Scheme performs task.
2. method according to claim 1, it is characterised in that in the step S1, further include：
S11：A target environment map is given, multiple robots with Mission Capability, category are distributed with the map
Property the task point that changes over time and several barriers；
S12：According to the state change of task point, factor and refractory gold ores that task distribution needs to consider are cleared.
3. method according to claim 2, it is characterised in that in the step S11, described environment is robot work
Environment, the environmental map to giving sets up rectangular coordinate system, and with x-axis to the right as positive direction, y-axis is upwards positive direction to coordinate system,
And the working environment is divided into multiple grids, environmental map is spatially distributed with N (N ∈ Z+) individual task point, M (M ∈ Z+) individual
Robot and B (B ∈ Z+) individual barrier, and the coordinate of each task, robot and barrier is determined respectively.
4. method according to claim 2, it is characterised in that in the step S12, according to the state change of task point,
Task point-like states model is set up, the factor that analysis task distribution needs consider includes task point property parameters and the humanized ginseng of machine
Number, wherein,
Task point property parameters include：The task amount of task point, the status variation rate of task point and task point are on environmental map
The location of；
Robot property parameters include：Movement velocity, robot capability value and the robot location on environmental map.
5. method according to claim 1, it is characterised in that in the step S2, further include：
S21：When detecting task, the nearest robot of chosen distance task point as broker machines people, if apart from several tasks
The artificial same robot i (i=1,2 ..., M) of the nearest machine of point, system can select chosen distance right angle in several task points
The robot i of the nearest task point of coordinate origin is its broker machines people；
S22：Broker machines people issue auction information waits to be feedback to the auction robot in communication radius；
S23:The auction robot for receiving auction information selects income highest task point to be thrown according to self benefits function
Mark；
S24：Broker machines people collects bid information, and target robot performs task in target robot in selection, and notice；
S25：Not middle target auction robot performs nearest task point with the Robot Selection for being in communication blind zone.
6. method according to claim 5, it is characterised in that in the step S22, broker machines people is responsible for issue auction
Information waits to be feedback to the auction robot in communication radius；The broker machines people of each task point grasps the phase of the task point
Auction information is closed, the message includes：The position of task point, the rate of change of task point, the primary quantity of task point and task point institute
The quantity of the suitable machine people of needs.
7. method according to claim 5, it is characterised in that in the step S23, receive z (0≤z≤N) individual agency
The auction robot of robot issue auction information is weighed to these information, and balance standard is：Auction robot is to this z
Auction robot issue auction information is analyzed, and robot is calculated and performed the financial value after certain task point, and selects to receive
Beneficial highest task point is submitted a tender.
8. method according to claim 5, it is characterised in that in the step S24, broker machines people receives auction machine
After the bid information of people, its information is analyzed, according to the target that overall efficiency is maximum, target auction robot in selection, and notified
The auction robot result of all bids.
9. method according to claim 5, it is characterised in that in the step S24, robot leads to during traveling
The optimal nothing for crossing A-star algorithm search arrival task point touches path.
10. method according to claim 5, it is characterised in that in the step S2, when detect completed task point and
During aimless idle machine people, idle machine people can again be auctioned so that idle machine people regains new appointing
Business point, so as to draw new allocative decision, until after all of task point has been performed in environment, stopping auction.
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