CN111928849B - Multi-medical delivery robot real-time path planning method - Google Patents

Multi-medical delivery robot real-time path planning method Download PDF

Info

Publication number
CN111928849B
CN111928849B CN201911326135.6A CN201911326135A CN111928849B CN 111928849 B CN111928849 B CN 111928849B CN 201911326135 A CN201911326135 A CN 201911326135A CN 111928849 B CN111928849 B CN 111928849B
Authority
CN
China
Prior art keywords
robot
time
task
area
constraint
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201911326135.6A
Other languages
Chinese (zh)
Other versions
CN111928849A (en
Inventor
何舟
张瑞杰
胡兴律
汤伟
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shaanxi University of Science and Technology
Original Assignee
Shaanxi University of Science and Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shaanxi University of Science and Technology filed Critical Shaanxi University of Science and Technology
Priority to CN201911326135.6A priority Critical patent/CN111928849B/en
Publication of CN111928849A publication Critical patent/CN111928849A/en
Application granted granted Critical
Publication of CN111928849B publication Critical patent/CN111928849B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
    • B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Development Economics (AREA)
  • Automation & Control Theory (AREA)
  • Mechanical Engineering (AREA)
  • Game Theory and Decision Science (AREA)
  • Robotics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Marketing (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)

Abstract

A multi-medical delivery robot real-time path planning method comprises the following steps; 1) modeling a static environment of a hospital by adopting a time Petri network, and solving a multi-robot multitask shortest path track with time window constraint by using an integer linear programming problem in combination with the mathematical characteristics of the Petri network; 2) aiming at minimizing the sum of the paths of the robots, the mathematical model established in the step 1) is utilized to distribute tasks for the robots, plan the path track of each robot, and complete the sum of the shortest path tracks of all distribution tasks on time on the basis of avoiding forbidden areas. The invention can plan the path and the track of the robot for executing tasks on different floors, thereby effectively reducing the heavy work load of medical staff for delivering articles.

Description

Multi-medical delivery robot real-time path planning method
Technical Field
The invention relates to the technical field of logistics distribution, in particular to a real-time path planning method for a multi-medical distribution robot.
Background
In recent years, robots are one of the most popular research fields of human beings, and have wide applications in various fields of agriculture, industry and medicine. With the progress of the related research of robots, how to perform task allocation and path planning on a multi-robot system is a research foundation for realizing multi-robot cooperative work and is also a research hotspot at present.
In recent years, Petri net-based robot path planning has been a focus of research. The research of a large number of researchers focuses on how to perform deadlock prevention, obstacle avoidance and multi-robot work coordination on the basis of optimizing indexes such as a traveling distance and energy consumption. Costelha and Lima propose a generalized random Petri net model to handle mission planning of robots. Assuming that the capacity of some areas is limited, in order to avoid collisions, a robot deadlock prevention strategy is proposed. In the work of Mahulea and Kloetzer, the running environment of a robot is divided into a plurality of areas, the connection between each area and the two areas is represented by a Petri net library and a transition respectively, a method for finding the shortest path of a mobile robot team based on Boolean specifications is provided, when the robot deviates from a planned orbit, a path can be updated through an online monitoring program, tasks based on Boolean values are converted into linear constraints by modeling the environment in a state machine, and an Integer Linear Programming Problem (ILPP) is provided for solving the motion trail of a group of mobile robots.
However, in real-time systems, the task area is always time constrained. The robot needs to reach all task areas and must reach the corresponding task area within a specified time range to perform a task. Current research efforts on path planning do not meet the above-mentioned practical task requirements.
Disclosure of Invention
In order to solve the technical problems, an object of the present invention is to provide a real-time path planning method for a multi-medical delivery robot, which can plan path trajectories for the robot to perform tasks on different floors, thereby effectively reducing burdensome work burden of medical staff for delivering articles.
In order to achieve the purpose, the invention adopts the technical scheme that:
a multi-medical delivery robot real-time path planning method comprises the following steps;
1) modeling a static environment of a hospital by adopting a time Petri network, and providing an Integer Linear Programming Problem (ILPP) to solve a multi-robot multitask shortest path track with time window constraint by combining with the mathematical characteristics of the Petri network;
2) aiming at minimizing the sum of the paths of the robots, the mathematical model established in the step 1) is utilized to distribute tasks for the robots, plan the path track of each robot, and complete the sum of the shortest path tracks of all distribution tasks on time on the basis of avoiding forbidden areas.
The step 1) utilizes a time Petri network to model hospital environments aiming at different environment layouts of different hospitals, and the specific modeling rule is as follows:
1) a Petri network is introduced to model the actual environment of a department ward of a hospital, and a unit is divided into a plurality of areas pi ═ pi firstly1,Π2，...，Π|П|}; each region corresponding to a library of Petri netsP ═ P1,p2…pn}; the small black points in the library are called as Token, the positions of the robots in the current area are shown, the Token number shows the number of the robots in one area, and when more than three robots are simultaneously in one area, the number can be directly shown; two adjacent communicating libraries are connected by transitions, and the robot realizes the moving process by exciting the corresponding transitions t;
2) initial identification M of Petri net0Indicating the initial position distribution of the robot, MhIndicating the position distribution of the robot after finishing all tasks; robot transmits a series of transitions t from initial positioni1,ti2,…tihThe transition sequence transmitted at the final position is denoted by the symbol σ;
3) respectively weighting distance information w and time information theta on transitions between two adjacent libraries; the values of w and theta are determined according to the real-time traffic conditions of roads in different time periods of the actual hospital.
The path planning method can plan the path track of the robot in real time according to the road traffic condition fed back by the monitoring device, and ensures that the robot arrives and executes the distribution task at the task point with time requirement within the specified time range.
The forbidden area, the task area and the corresponding time constraint in the robot task execution environment are expressed by Boolean logic, and specifically are as follows:
by a Boolean logic symbol crossing (A), and a V-interval
Figure BDA0002328437910000033
In a real time representation system, a running environment is divided into pi regions, and a task region gamma is divided into a task region set gamma required to be executed in a trackt，Γt＝{Π1，Π2，...，Π|Π|}; and the region set gamma of the robot which finally requires parkingf，Γf＝{π1，π2，...，π|Π|Let the initial time be τ0，[Ei，Li]Task area II representing time constraintsiTime scale ofAnd (5) enclosing.
The set of tasks can be described as
Figure BDA0002328437910000031
Figure BDA0002328437910000032
It indicates that the robot group is at tau0Starts working at any moment and is in a region II in the running process of the robot4Always forbid passing, and the robot is required to be in the time range [ E ]1，L1]Internal access region II1Organic robot in time Range [ E2，L2]Inner access area Π2Not allowing robot to stop in region II2。
The integer linear programming model of the path planning problem is represented as:
Figure BDA0002328437910000041
wherein σ ═ (σ)j1，σj2，...，σjk) Representing the emission sequence of the robot j in the Petri model, correspondingly representing the motion track of the robot j, wherein W represents the distance between two adjacent communication areas, and the objective function of the model is that the sum of the paths of all the robots is the shortest; constraints (a-b) ensure the correctness of the robot transmission sequence, Mj,iRepresenting the position identification of the jth robot in the ith step, and C representing the correlation matrix of the PN model; constraint (c) represents the record of the time of day, τ, during the operation of each robotj，iRepresenting the time of the jth robot at the ith step; constraint (d) ensures xγThe correctness of the test; vector quantity
Figure BDA0002328437910000042
Figure BDA0002328437910000051
Representing a series of binary variables, when region IIiIs a region of interest
Figure BDA0002328437910000052
Otherwise
Figure BDA0002328437910000053
When region piiIs a region of interest
Figure BDA0002328437910000054
Otherwise
Figure BDA0002328437910000055
Wherein alpha isiΓ → { -1,0,1} has the following meaning:
Figure BDA0002328437910000056
when gamma is not in
Figure BDA0002328437910000057
In (c), constraint (d) has no effect thereon; when gamma is in
Figure BDA0002328437910000058
In (A), (B)
Figure BDA0002328437910000059
In that
Figure BDA00023284379100000510
In) aiThe value of (gamma) ensures xγThe constraint (e-f) further strengthens the binary variable in the region x which ultimately requires access∏iWhere k is the number of robots, VΠi∈{0，1}1·|p|II is an area of interestiCharacteristic vector n ofi∈П,VΠi(pr) 1 when piiIs the area that ultimately requires access, otherwise VΠi(pr) For a final identifier M, if V, 0γM > 0, then γ is alive at the final marker M, constraints (g-H) ensure that the robot can visit the task area in the trajectory, Z is a binary variable, H is a very large value, soAnd the constraint (g-h) ensures that at least one robot performs the task to the task area, and the constraint (j-k) ensures that at least one robot performs the same task in a specified time range in one task area.
The invention has the beneficial effects that:
the path trajectory calculated by the Integer Linear Programming Problem (ILPP) proposed by the present invention is the shortest path trajectory that ensures that all tasks are executed within a specified time frame. However, in a practical hospital environment, road traffic conditions vary with time intervals, and a congested road segment may exist in a shortest path trajectory calculated by a mathematical model proposed in the present invention, and the actual time taken to pass through the congested road segment is much greater than the predicted traffic time, thereby causing some tasks not to be performed within a specified time range. Aiming at the situation, the traffic index of the road is introduced, and the road traffic condition fed back by the monitoring center in real time is continuously updated in the Petri network model, so that the operation algorithm plans the path track in real time according to the road real-time traffic condition, and the condition that certain tasks cannot be accessed in a specified time range due to the fact that the robot is trapped in a crowded road section is avoided.
Drawings
FIG. 1 is a perspective view of a department and a pharmacy in a hospital.
FIG. 2 is a time Petri net modeling diagram for a portion of a hospital environment.
Fig. 3 is a process flow of a hospital part distribution task.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
1) Modeling a static environment of a hospital by adopting a time Petri network, and providing an integer linear programming model (ILPP) to solve the shortest path track of multi-robot multi-band time window constraint task points by combining with the mathematical characteristics of the Petri network;
2) aiming at minimizing the sum of the paths of the robots, performing task allocation on the robots by using the mathematical model established in the step 1), planning the path track of each robot, and ensuring that all distribution tasks are completed within a specified time range on the basis of avoiding forbidden areas;
aiming at different environment layouts of different hospitals, a Petri network is utilized to model the hospital environment, and the specific modeling rule is as follows:
(1) dividing hospital into several regions II (II) with department ward as unit1,П2，...，Π|П|Each region corresponds to a library P ═ P1,p2…pn}. The token in the library indicates the position of the robot in the current area; two adjacent communicating libraries are connected by transitions, and the robot realizes a moving process by exciting the corresponding transitions; the movement of the robot between different floors is realized by exciting the connected transition of the elevator rooms of different floors;
(2) initial identification M of Petri net0Representing the initial position distribution of the robot, and transmitting a series of transitions sigma ═ t from the initial state according to the actual task requirements i1,t i2,…tih}，MhIndicating the position distribution of the robot after finishing all tasks;
(3) respectively carrying out information on transition distance w and weighting time theta between two adjacent libraries; the values of w and theta are determined according to road real-time traffic conditions of different hospitals and different time periods.
The passing time of the road is calculated according to the formula (1):
Figure BDA0002328437910000071
wherein VFruit of Chinese wolfberryThe actual running speed of the robot on the road is represented and related to the real-time traffic situation of the road, and is calculated by the following formula (2):
Vfruit of Chinese wolfberry＝ξ*VForehead (forehead) (2)
VForehead (forehead)Representing a nominal speed of the robot; xi represents the traffic index of the road and is divided into three levels of normal traffic, light congestion and severe congestion. The numerical value is continuously changed according to the road traffic condition fed back by the monitoring device, so that the path track of the robot is planned in real time. If an emergency happens to a certain road in the pre-planned track, the road is seriously congested without congestionAnd (4) passing, feeding back real-time information to a dispatching center according to the road monitoring device, wherein the passing index xi of the road is changed into 1 x 10-4The actual speed of the robot on the road is then approximately 0. In order to ensure that the remaining task points have the robots to arrive and execute the distribution tasks within the specified time range, the dispatching center re-routes the robots, and the tasks can be completed on time.
Figure BDA0002328437910000072
Figure BDA0002328437910000081
The forbidden area, the task area and the corresponding time constraint in the task execution environment of the robot are expressed by Boolean logic, and specifically are as follows:
by a Boolean logic symbol crossing (A), and a V-interval
Figure BDA0002328437910000085
Representing the trajectory and the finally requested task region Γ ═ Γ in real time systemst∪Γf(ii) a Set gammatPi denotes a task area with a time requirement that is required to be executed in the trajectory; set gammaf＝{π1，π2…，π|П|And represents an area where the robot stops in time within a specified time range.
Assume an initial time of τ0，[Ei，Li]Representing a time-constrained mission region |iTime range of (d). Thus, a set of tasks can be described as
Figure BDA0002328437910000082
Figure BDA0002328437910000083
Indicates that the robot is at tau0The work is started at that moment. Region II in the running process of the robot4Always forbid passing, and the robot is required to be in the time range [ E ]1，L1]Internal access region II1Organic robot in time Range [ E2，L2]Internal access region II2Not allowing robot to stop in region II2。
The integer linear programming model of the path planning problem is represented as:
Figure BDA0002328437910000084
Figure BDA0002328437910000091
wherein σ ═ (σ)j1，σj2，...，σjk) Representing the emission sequence of the robot j in the Petri model, correspondingly representing the motion trail of the robot j, wherein the objective function of the model is that the sum of the paths of all the robots is shortest; constraints (4a-4b) ensure the correctness of the robot emission sequence, Mj,iRepresenting the position identification of the jth robot in the ith step, and C representing the correlation matrix of the PN model; the constraint (4c) represents the record of the time of day, τ, during the operation of each robotj，iRepresenting the time of the jth robot at the ith step; constraint (4d) ensures xγThe correctness of the test; vector quantity
Figure BDA0002328437910000092
Figure BDA0002328437910000093
Representing a series of binary variables, when region ΠiIs a region of interest
Figure BDA0002328437910000094
Otherwise
Figure BDA0002328437910000095
When region piiIs a region of interest
Figure BDA0002328437910000096
Otherwise
Figure BDA0002328437910000097
Wherein alpha isiΓ → { -1,0,1} has the following meaning:
Figure BDA0002328437910000098
when gamma is not in
Figure BDA0002328437910000099
Medium, on which constraint (4d) has no effect; when gamma is in
Figure BDA00023284379100000910
In (A), (B)
Figure BDA00023284379100000911
In that
Figure BDA0002328437910000101
In) aiThe value of (gamma) ensures xγThe correctness of the operation. Constraints (4e-4f) further strengthen the binary variable in region x which ultimately requires accessΠiWhere k is the number of robots. VΠi∈{0，1}1·|p|II is an area of interestiCharacteristic vector n ofi∈∏,VΠi(pr) 1 when piiIs the area that ultimately requires access, otherwise VΠi(pr) 0. For the final M, if VγM > 0, then γ is alive at the final marker M. Constraints (4g-4h) ensure that the robot can access the task area in the trajectory. Z is a binary variable and H is a very large value, so that constraints (4g-4H) guarantee that at least one robot performs a task to the task area. Constraints (4j-4k) ensure that at least one robot in a task area performs a phase task within a specified time frame.
Example (b):
the invention provides a multi-medical delivery robot real-time path planning method based on a time Petri network, which is implemented by taking a certain Hospital in Western-style safety as an example and specifically comprises the following steps:
step 1, a reasonable Petri network model is established according to a researched hospital environment figure 1, wherein the model is shown in figure 2, and has 73 places and 162 transitions.
Dividing the hospital environment into a plurality of areas according to the following dividing principle: one room (ward, pharmacy, elevator room) is a depot, and the corridor is divided into a plurality of areas according to the room. The interconnected zones are connected by transitions, and the distance w between the zones, the transit time theta, is weighted on the transitions. The transit time varies with the real-time traffic information.
Except for the transition distance information shown in the table 1, the rest of the transition distance information of the model II is 1;
TABLE 1 transition distance information
Figure BDA0002328437910000102
Figure BDA0002328437910000111
The time for the robot to go upstairs and downstairs is determined according to the running time and the waiting time of the elevator, through actual investigation, the moving time of the robot on the third floor is 8min, the moving time of the robot on the second floor is 7min, and the moving time of the robot on the first floor is 5 min; the time for taking the medicine is 3min, and the time for delivering the medicine is 2min, which are respectively added to the output transition of a ward and a pharmacy;
in the straight-going process, the acceleration and deceleration in the starting and stopping stages are not considered, the rated speed of the robot in the straight-going process is 25(m/min), the rated speed in the turning process is 1(m/min), the medicine taking and delivering time of the robot is 5min and 2min respectively, and the time information is weighted to the post-position transition of the task area. Calculating the passing time of each transition of the model II in the normal passing process of the road according to the formulas 1 and 2, wherein the rest time is 1 except the time information shown in the table 2;
TABLE 2 transition time information
Figure BDA0002328437910000112
Figure BDA0002328437910000121
According to the environmental layout of the hospital, four drugstores, particle drugstores, western medicine drugstores, traditional Chinese medicine drugstores and decoction drugstores are distributed in a first floor hall and an underground first floor respectively. During non-working period, the robots stay in the rest room on the first floor to wait for working instructions, and the total number of the robots is two.
The daily work content of the distribution robot is shown in fig. three;
the distribution of the morning tasks and the time requirements on a certain day can be expressed as:
Figure BDA0002328437910000122
according to the task, the path track of the robot is solved by adopting the proposed integer linear programming model.
Figure BDA0002328437910000131
Wherein σ ═ (σ)j1，σj2，...，σjk) The emitting sequence of the robot j in the Petri model is represented, and the corresponding motion trail of the robot j is represented. The objective function of the model is that the sum of the paths of all the robots is shortest; constraints (5a-5b) guarantee the correctness of the robot transmission sequence; the constraint (5c) represents the record of the time of each robot in the operation process; constraints (5d-5i) ensure that at least one robot passes through and performs tasks in each task area; constraints (5j-5k) ensure that at least one robot in a task area performs a phase task within a specified time frame.
Solving the integer programming model by combining a Matlab Yalmip tool box with a Gurobi optimization solver, wherein the solving result is as follows:
motion trajectory of robot 1: p64Rest room (7:00)→P65 →P68Western medicine pharmacy (7:02)→P65 →P66 →P69Traditional Chinese medicine pharmacy (7:08)→P66 →P65 →P63 →P67Particle pharmacy (7:15)→P63 →P61 →P52 →P42 →P43 →P44 →P54Ward (7:37)→P44 →P34Ward (7:41)→P44 →P43 →P42 →P52 →P12 →P61 →P63 →P65 →P64Rest room (8:03)
The motion track of the robot 2: p64Rest room (7:00)→P65 →P63 →P61 →P70 →P72 →P73Soup pharmacy (7:14)→P72 →P70 →P12 →P22 →P23 →P3Ward (7:33)→P23 →P24 →P4Ward (7:38)→P24 →P25 →P15Ward (7:43)→P25 →P24 →P23 →P22 →P12 →P52 →P61 →P63 →P65 →P64Rest room (8:04)
The calculation result meets the requirement, all patients receive the medicines of themselves in the specified time range, and the total path track of the robot is 686 m.
The invention plans the path of the problem of multi-band time constraint task points of the multi-medical robot by providing an integer linear programming model, thereby planning a shortest path track for executing corresponding tasks in a specified time range.

Claims (4)

1. A multi-medical delivery robot real-time path planning method is characterized by comprising the following steps;
1) modeling a static environment of a hospital by adopting a time Petri network, and solving a multi-robot multitask shortest path track with time window constraint by using an integer linear programming problem in combination with the mathematical characteristics of the Petri network;
2) aiming at minimizing the sum of the paths of the robots, performing task allocation on the robots by using the mathematical model established in the step 1), planning the path track of each robot, and completing the sum of the shortest path tracks of all distribution tasks on time on the basis of avoiding forbidden areas;
the robot task execution environment comprises a forbidden zone and a task zone, the forbidden zone, the task zone and corresponding time constraint are represented by Boolean logic, wherein the Boolean logic symbol comprises an inverted V shape and a non-V shape
Figure FDA0003540491440000012
Dividing the operating environment into pi areas, and dividing the task area gamma into a task area set gamma required to be executed in the trackt，Γt＝{Π1，Π2…，Π|Π|}; and the region set gamma of the robot which finally requires parkingf，Γf＝{π1，π2…，π|Π|Let the initial time be τ0，[Ei，Li]Representing a time-constrained mission region |iThe time range of (d);
the integer linear programming model of the path planning problem is represented as:
min w*σj，i
Figure FDA0003540491440000011
wherein σ ═ (σ)j1，σj2，…，σjk) Representing the emission sequence of the robot j in the Petri model, correspondingly representing the motion track of the robot j, wherein W represents the distance between two adjacent communication areas, and the objective function of the model is that the sum of the paths of all the robots is the shortest; constraints (a-b) ensure the correctness of the robot transmission sequence, Mj，iRepresenting the position identification of the jth robot in the ith step, and C representing the correlation matrix of the PN model; constraint (c) represents the record of the time of day, τ, during the operation of each robotj，iRepresenting the time of the jth robot at the ith step; constraint (d) ensures xγThe correctness of the test; vector quantity
Figure FDA0003540491440000021
Figure FDA0003540491440000022
Representing a series of binary variables, when region ΠiIs a region of interest
Figure FDA0003540491440000023
Otherwise
Figure FDA0003540491440000024
When region piiIs a region of interest
Figure FDA0003540491440000025
Otherwise
Figure FDA0003540491440000026
Wherein alpha isi: Γ → { -1,0,1} has the following meaning:
Figure FDA0003540491440000027
when gamma is not in
Figure FDA0003540491440000028
In (c), constraint (d) has no effect thereon; when gamma is in
Figure FDA0003540491440000029
In alphaiThe value of (gamma) ensures xγThe constraint (e-f) further strengthens the binary variable in the area that ultimately requires access
Figure FDA00035404914400000210
Where k is the number of robots,
Figure FDA00035404914400000211
II is an area of interestiCharacteristic vector n ofi∈Π，
Figure FDA00035404914400000212
When piiIs the area that ultimately requires access, otherwise
Figure FDA00035404914400000213
For the final M, if VγAnd M is greater than 0, gamma is alive at the final mark M, the constraint (g-H) ensures that the robot can visit the task area in the track, Z is a binary variable, H is a maximum value, so that the constraint (g-H) ensures that at least one robot executes the task to the task area, the constraint (j-k) ensures that at least one robot executes the task in a specified time range in one task area, and theta is weighted time.
2. The real-time path planning method for the multi-medical delivery robot according to claim 1, wherein the step 1) is to model hospital environments by using a time Petri network for different environmental layouts of different hospitals, and the specific modeling rule is as follows:
1) dividing a hospital into a plurality of areas pi ═ pi { pi with department wards as a unit1，Π2，…，Π|Π|Each region corresponds to a library P ═ P1，p2…pn-the tobken in the library indicates the position of the robot in the current area; two adjacent communicating libraries are connected by transitions, and the robot realizes the moving process by exciting the corresponding transitions t;
2) initial identification M of Petri net0Indicating the initial position distribution of the robot, MhIndicating the position distribution of the robot after finishing all tasks; robot transmits a series of transitions t from initial positioni1，ti2，…tihThe transition sequence transmitted at the final position is denoted by the symbol σ;
3) respectively acquiring the information of transition distance w and weighting time theta between two adjacent libraries; the values of w and theta are determined according to road real-time traffic conditions of different hospitals and different time periods.
3. The real-time path planning method for the multi-medical delivery robot as claimed in claim 1, wherein the path planning method plans the path trajectory of the robot in real time according to the road traffic condition fed back by the monitoring device, and ensures that the robot arrives and performs the delivery task at the task point with time requirement within a specified time range.
4. The multi-medical delivery robot real-time path planning method of claim 1, wherein a set of tasks is described as
Figure FDA0003540491440000031
It indicates that the robot group is at tau0Working at any moment, and n areas during the operation of the robot4Always forbid passing, and the robot is required to be in the time range [ E ]1，L1]Inner access area Π1Organic robot in time Range [ E2，L2]Inner access area Π2Not allowing robot to stop in area II2。
CN201911326135.6A 2019-12-20 2019-12-20 Multi-medical delivery robot real-time path planning method Active CN111928849B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911326135.6A CN111928849B (en) 2019-12-20 2019-12-20 Multi-medical delivery robot real-time path planning method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911326135.6A CN111928849B (en) 2019-12-20 2019-12-20 Multi-medical delivery robot real-time path planning method

Publications (2)

Publication Number Publication Date
CN111928849A CN111928849A (en) 2020-11-13
CN111928849B true CN111928849B (en) 2022-04-26

Family

ID=73282907

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911326135.6A Active CN111928849B (en) 2019-12-20 2019-12-20 Multi-medical delivery robot real-time path planning method

Country Status (1)

Country Link
CN (1) CN111928849B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113657718B (en) * 2021-07-20 2024-04-19 香港中文大学（深圳） Multi-robot dynamic alliance task allocation method and related device
CN114019960B (en) * 2021-10-19 2024-06-07 北京云迹科技股份有限公司 Scheduling method and device for multi-robot delivery
CN117516575A (en) * 2023-11-28 2024-02-06 陕西科技大学 Multi-agent optimal task allocation and planning method, device and system based on base reachability graph

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20120015771A (en) * 2010-08-13 2012-02-22 고려대학교 산학협력단 Navigation control method for mobile robot using gspn and mobile robot using the same
CN104408910A (en) * 2014-11-24 2015-03-11 无锡清华信息科学与技术国家实验室物联网技术中心 Taxi-sharing scheduling method beneficial to multiple parties
CN108873737A (en) * 2018-07-18 2018-11-23 东北大学 A kind of automatic sorting control and decision system based on M-HSTPN model
CN110155126A (en) * 2019-06-03 2019-08-23 北京交通大学 The integrated optimization method of high-speed rail train scheduling and control under temporary speed limitation
CN110334837A (en) * 2019-04-11 2019-10-15 浙江同筑科技有限公司 A kind of more AGV dispatching methods decomposed based on petri net

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20120015771A (en) * 2010-08-13 2012-02-22 고려대학교 산학협력단 Navigation control method for mobile robot using gspn and mobile robot using the same
CN104408910A (en) * 2014-11-24 2015-03-11 无锡清华信息科学与技术国家实验室物联网技术中心 Taxi-sharing scheduling method beneficial to multiple parties
CN108873737A (en) * 2018-07-18 2018-11-23 东北大学 A kind of automatic sorting control and decision system based on M-HSTPN model
CN110334837A (en) * 2019-04-11 2019-10-15 浙江同筑科技有限公司 A kind of more AGV dispatching methods decomposed based on petri net
CN110155126A (en) * 2019-06-03 2019-08-23 北京交通大学 The integrated optimization method of high-speed rail train scheduling and control under temporary speed limitation

Also Published As

Publication number Publication date
CN111928849A (en) 2020-11-13

Similar Documents

Publication Publication Date Title
CN111928849B (en) Multi-medical delivery robot real-time path planning method
Hu et al. Anti-conflict AGV path planning in automated container terminals based on multi-agent reinforcement learning
Gul et al. Meta-heuristic approach for solving multi-objective path planning for autonomous guided robot using PSO–GWO optimization algorithm with evolutionary programming
Srivastava et al. Development of an intelligent agent-based AGV controller for a flexible manufacturing system
Yeh et al. Composite agents
Morales et al. Social robotic wheelchair centered on passenger and pedestrian comfort
Chen et al. Multiobjective scheduling strategy with genetic algorithm and time-enhanced A* planning for autonomous parking robotics in high-density unmanned parking lots
Kala et al. Planning of multiple autonomous vehicles using rrt
Babu et al. HOPAV: Hybrid optimization‐oriented path planning for non‐connected and connected automated vehicles
Karamouzas et al. Space-time group motion planning
Yu et al. A parallel algorithm for multi-AGV systems
Wang et al. Heterogeneous multi-agent routing strategy for robot-and-picker-to-good order fulfillment system
Kolushev et al. Multi-agent optimal path planning for mobile robots in environment with obstacles
Sun et al. On the design for AGVs: Modeling, path planning and localization
Pérez-Dattari et al. Visually-guided motion planning for autonomous driving from interactive demonstrations
Gao et al. A review of graph-based multi-agent pathfinding solvers: From classical to beyond classical
Causse et al. Management of a multi-robot system in a public environment
Su et al. Collaborative motion planning based on the improved ant colony algorithm for multiple autonomous vehicles
Yang et al. Decoupled real-time trajectory planning for multiple autonomous mining trucks in unloading areas
Zhen et al. A decision framework for automatic guided vehicle routing problem with traffic congestions
Vrabič et al. Improving the flow in multi-robot logistic systems through optimization of layout roadmaps
Xing et al. Collision avoidance and give way of heterogeneous and variable-sized multiple mobile robots based on glued nodes
Wang et al. Hierarchical Planning for Autonomous Parking in Dynamic Environments
Davydov et al. Q-Mixing network for multi-agent pathfinding in partially observable grid environments
Arul et al. Cglr: Dense multi-agent navigation using voronoi cells and congestion metric-based replanning

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant