CN109583709A - A kind of automatic parking robot group method for scheduling task - Google Patents
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Abstract
The invention proposes a kind of automatic parking robot group method for scheduling task, comprising the following steps: (1) triggers task schedule；(2) application information for extracting each task application obtains the position of the pickup node of each task application, generates its position for placing node；Obtain the position of current all free automatic parking robots；(3) the most short access path of each task application is searched for；The cost topological diagram between each node is generated, and then generates node cost matrix；(4) global path planning is carried out to current each automatic parking robot, and provides E.T.A for client；(5) node cost matrix is combined, using multiple target cost function, current cost and operating cost are assessed, to complete overall cost used in the batch task is at least that target carries out sector planning to each automatic parking robot, obtains the task sequence of each parking robot.The present invention can improve the calculating speed entirely planned to the full extent, cut operating costs.
Description
Technical field
The invention belongs to intelligent scheduling technology fields, are related to a kind of method for scheduling task, especially a kind of robot group
Method for scheduling task.
Background technique
The scheduling of Group Robots intelligent task aims at the intelligent robot task distribution under the conditions of more resource multiple targets,
Reduce multirobot actual operation cost.Specific to Vehicle Routing Problems (VRP), it refers to a certain number of clients, each own
The cargo demand of different number, home-delivery center provide cargo to client, are responsible for sending cargo by a fleet, organize row appropriate
Bus or train route line, target are and to reach that such as distance is most short, cost under certain constraint so that the demand of client is met
The purpose of minimum, minimum consuming time.If the time that demand point reaches vehicle have it is required, Vehicle Routing Problems it
Window limits when middle addition, becomes vehicle routing problem with time windows (VRP with Time Windows, vehicle path planning).
In Vehicle routing problem, other than running cost, cost function will also include being caused due to early to some client
Waiting time and client need service time.
The general method for solving of Vehicle routing problem has rigorous solution and heuritic approach.Accurate resolving Algorithm Xie Chelianglu
Strategy there are three diameter planning problem is main, Lagrange relaxation, column-generation and Dynamic Programming, but the example scale that can be solved
It is very small.There is significant development in this field in recent years, be the heuristic solution of a new generation, includes tabu search algorithm (Tabu
Search), simulated annealing (Simulated Annealing), genetic algorithm (Genetic Algorithm) and door receive
Method (Threshold Accepting) etc. can effectively solve the puzzlement for falling into local optimum.How to be obtained in finite time
The reliable solution of oversize vehicle path planning problem is still a challenge.
Summary of the invention
The purpose of the present invention is to provide a kind of within the limited time to the large-scale problem of the path planning of mobile tool
It is planned in real time, improves the calculating speed entirely planned to the full extent, the method to cut operating costs.
In order to achieve the above object, solution of the invention is:
A kind of automatic parking robot group method for scheduling task, comprising the following steps:
(1) task schedule is triggered；
(2) application information for extracting each task application obtains the position of the pickup node of each task application, generates described appoint
The position of the placement node of business application；Obtain the position of current all free automatic parking robots；
(3) the most short access path of each task application is searched for；The cost topological diagram between each node is generated, and then generates section
Point cost matrix；
(4) global path planning is carried out to current each automatic parking robot, and provides E.T.A for client；
(5) current cost and operating cost are assessed using multiple target cost function in conjunction with the node cost matrix, it is right
Each automatic parking robot is at least that target carries out sector planning to complete overall cost used in the batch task, is obtained each
The task sequence of parking robot.
Task schedule is triggered by the way of period triggering；The period triggering refers to: when every period by setting
Between, trigger a task schedule.
Preferably, the method triggers task schedule using period triggering and event triggering parallel form；The event
The priority of triggering is triggered higher than the period, and the event triggering refers to: when switching station is fully parked with, being not to wait for down
A cycle starts to trigger task schedule.
Using the most short access path of each task application of A* algorithm search in the step (3).
The instant flow of road and current shape are introduced in the step (3) when generating the cost topological diagram between each node
Condition.
Carrying out global path planning to current each automatic parking robot in the step (4) includes: by column-generation point
Branch bound method or approximate algorithm carry out integer programming, generate the global path of each automatic parking robot.
Overall cost in the step (5) includes the actual operation cost and time cost in parking lot.
The actual operation cost in the parking lot includes completing the power consumption of all automatic parking robots of batch task.
The sector planning is carried out using DWA sector planning algorithm in the step (5).
The method also includes step (6): sending corresponding task sequence to each automatic parking robot.
By adopting the above scheme, the beneficial effects of the present invention are: the present invention can effectively solve the problem that in changeable dynamic ring
The problem of Jing Xia automatic parking robot group's task schedule, can move the road of tool to vehicle etc. within the limited time
The large-scale problem of diameter planning is planned in real time, improves the calculating speed entirely planned to the full extent, reduces such as parking lot
Operation cost.
Detailed description of the invention
Fig. 1 is the flow chart of automatic parking robot group task schedule in one embodiment of the invention；
Fig. 2 is the schematic diagram of the decision variable of planning layer in the embodiment.
Specific embodiment
The present invention will be further described with reference to the accompanying drawings.
The invention proposes a kind of automatic parking robot group method for scheduling task, this method can be according to realtime running
Information, carry out automated parking system Group Robots task schedule, it is ensured that customer's vehicle delay cost, parking robot investment at
This is optimal, and utilizes parking robot resource, optimizing management cost to greatest extent.The automatic parking robot group task schedule
Method the following steps are included:
(1) parallel form is triggered by period triggering and event and triggers task schedule；
Wherein, period triggering refers to every cycle time by setting, triggers a task schedule, such as uses one minute
As the triggering period.The priority of event triggering is triggered higher than the period, and event triggering is referred to when switching station is fully parked with,
Next cycle is not to wait for start to trigger task schedule.
(2) application information for extracting each task application, obtain the pickup node of each task application position (in bicycle parking,
That picks up that node refers to client's bicycle parking puts vehicle place；For picking up the car, picks up node and refer to being stored in customer's vehicle
Node in parking lot), generate the position of the placement node of the task application；Determine current all free automatic parking machines
Device people (free automatic parking robot refers to that current state is the free time, not the automatic parking robot of task) exists
World coordinates on Vectormap high-precision map.
In the present invention, the placement node of bicycle parking task application can search for most short access using identical with next step
The method in path generates, such as select distance in parking lot it is current pick up the nearest parking stall of node as placement node；It picks up the car
Placement node can generate at random.
(3) the most short of each task application is searched for by A* algorithm in basis position of each discrete nodes on high-precision map
(the most short access path is automatic parking robot from above-mentioned pickup node to the shortest traveling road for placing node to access path
Diameter)；The instant flow of road and traffic status are introduced, generates the cost topological diagram between each node, and then generate node cost square
Battle array.
Wherein, cost matrix is obtained based on cost topological diagram, and that generate in cost topological diagram is one between adjacent node
The distance between a cost, such as adjacent node, and each cost value is not necessarily two neighboring node in node cost matrix
Between.
(4) plan as a whole resource, the global path planning based on high-precision map is carried out to current each automatic parking robot, and
E.T.A (only needing to provide E.T.A for the client of pick-up) is provided for user.
(5) in conjunction with the node cost matrix, using multiple target cost function (residence time of vehicle i.e. to be serviced is small,
The path length and time cost of automatic parking robot, automatic parking robot waiting time and high priority work terminate to prolong
The slow time), assess current cost and operating cost, to each automatic parking robot with complete synthesis used in the batch task at
This at least carries out sector planning for target, obtains the task sequence of each parking robot.
(6) corresponding task sequence is sent to each automatic parking robot.
Fig. 1 show the flow chart of the present embodiment automatic parking robot group method for scheduling task.
In the present embodiment, a central garage parking radiates multiple switching stations, facilitates it according to each block actual demand and item
Part is arranged.In addition to each exchange tiny node for service, terminal is also set up, when guaranteeing high service request rate, exchange
Shared parking stall in standing can obtain quick release.The switching station of standard can set up four bicycle parking areas, two pick-up areas；Stop in center
Every layer of garage is equipped with robot rest area (chargeable), robot maintenance area.When next time robot electric quantity is not enough to complete to adjust
Degree task will go to rest area to charge.
In order to facilitate the path planning of automatic parking robot and its in parking lot, (parking lot refers to specifically storing vehicle
Place.Client's bicycle parking is that vehicle is removed from switching station by automatic parking robot later car stowage in switching station
Transport in parking lot) in positioning, parking lot is divided according to a certain size rectangle, and each rectangle is regarded as
One node.The state and attribute of node safeguarded by two tables, node state table by node i d, Group_id, coordinate, occupy shape
State composition, the attribute for the node cluster that Group groups of attribute list descriptions are made of multiple child nodes, i.e. rest area, maintenance area, central vehicle
One in library, switching station, terminal etc., and record the other informations such as the node cluster geographical location, maintenance state.
When parking, after client reaches switching station, the information such as access time and place are submitted by APP or interaction board, and scan
Two dimensional code confirmation, after server obtains client application, more new task application and the task application for storing current time are to be triggered
After be scheduled.When pick-up, client reaches switching station and proposes the task application picked up the car, and the pickup node of customer's vehicle is at this time
(i.e. its placement node in bicycle parking task before) known, this method generate the position of the placement node of customer's vehicle.
The scheduler task application once stopped includes two nodes, the two nodes are to pick up node and placement node.
When bicycle parking, picks up node and generated by the two-dimensional barcode information that client is submitted, place node and generated by system according to current state.It takes
Che Shi, system can store placement node of the customer's vehicle in bicycle parking before, and customer's vehicle picks up when which is this pick-up
The pickup node of customer's vehicle can be directly acquired when taking node, therefore picking up the car, and is generated and placed according to the idle condition in parking lot
Node notifies client to pick up the car to the placement node.
In the present embodiment, is triggered using the period and the event triggering parallel form triggering based on switching station loading condition is appointed
Business scheduling.Here why take two kinds of triggering mode parallel forms to be triggered, be because only the period triggers task schedule
It has certain problems, i.e., since the number for the switching station placed for client is limited, may occur also in peak time
The case where switching station is fully parked with when not arriving the timing node of a cycle, at this moment will trigger immediately task schedule (i.e. with
The mode of event triggering) rather than until the timing node for reaching next cycle goes triggering task schedule again.
The generation strategy of above-mentioned placement node is divided into two kinds.Parking lot is divided into two parts, one is temporary parking
Area, referred to as terminal, the other is normal parking area, the position of terminal is being closed at switching station.When the triggering of scheduling
Mode is when triggering in the period, and system can be in the placement node of normal parking area generation vehicle.When to be loaded based on switching station
When the event triggering of condition, illustrate currently to be the peak time stopped, so needing rapidly the vehicle being in switching station
It removes, placement node can be generated in closing on switching station to reduce the time of automatic parking robot on the way at this time
Turn to carry out interim storage at station.
In the present embodiment, to the task application that (in the present embodiment, the period of period triggering is one minute) generates per minute
It is scheduled, according to the position of discrete nodes each on high-precision map, by A* algorithm etc., searches for its shortest path, introduce road
The instant flow in road and traffic status generate cost topological diagram and node cost matrix.Comprehensive affirming service cost matrix, such as up and down
The stair number of plies, hourage etc., and consider that high priority work terminates the multiple targets such as delay time, it is ready for planning.By working as
Preplanning restrictive condition carries out integer programming by column-generation branch and bound method or approximate algorithm, solves small-scale path planning
Problem, generate each automatic parking robot task sequence (i.e. an automatic parking robot a vehicle stop it is good after can stand
A next pick-up node is gone to go to leave a vehicle at quarter, task arrangement is only to arrange out its work to each automatic parking robot
Make in proper order) and global path (referring to the specific path that automatic parking robot is completed to walk needed for the task arrangement of above-mentioned generation), and
To each automatic parking robot send assignment instructions (send here be task specific path, i.e., informing automatic parking machine
Which position people goes to, and what path to go to the position by).Parallel form is triggered by event triggering and period,
Carry out above-mentioned steps.Each automatic parking robot by high-precision map and Real-time modeling set and is determined under global path requirement
Position carries out sector planning, algorithm DWA sector planning algorithm therein etc..
The dispatching method is in addition to the shortest path operation cost for considering economic dimensions, it is also contemplated that the limited sharing of serving size provides
Source (switching station parking stall) cost.The core of scheduling is to make position of always having a surplus in switching station.Meanwhile consumption of travelling between considering node
When outside, it is also contemplated that each node serve is time-consuming, target location number of plies difference bring elevator transfer time-consuming etc. in sky parking.
In extraction-placement node, terminal (Transfer) setting is added, facilitates vehicle in switching station to recall immediately, has been not take up
Limit resource.In addition, when request of picking up the car is clashed with bicycle parking request (such as vacant automatic parking robot quantity is inadequate), then
Comprehensive descision node load rate and delay of picking up the car, are delayed in acceptable situation picking up the car, if node load rate is excessive, because will
Service is influenced, therefore first carries out bicycle parking, otherwise first carries out pick-up.
In the present embodiment, the meaning of each system parameter is as follows:
N: task quantity
M: robot quantity
V={ 1 ..., m }: automatic parking robot set uses v index
S={ 1 ..., m }: the pick-up point set of automatic parking robot
F={ m+2n+1 ..., 2m+2n }: automatic parking robot puts vehicle point set
P+={ m+1 ..., m+n }: task extracts point set
P-={ m+n+1 ..., m+2n }: task places point set
P={ P+, P-}: P is the set that task extracts point and task set-point
H+Indicate the task pickup point of high priority, which shows that having partial task in all tasks is Gao You
First grade task Nv=P ∪ { S (v), F (v) }: NvIndicate the set of v robot traversed a little, P indicates automatic parking machine
The set of the pick-up point and set-point of people, S (v) indicates automatic parking robot, and point is (referred to as before stopping and putting vehicle vehicle
Starting point) set, F (v) indicates that automatic parking robot is completed to pick up the car and put gone point (referred to as terminating point) after vehicle task
Set Transfer={ 1 ..., q }: Transfer indicate terminal node set, wherein each element is respectively indicated from serial number
1 arrives the q transfer tiny node of q
[ei, ui]: each node time window to be serviced can unify setting ui=ei+λ(eiIt indicates to allow to service most in i-node
At the time of early；uiIt indicates to allow the moment the latest in i-node service；λ indicates to allow total time in i-node service)
tij: the average hourage of node i to node j
hij∈ { 0,1 }, i, j ∈ P: high-priority task marks (hijIndicate from i-node pick up the car to j node put vehicle this
Whether task is high-priority task, if it is just for 1 be otherwise 0) (introduce high priority grade task purpose be, it is ensured that in
Turn station moment holding surplus calls automatic parking robot that the vehicle in terminal is removed when position deficiency remaining in terminal,
The task that vehicle is removed is high-priority task, i.e., preferentially to complete)
Statusv: the battery allowance parameter of the v automatic parking robot
λ4: average minute clock power consumption
λ1, λ2, λ3: (objective function of setting includes three parts to the time cost (member/per minute) of planning: first is that parking lot
Operation cost cost, this amount completes the respective time of a collection of task with all automatic parking robots and indicates；First is that not
The cost of the waiting time of completion task；First is that the cost of the waiting time of high top grade；λ1, λ2, λ3It is these three costs respectively
Coefficient) planning variable:
01 planning variable, v ∈ V.i, j ∈ Nv, i ≠ j (Indicate v-th of automatic parking robot whether from
I-node goes to j node, and if it is being just assigned a value of 1,0) on the contrary is
Ti: automatic parking robot reaches the time of i-th of node
Fig. 2 show decision variable schematic diagram in the present embodiment.
Model planning is as follows:
(TjIndicate automatic parking machine
People reaches the time of j-th of node)
(above formula makes be averaged transit time, single node service time of node minimum, and the waiting time of high-priority task
It is most short)
Subject to:
(formula indicates that only an automatic parking robot goes to take to any task)
(formula limits automatic parking robot cannot be in a node
It stops, for example an automatic parking robot cannot stop always after completing a task in the place that it puts vehicle, terribly
It is enabled to go to a next pick-up place either its relaxing area)
(formula indicates to any one automatic parking robot, from starting point,
An only access) (It indicates from node (i.e. S(v)Node, that is, the starting point of parking robot) leave for its pick-up
Place)(formula, which limits a terminating point, can only have an automatic parking robot to go to,
Cannot have two or more automatic parking robots while go to the same terminating point) (Indicate automatic parking machine
Device people completes to return to its terminating point after task)
(formula limits each task only by an automatic parking robot
Complete) (Indicate that automatic parking robot goes to its corresponding placement node after i-node pick-up)
(formula indicates that all tasks must be completed) (WithMeaning it is identical)
Ti+tI, n+i≤Tn+i, i ∈ P+, v ∈ V (tI, n+iIndicate the average hourage of node i to node n+i；Tn+iIt indicates
At the time of automatic parking robot reaches the n-th+i nodes) (formula carries out the limitation of task hourage)
(formula carries out each node hourage limitation)
ei≤Ti≤ui, i ∈ Nv, (formula indicates that the time of automatic parking robot arrival i-node will be in permission to v ∈ V
In range)
(formula indicates whether v-th parking robot from i-node goes to j to save
Point, if it isBe assigned a value of 1, otherwise for 0)
(residence time of vehicle i.e. to be serviced is small, automatic parking robot using multiple target cost function by the present invention as a result,
Path length and time cost, the automatic parking robot waiting time and high priority work terminate delay time) come assess weight
The long-time operating performance of scheduling strategy maximizes optimization customer service and operation cost.The present invention uses rescheduling strategy
Handle the new task of oversize vehicle path planning problem and Dynamic trigger.Large-scale rescheduling strategy problem is arrived according to task
Small-scale subproblem is divided into up to the time.The dispatching method can arbitrarily use accurate resolving Algorithm, and (such as column-generation branch and bound is calculated
Method) or approximate algorithm (such as auction algorithm) to strategy carry out Long-term planning.Since this is based on event driven rescheduling strategy,
The working strategies that the sum that i.e. rescheduling is newly submitted is not carried out can significantly be quickly obtained the feasible solution of Large-scale Optimization Problems, have
Preferable effect of optimization.
This hair can be understood and applied the above description of the embodiments is intended to facilitate those skilled in the art
It is bright.Person skilled in the art obviously easily can make various modifications to these embodiments, and described herein
General Principle is applied in other embodiments without having to go through creative labor.Therefore, the present invention is not limited to implementations here
Example, those skilled in the art's announcement according to the present invention, improvement and modification made without departing from the scope of the present invention all should be
Within protection scope of the present invention.
Claims (10)
1. a kind of automatic parking robot group method for scheduling task, it is characterised in that:
(1) task schedule is triggered；
(2) application information for extracting each task application obtains the position of the pickup node of each task application, generates the task Shen
The position of placement node please；Obtain the position of current all free automatic parking robots；
(3) the most short access path of each task application is searched for；The cost topological diagram between each node is generated, and then generates node generation
Valence matrix；
(4) global path planning is carried out to current each automatic parking robot, and provides E.T.A for client；
(5) current cost and operating cost are assessed, to respective using multiple target cost function in conjunction with the node cost matrix
Dynamic parking robot is at least that target carries out sector planning to complete overall cost used in the batch task, obtains each park
The task sequence of robot.
2. automatic parking robot group according to claim 1 method for scheduling task, it is characterised in that: touched using the period
The mode of hair triggers task schedule；
The period triggering refers to: every cycle time by setting, triggering a task schedule.
3. automatic parking robot group according to claim 2 method for scheduling task, it is characterised in that: the method is adopted
Task schedule is triggered with period triggering and event triggering parallel form；
The priority of the event triggering is triggered higher than the period, and the event triggering refers to: having been stopped in switching station
Man Shi is not to wait for next cycle and starts to trigger task schedule.
4. automatic parking robot group according to claim 1 method for scheduling task, it is characterised in that: the step
(3) using the most short access path of each task application of A* algorithm search in.
5. automatic parking robot group according to claim 1 method for scheduling task, it is characterised in that: the step
(3) the instant flow of road and traffic status are introduced in when generating the cost topological diagram between each node.
6. automatic parking robot group according to claim 1 method for scheduling task, it is characterised in that: the step
(4) carrying out global path planning to current each automatic parking robot in includes:
Integer programming is carried out by column-generation branch and bound method or approximate algorithm, generates the global road of each automatic parking robot
Diameter.
7. automatic parking robot group according to claim 1 method for scheduling task, it is characterised in that: the step
(5) overall cost in includes the actual operation cost and time cost in parking lot.
8. automatic parking robot group according to claim 7 method for scheduling task, it is characterised in that: the parking lot
Actual operation cost include complete all automatic parking robots of batch task power consumption.
9. automatic parking robot group according to claim 1 method for scheduling task, it is characterised in that: the step
(5) sector planning is carried out using DWA sector planning algorithm in.
10. automatic parking robot group according to claim 1 method for scheduling task, it is characterised in that: the method
Further include step (6): sending corresponding task sequence to each automatic parking robot.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110046818A (en) * | 2019-04-15 | 2019-07-23 | 广州高新兴机器人有限公司 | A kind of multi-robot Task Allocation |
| CN111341136A (en) * | 2020-02-11 | 2020-06-26 | 浙江吉利汽车研究院有限公司 | Passenger-riding parking method, system and storage medium based on vehicle-road cooperation |
| CN112101747A (en) * | 2020-08-28 | 2020-12-18 | 上海交通大学 | Unmanned ship interception task allocation method based on tabu consensus auction algorithm |
| CN112926779A (en) * | 2021-03-01 | 2021-06-08 | 汇链通供应链科技(上海)有限公司 | Intelligent scheduling system and method based on path planning |
| CN112987721A (en) * | 2021-02-01 | 2021-06-18 | 哈尔滨工业大学 | Multi-AGV dispatching package and fusion method of global planning and local planning thereof |
| CN113128938A (en) * | 2021-05-19 | 2021-07-16 | 广州赛特智能科技有限公司 | Robot moving path planning method |
| CN113505931A (en) * | 2021-07-19 | 2021-10-15 | 温州大学 | Charger robot dynamic scheduling optimization method based on genetic algorithm |
| WO2022237323A1 (en) * | 2021-05-13 | 2022-11-17 | 灵动科技（北京）有限公司 | Scheduling system and method for robot, robot, and customization method |
| CN116755866A (en) * | 2023-08-16 | 2023-09-15 | 中移(苏州)软件技术有限公司 | Resource scheduling method and device, electronic equipment and readable storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105069530A (en) * | 2015-08-13 | 2015-11-18 | 肇庆学院 | Multi-robot task allocation method based on multi-objective optimization |
| CN107727099A (en) * | 2017-09-29 | 2018-02-23 | 山东大学 | The more AGV scheduling of material transportation and paths planning method in a kind of factory |
-
2018
- 2018-11-09 CN CN201811329205.9A patent/CN109583709A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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