CN112636345B - Distributed multi-robot charging station distribution problem solving method - Google Patents

Distributed multi-robot charging station distribution problem solving method Download PDF

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CN112636345B
CN112636345B CN202011589000.1A CN202011589000A CN112636345B CN 112636345 B CN112636345 B CN 112636345B CN 202011589000 A CN202011589000 A CN 202011589000A CN 112636345 B CN112636345 B CN 112636345B
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robot
charging
robots
idle
charging station
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CN112636345A (en
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邱方长
魏璇
许林杰
李立
林祖乾
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Zhejiang Cotek Robot Co ltd
Zhejiang EP Equipment Co Ltd
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Zhejiang Cotek Robot Co ltd
Zhejiang EP Equipment Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
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    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/62—Monitoring or controlling charging stations in response to charging parameters, e.g. current, voltage or electrical charge
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
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    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/66—Data transfer between charging stations and vehicles
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    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
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    • B60L53/67—Controlling two or more charging stations
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    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
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    • B60L2200/00—Type of vehicles
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    • B60L2240/00—Control parameters of input or output; Target parameters
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    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/70—Interactions with external data bases, e.g. traffic centres
    • B60L2240/72—Charging station selection relying on external data
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    • H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
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    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
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Abstract

The invention discloses a method for solving the problem of distribution of charging stations based on distributed multiple robots, aiming at minimizing the total time consumption of all robots for robots which have tasks and need to be charged, and selecting the charging stations by each robot based on the principle of shortest charging time consumption by the robot by utilizing the information of the positions, the residual electric quantity, the neighbor robots with the priority higher than the priority of the robot and the like of the neighbor robots in the communication range acquired by the robot, so as to realize the approximate optimal solution. For idle robots, the aim is to maximize the electric quantity of all robots and ensure the electric quantity balance. The idle robot carries out charging scheduling based on the principle of charging station distribution balance, and the idle robot of each charging station solves the charging time of the idle robot through cooperation to complete charging in sequence. The invention effectively shortens the charging time of the task robot and solves the problem of electric quantity balance after the idle robot is charged. The high availability of the system is ensured, and the working efficiency of the robot is greatly improved.

Description

Solution method based on distribution problem of distributed multi-robot charging station
Technical Field
The invention relates to the technical field of multi-robot resource scheduling optimization, in particular to a method for solving a distribution problem based on a distributed multi-robot charging station.
Background
Automated guided vehicles (robots) are electrically driven, capable of performing tasks as required and can interact with other equipment. The robot has the characteristics of high automation degree, good reliability, strong adaptability and the like, so that the robot is widely applied to a plurality of industries such as automobiles, chemical engineering, logistics, assembly and the like, the working efficiency is greatly improved, the enterprise expense is reduced, and the labor intensity is reduced. Especially, the application in the field of automatic workshops is wider. In order to improve the working efficiency of the robot in the field of automatic workshops and enable the robot to be more reliably put into production, the limited resource sharing of the robot is a very key technical problem.
The robot is fundamentally used as an electric product and must be charged or replaced in the working process, but the area of an automatic workshop is limited and precious, a charging station cannot be provided for each robot, which obviously wastes resources and cost, in addition, extra driving time is added in the charging process of the robot, and the charging station is generally arranged at a place far away from the workshop. This is not allowed to happen in modern workshops, which require a lot of labour and time costs to be invested once this occurs, and it is therefore important in a workshop with limited charging stations to keep each robot powered and maintained at a relatively stable level. So as soon as the robot has low battery, we allocate a nearest idle charging station to charge it. In the whole charging process, the robot cannot execute tasks, and can not be put into use again until charging is completed.
Disclosure of Invention
The invention aims to provide a solving method based on the distribution problem of a distributed multi-robot charging station aiming at the defects of the prior art, mainly optimizes the problem of multi-robot charging scheduling, and greatly shortens the charging process of the robots which have tasks and need to be charged; for idle robots without tasks, the invention distributes them evenly near each charging station and achieves equalization of the charge (i.e. the charge of each robot is almost equal). The invention improves the charging efficiency of the robot with tasks and realizes the electric quantity balance of the idle robot. The high availability of the system is ensured, and the working efficiency of all robots is improved.
The present invention mainly solves the following three problems.
1. Is tasked and has an electric quantity lower than theta1The robot selects a charging station with the shortest charging time (shortest driving time plus shortest queuing time).
2. The task-free and idle robots are distributed relatively evenly in the vicinity of the individual charging stations.
3. In a given time, the balance of the charged electric quantity of the idle robots near each charging station is realized (namely, the charged electric quantity of each idle robot is almost equal).
The purpose of the invention is realized by the following technical scheme: a method for solving a problem of distributed multi-robot charging station distribution comprises three stages, wherein the first stage is the charging station distribution of a robot with a task and needing to be charged, the second stage is the charging station distribution of a robot without the task and in an idle state, and the third stage is the electric quantity balance after the idle robot is charged: the specific steps of the charging station allocation of the robot which has the task and needs to be charged are as follows:
step 1-1, each charging robot broadcasts own position information, residual electric quantity and charging state to own neighbor robots, and meanwhile receives the information from the neighbor robots.
And 1-2, calculating the charging priority of each robot with tasks and the charging priority of each robot with the tasks according to the residual electric quantity and the charging state of the neighbor robots. The robot with the high priority preferentially selects the charging station.
And 1-3, after the robot with the high priority selects a charging station with the shortest charging time, broadcasting the number and the charging time of the charging station to the neighbor robots. And then, the user does not participate in charging station selection any more, and directly goes to the selected charging station to wait for charging in a queue.
And 1-4, after the neighbor robots receive the broadcast messages from the step 1-3, updating the queuing time of the selected charging station, and continuing to perform next round of selection and charging of the rest robots according to the priority level until each robot with tasks and needing charging completes the selection and charging of the charging station.
The specific steps of the charging station allocation of the task-free and idle robot are as follows:
and 2-1, calculating the distances from the idle robot to all charging stations, and sequencing the distances from small to large to form a distance list.
And 2-2, taking all idle robots as a cluster, selecting a leader robot, calculating the maximum capacity of each charging station by the leader robot according to the number of the charging stations and the number of the idle robots, updating the result to other idle robots, and synchronizing the information in the cluster when the leader robot receives more than half of the robot responses in the cluster.
And 2-3, the idle robot selects the nearest charging station from the distance list and sends a request to the leader robot, the leader robot judges whether the selected charging station reaches the maximum capacity after receiving the request, if the selected charging station does not reach the maximum capacity of the charging station, the request of the robot is agreed, meanwhile, the leader robot reduces the capacity of the number of the corresponding charging station and updates the capacity information of the charging station to the cluster, and otherwise, the leader robot refuses the request of the idle robot.
And 2-4, if the idle robot receives the rejection request from the leader robot, selecting the next closest charging station, and continuing to send the request, otherwise, marking the idle robot to finish the distribution.
In a given time, the steps of equalizing the electric quantity after the idle robot is charged are as follows:
and 3-1, after the charging station distribution of the task-free and idle robots is completed, a plurality of idle robots are arranged near each charging station, and all the idle robots selecting the same charging station form a neighbor set.
And 3-2, the idle robot in each neighbor set calculates the own optimal Lagrange multiplier through interactive iteration with the neighbor robot by utilizing a distributed optimization algorithm based on a Lagrange multiplier method.
And 3-3, calculating the charging time of each idle robot in the neighbor set by using the obtained optimal Lagrange multiplier.
And 3-4, after each idle robot calculates the charging time of the idle robot, the idle robot in the neighbor set selecting the same charging station can select a leader robot again at the moment, and the charging time of the idle robot is sent to the leader robot.
And 3-5, after the leader robot in each neighbor set receives the charging time of the idle robots, synchronizing the charging time to more than half of the robots in the cluster until the charging time of all the idle robots is saved by the cluster.
And 3-6, sequencing the numbers of all idle robots by the leader robot in each neighbor set in an ascending order, updating the numbers of the robots to be charged and the time for starting charging to the cluster by the leader robot, and after receiving the agreement of more than half of the robots in the cluster. And the robot with the largest designated number is charged.
And 3-7, after the charging robot finishes charging, sending a request for finishing charging to the leader robot in the neighbor set, and then repeating the step 3-6 by the leader robot to appoint the next idle robot to charge.
And 3-8, repeating the steps 3-6 and 3-7 until the charging is completed.
Further, in step 1-2, the following two assumptions are made for the robot that is tasked and is to be charged:
1) the robot with the task is in the state that the electric quantity is lower than the electric quantity threshold value theta1All tasks are abandoned at the time, and charging is defined.
2) The electric quantity of the robot which is charging and has a task is higher than the electric quantity threshold value theta1But below the threshold theta2And at the same time, the charging is considered to be required. Determining a priority of the robot based on the robot power, the priority of charging of the robot being determined by the following formula:
Figure BDA0002868259870000031
wherein theta is1For the lowest amount of power, θ, that the robot can perform a task2Minimum charge for robot, xiThe residual capacity of the robot which is the ith task and needs to be charged. The robot calculates the function y according to the electric quantity of the robotiThe value of (c) indicates the priority of the robot for which there is a task and which needs to be charged. y isi1 is highest in priority, and indicates that the electric quantity of the robot which is in charge and has a task is less than or equal to theta2。
Further, in steps 1-3, the ith robot r having tasks and needing to be chargediThe higher the priority of (a), the charging station is preferentially selected. If the priorities of the robots are consistent, the number value of the robot is used as an auxiliary factor for judging the priorities, and the priority is higher when the number of the robot is larger, so that the priorities of the robots are not equal.
Robot riThe charging station with the shortest total charging time consumption is selected from all the charging station sets S, and the robot with the highest priority is selected preferentially. Suppose robot riCharging stations are preferentially selected and the selected charging station is skCharging station skThe selection of (2) is realized by the following steps:
1) all robots to be charged can always reach all charging stations under the constraint of their electric quantity. Robot for charging immediatelyiThe set of reachable charging stations is S. Robot riThe time spent going to one charging station in the charging station set S and starting charging is:
Figure BDA0002868259870000041
wherein JijIndicating robot riTo charging station s numbered jjTime t 'at which charging is started'ijIndicating robot riTo charging station sjTime of travel of qijIndicating robot riTo charging station sjThe queuing time of (c).
2) Robot r with highest priorityiIs aimed at all JijFinding a charging station s with minimal time consumptionk。
Figure BDA0002868259870000042
When the robot riAfter the selection of the charging station is completed, the selected charging station s is selectedkThe number of (c) is not involved in the selection after being broadcast to its neighbor robots.
Further, in step 2-2, the leader robots in the cluster calculate the maximum number of robots allowed by each charging station according to the method that the number of idle robots divides the number of charging stations and rounds up.
The leader robot in the cluster firstly selects the charging station with the closest distance from the distance list, reduces the capacity of the charging station with the corresponding number by one, and then sends the updated capacity information of the charging station to the cluster. The method includes the steps that the idle robots except the leader robot in a cluster are follower robots, the follower robots select a charging station closest to the robot in a distance list of the robot and then send confirmation requests to the leader robots, when the confirmation from the leader is received, the follower robots finish distribution of the charging stations, otherwise, the follower robots can select a suboptimal charging station and continue to send the confirmation requests to the leader until distribution of the charging stations is finished.
If the charging station closest to the robot is not unique, the robot is preferably selected to be the charging station with the smallest number.
Further, step 3-2, after the idle robots are uniformly distributed near each charging station, assume that the charging station s isjWith K free robots, charging stations sjN for set of nearby idle robotssjIs represented by Nsj＝{rj1,rj2…,rjK}。rjKIndicating charging station sjThe K robot nearby.
And (3) equalizing the electric quantity of all idle robots in a given time T, and giving an objective function as follows:
Figure BDA0002868259870000043
Figure BDA0002868259870000044
wherein p isjiIndicating an idle robot rjiResidual capacity of rji∈Nsj. v denotes the charging rate of the charging station. w represents a penalty factor and w > 0. t is tjiIndicating robot rjiThe charging time of (c). T is a given charging time.
Because of Σ tjiT is a given time constant and w > 0, so the objective function can be converted to the following equation:
Figure BDA0002868259870000051
Figure BDA0002868259870000052
the above equation introduces the lagrangian function for the optimization problem with equality constraints:
Figure BDA0002868259870000053
wherein λ is lagrange multiplier, and is known from first-order optimal condition:
Figure BDA0002868259870000054
Figure BDA0002868259870000055
wherein a isji＝2·v2，bji＝2·v·pji。
The maximum charging time t of each idle robot is obtained through the conversionji *Dual conversion is carried out to obtain the optimal Lagrange multiplier lambda of each idle robot*。
Furthermore, each robot obtains the estimated value lambda of the Lagrange multiplier of the robot according to the Lagrange multiplier iteration calculation of the neighbor robot*。
The number of iterations is denoted by k, initially assuming a charging station sjLagrange multiplier lambda of the ith nearby idle robotji(0)＝T。
Lagrange multiplier sequence lambdaji(k) Updated from the following equation:
Figure BDA0002868259870000056
wherein N isjiIndicating an idle robot rjiSet of communicating neighbor robots, rlIs and robot rjiCommunicating robot, lambdalIs a robot rlLagrange multiplier coefficients. Alpha (alpha) ("alpha")tAnd betatIs an updated series of weight numbers for the lagrange multiplier sequence and decreases with time t, i.e. when t → ∞ then αt→0，βt→ 0, and βt/αt→ ∞. Wherein alpha istAnd betatThe following were selected:
Figure BDA0002868259870000057
Figure BDA0002868259870000058
further, the calculation is iterated through the step 3-2
Figure BDA0002868259870000061
Calculating a charging station s under the constraint of a given total time TjOptimal charging time t of nearby ith idle robotji *。
By
Figure BDA0002868259870000062
Calculating a charging station sjOptimal charging time t of nearby ith idle robotji *(k) The formula of (1) is as follows:
Figure BDA0002868259870000063
further, in step 3-4, after each idle robot calculates its own charging time, the leader robot in the neighbor set of the same charging station synchronizes the charging time of all idle robots in the neighbor set before charging, so that when the charged robot is disconnected for a long time and cannot contact the leader robot after charging is completed, the leader robot can judge whether charging is completed by using the charging start time and the charging time of the charging robot, thereby judging whether to designate the next robot to be charged. After the robot finishes charging, a request for completing charging is sent to the leader robot, and once the leader robot receives the request, the next robot is designated to be charged in the same way until all idle robots in the neighbor set finish charging.
The invention has the beneficial effects;
1. the invention sets priority for the robots which have tasks and need to be charged, and combines the distance between the robots and the charging stations and the restriction of queuing time, so that the robots which need to be charged can select an optimal charging station, and in addition, the robots can share the charging stations. The number of charging stations is reduced and the installation and maintenance costs of the charging stations are reduced.
2. The charging balance problem of the task-free robot is considered, the robot improves the electric quantity of the robot by using the idle time, and meanwhile, a punishment item is introduced into the objective function so as to improve the electric quantity of the robot with lower electric quantity. The robot can be kept in a high usable state, and the working efficiency of the robot is improved.
Drawings
Fig. 1 is a flow chart of charging station allocation for a robot that is tasked and needs to be charged.
Fig. 2 is a flow chart of the charging time calculation for a non-task and idle robot.
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
The invention provides a method for solving the problem of distributed multi-robot charging station distribution, which comprises three stages, wherein the first stage is the charging station distribution of a robot with a task and needing to be charged, the second stage is the charging station distribution of a robot without the task and in an idle state, and the third stage is the electric quantity balance of the idle robot after being charged:
aiming at the problem of charging scheduling of multiple robots, firstly, the task is consideredThe robot assumes that there are n mobile robots R ═ R that have tasks and need to be charged1，r2…，RnIn which r isiIs the number of the ith robot. There are m charging stations S ═ S1，s2，…，smIn which s isjIs the number of the jth charging station. Assuming that the charging station is stationary, all robots know the location of the charging station; the electric quantity of the robot is lower than a set threshold eta1When the power supply is in use, the power supply can reach any charging station under the constraint of the residual power. Robot riTo charging station sjT 'for travel time of'ijAnd (4) showing. By using
Figure BDA0002868259870000071
Robot r for indicating chargingiAt charging station sjThe charging time of (2). q. q.sijPresentation robotiAt charging station sjThe queuing time of (c).
According to the residual electric quantity of the charged robot with tasks, the invention adopts an event triggering mechanism to dynamically set the priority for the robot, and the invention assumes that
Figure BDA0002868259870000072
A priority and
Figure BDA0002868259870000073
r for robot set corresponding to priority kkAnd (4) showing. The present invention defines that when k > j,
Figure BDA0002868259870000074
j is less than or equal to n, and the robot set RkIs lower than the robot set Rj. Robot set RkThe following constraints should be satisfied
Figure BDA0002868259870000075
The invention selects a charging station with the shortest charging process from a high-low robot set according to priority. (shortest charging process — shortest travel time + shortest queue time) all robots to be charged are assigned to a charging station, and the cost function of an assignment a for all robots involved is defined as follows:
Figure BDA0002868259870000076
whereinkRepresents a robot set RkIs weighted by priority andk＞＞wk+1. Allocation scheme a is a mapping of R to S: { r1，r2…，rn}→{s1，s2，…，sm}. The solution space of A is a set of charging stations selected by all the robots having the task, and is used
Figure BDA0002868259870000077
And (4) showing. U shapeiIs a robot riCharging reachable set of (U)iS. The elements of U correspond to an allocation scheme A ═ A (1), …, A (n)]A (i) ═ j, i ═ 1, …, n. Indicating robot riSelecting charging stations sj。
An optimal allocation scheme A*Satisfies the following formula:
Figure BDA0002868259870000078
the invention aims at a task and a robot needing to be charged, and aims at finding an approximately optimal distribution scheme through a distributed algorithm. In a distributed environment, each robot is an independent individual, which always wants to minimize its own charging process, but due to the lack of global information, i.e. the robot r to be chargediWhen the queuing times of all charging stations are not known, the best decision (selection of charging station) is made only with the current information. In order to ensure the feasibility of the algorithm, the robot is specified to be unique to the nearest charging station, and in practice, a plurality of charging stations nearest to the robot are possible, but the index of the charging stations is unique, so that the index of the charging stations can beThis uniqueness is guaranteed with the smallest indexed charging station. If j < k when t'ij+qij＝t′ik+qikThen (c) is performed. Then t 'is defined'ij+qij＜t′ik+qik。
The invention combines the robot set with two charging priorities, and the electric quantity of the robot which has a task and is being charged is less than eta2The charging priority of the robot is highest, and the electric quantity of the robot which has a task and is not being charged is less than eta1R for robot set with lowest charging priority and highest charging priority1R for robot set indicating lowest priority of charging2And (4) showing. The invention enables the electric quantity of the robot which has a task and is being charged to be less than eta2The charging priority of the robot is set to be the highest because the robot still selects the charging station which is being charged under the objective function of the invention, and the robot with the low charging priority can not be influenced to select the charging station.
For an idle robot, the robot automatically searches for a charging station to charge, and n idle robots are assumed to be provided, wherein R is equal to R1，r2…，rnIn which r isiIs the number of the ith robot. Charging station set S ═ S1，s2，…，smIn order to avoid accumulation of a large number of idle robots near one charging station, the method is based on the idea of 'uniform distribution and electric quantity balance' to perform charging scheduling on the idle robots, firstly, the idle robots are uniformly distributed near each charging station on the basis of the principle that the total travel time is shortest, and then, the optimal Lagrangian multiplier is calculated through interactive iteration with the neighboring robots by using a distributed optimization algorithm based on a Lagrangian multiplier method, so that the optimal charging time of the idle robots is calculated. So that all idle robots selecting the same charging station achieve the balance of electric quantity in a given charging time.
The objective function for idle robot charging station selection is:
Figure BDA0002868259870000081
Figure BDA0002868259870000082
Figure BDA0002868259870000083
Figure BDA0002868259870000084
wherein SjIndicating charging station sjThe maximum amount of power that can be provided. diIs numbered as riThe amount of power required by the idle robot, xijRepresenting the amount of decision, xij1 denotes a robot riSelecting charging stations sj,xijDenotes "0" robot riWithout selecting charging station sj。
Aiming at the objective function, the invention provides an approximately optimal distribution scheme to meet the goal that the upper idle robot uniformly selects charging stations.
For convenience of description, the invention defines that an idle robot has three different states, namely a lower state, a candidate state and a leader state. All idle robots are in a follower state in the initial stage; if the heartbeat packet, namely the data packet, from the leader is not received within a period of time, switching from the folower to the candidate, and initiating election; if receiving the votes (including a vote) of most idle robots, switching to a leader state, and then sending a heartbeat packet to the idle robots all the time to maintain the state of the idle robots; if it is found at this time that the term of the other idle robot is newer than itself, it actively switches to folower.
Each idle robot has two important concepts term and election timeout (election times). the initial value of term is 0, and 1 is added to the election term of each round of the idle robot, so that the idle robot plays a role of a logic clock. The election timeouts is an arbitrary value between 150ms and 200ms, and when the idle robot does not receive the heartbeat packet from the leader in the time, the idle robot is changed from the follower state to the candidate state, and a voting request is initiated to serve as the idle robot to trigger election.
The specific leader election process is as follows:
the robot states of the initial states are all follower, and the tenure of each robot of the initial states is started from 0 and is incremented. Firstly, the term of the local robot is added with 1 and is changed to the candidate state, secondly, a vote is cast, the voting request is sent to other robots in parallel, and finally, the reply of other robots is waited.
In this process, three results may occur based on messages from other robots
1) Most votes (including a vote of the owner) from other robots are received, and the election is won to be a leader.
2) And the user is informed that others are elected, the user switches to the follower by himself.
3) If the majority of votes of other robots are not received within a period of time, the candidate state is maintained, and a new round of election is issued again.
In the first case, after winning the election, the new leader will send a message to all robots immediately, maintaining the state of its own leader.
The voting mechanism of the invention is that when the robot in the follower state receives the voting request from the candidate robot, the following judgments are made:
1) in an idle period, the robot can only cast one ticket.
2) The follower robot receives the request first and votes first.
3) The tenue term of the candidate robot cannot be smaller than itself.
Second case, if r1,r2,r3Three robots, r1,r2Elections are initiated simultaneously, and r1The election message of (2) arrives first at r3，r3To r1Throw a ticket when2Message arrival of3At this time, the3Only one ticket has been cast to r within its own tenure term1I.e. r3Will not give r2Voting, and r1And r2Clearly no vote is given to the other party. r is1After winning, r is given2，r3Sending a heartbeat message, r2Discovery r1If its term is not lower than its term, then r is2And converted to a follower.
In the third case, no candidate votes are obtained, such as the following: let r be1,r2,r3,r4Four robots, r3,r4And at the same time become candidate. But r is1Throw r4A ticket r2Throw r3Once a ticket, this is the case for a flat ticket. At this time, the leader is waited to appear, and a new round of election is initiated again until the organic robot election is overtime. To avoid the flat ticket situation, the election timeout is typically set to a random number between 150ms and 200 ms. Thus, the election of the leader robot is completed.
Based on the thought and the setting, the solving method based on the distribution problem of the distributed multi-robot charging station provided by the invention comprises the following three stages of concrete processes:
the specific steps of the charging station allocation of the robot which has the task and needs to be charged are as follows:
step 1-1, each charging robot broadcasts own position information, residual electric quantity and charging state to own neighbor robots and receives the information from the neighbor robots.
And 1-2, calculating the charging priority of each robot with tasks and the charging priority of each robot with the tasks according to the residual electric quantity and the charging state of the neighbor robots. The robot with the high priority preferentially selects the charging station. The following two assumptions are made for a robot that is tasked and is to be charged:
1) the robot with the task is in the state that the electric quantity is lower than the electric quantity threshold value theta1All tasks are abandoned at the time, and charging is defined.
2) Is charging and hasThe robot electric quantity of the task is higher than an electric quantity threshold value theta1But below the threshold theta2And at the same time, the charging is considered to be required. Determining a priority of the robot based on the robot power, the priority of charging of the robot being determined by the following formula:
Figure BDA0002868259870000101
wherein theta is1Is the lowest amount of power, θ, that the robot can perform a task2Minimum charge for robot, xiThe residual capacity of the robot which is the ith task and needs to be charged. The robot calculates the function y according to the electric quantity of the robotiThe value of (c) indicates the priority of the robot for which there is a task and which needs to be charged. y isi1 is highest in priority, and indicates that the electric quantity of the robot which is in charge and has a task is less than or equal to theta2. The reason that this priority setting is highest is that a high priority robot does not affect a low priority robot to make a decision while meeting the objectives of the present invention. y isi2, the priority is lowest, which indicates that the task is available and the electric quantity is less than theta1The robot of (1).
And 1-3, after the robot with the high priority selects a charging station with the shortest charging time, broadcasting the number and the charging time of the charging station to the neighbor robots. And then, the user does not participate in charging station selection any more, and directly goes to the selected charging station to wait for charging in a queue. Ith robot r having tasks and needing to be chargediThe higher the priority of (a), the charging station is preferentially selected. If the priorities of the robots are consistent, the number value of the robot is used as an auxiliary factor for judging the priorities, and the priority is higher when the number of the robot is larger, so that the priorities of the robots are not equal.
Robot riThe charging station with the shortest total charging time is selected from all the charging station sets S, and the robot with the highest priority is selected preferentially. Suppose robot riCharging stations are preferentially selected and the selected charging station is skCharging station skThe selection of (A) is realized by the following steps:
1) all robots to be charged can always reach all charging stations under the constraint of their electric quantity. Robot for charging immediatelyiThe set of reachable charging stations is S. Robot riThe time taken to go to one charging station in the set S of charging stations and start charging is:
Figure BDA0002868259870000102
wherein JijIndicating robot riTo charging station s numbered jjTime to start charging, t'ijIndicating robot riTo charging station sjTime of travel of qijIndicating robot riTo charging station sjThe queuing time of (c).
2) Robot r with highest priorityiIs aimed at all JijFinding a charging station s with minimal time consumptionk。
Figure BDA0002868259870000103
When the robot riAfter the selection of the charging station is completed, the selected charging station s is selectedkThe number of (c) is not involved in the selection after being broadcast to its neighbor robots.
The robot with the highest priority is the robot which is on a task and is charging but has the electric quantity less than theta2The robot of (1). Obviously, its queue time and travel time are both minimal, so setting its charging priority highest, it still selects the charging station that it is charging. The method has the advantages that more charging queue information can be obtained after the charging robots with low priority and the charging robots establish communication, and therefore the charging robots with low priority can make decisions.
And 1-4, after the neighbor robots receive the broadcast messages from the step 1-3, updating the queuing time of the selected charging station, and continuing to perform next round of selection and charging of the rest robots according to the priority level until each robot with tasks and needing charging completes the selection and charging of the charging station.
The specific steps of the charging station allocation of the task-free and idle robot are as follows:
and 2-1, the idle robots know the position information of all the charging stations, so that each idle robot can calculate the distance from the idle robot to the charging stations and sort the distances from small to large to form a distance list.
And 2-2, taking all idle robots as a cluster, selecting a leader robot, calculating the maximum number of robots allowed by each charging station by the leader robot in the cluster according to the method of dividing the number of the charging stations by the number of the idle robots and rounding up, updating the result to other idle robots, and synchronizing the information in the cluster when the leader robot receives more than half of the robots in the cluster.
The leader robot in the cluster firstly selects a charging station closest to the leader robot in the distance list, reduces the capacity of the charging station with the corresponding number by one, then sends updated charging station capacity information to the cluster, and finally sends the charging station capacity information to the cluster when receiving the volume of the majority of robots in the cluster. The method comprises the steps that other idle robots except for a leader robot in a cluster are follower robots, the follower robots select a charging station with the closest distance from a distance list of the robot and then send confirmation requests to the leader robots, when the confirmation from the leader is received, the follower robots finish distribution of the charging stations, otherwise, the follower robots can select a suboptimal charging station and continue to send the confirmation requests to the leader until distribution of the charging stations is finished.
If the charging station closest to the robot is not unique, the robot is defined to preferentially select the charging station with the smallest number if a plurality of charging stations are closest after the idle robot selects the leader, considering that the number of the charging stations is fixed and the robot knows the positions of all the charging stations. (i.e., if j < k and t'ij＝t′ikThen, define t'ij＜t′ik。
Figure BDA0002868259870000111
k∈S，
Figure BDA0002868259870000112
t′ij、t′ikIs a robot riTo charging station sj、skTravel time) of the vehicle.
And 2-3, the idle robot selects the nearest charging station from the distance list and sends a request to the leader robot, the leader robot judges whether the selected charging station reaches the maximum capacity after receiving the request, if the selected charging station does not reach the maximum capacity of the charging station, the request of the robot is agreed, meanwhile, the leader robot reduces the capacity of the number of the corresponding charging station and updates the capacity information of the charging station to the cluster, and otherwise, the leader robot refuses the request of the idle robot.
And 2-4, if the idle robot receives the rejection request from the leader robot, selecting the next closest charging station, and continuing to send the request, otherwise, marking the idle robot to finish the distribution.
In a given time, the steps of equalizing the electric quantity after the idle robot is charged are as follows:
and 3-1, after the charging station distribution of the task-free and idle robots is completed, a plurality of idle robots are arranged near each charging station, and all the idle robots selecting the same charging station form a neighbor set.
And 3-2, the idle robot in each neighbor set calculates the optimal Lagrange multiplier of the idle robot by interactive iteration with the neighbor robot by utilizing a distributed optimization algorithm based on a Lagrange multiplier method. The specific process is as follows:
when the idle robots are uniformly distributed near each charging station, it is assumed that the charging station s isjWith K free robots, charging stations sjN for set of nearby idle robotssjRepresents Nsj＝{rj1,rj2…,rjK}。rjKIndicating charging station sjThe K robot nearby.
And (3) equalizing the electric quantity of all idle robots within a given time T, and giving an objective function as follows:
Figure BDA0002868259870000121
Figure BDA0002868259870000122
wherein p isjiIndicating an idle robot rjiResidual capacity of rji∈Nsj. v denotes the charging rate of the charging station. w represents a penalty factor and w > 0. t is tjiIndicating robot rjiThe charging time of (c). T is a given charging time.
Because of Σ tjiT is a given time constant and w > 0, so the objective function can be converted to the following equation:
Figure BDA0002868259870000123
Figure BDA0002868259870000124
the above equation introduces the lagrangian function for the optimization problem with equality constraints:
Figure BDA0002868259870000125
wherein λ is lagrange multiplier, and is known from first-order optimal condition:
Figure BDA0002868259870000126
Figure BDA0002868259870000127
wherein a isji＝2·v2，bji＝2·v·pji。
The maximum charging time t of each idle robot is obtained through the conversionji *Dual conversion is carried out to obtain the optimal Lagrange multiplier lambda of each idle robot*。
Each robot is made to iteratively calculate to obtain an estimated value lambda of the Lagrange multiplier of the robot according to the Lagrange multiplier of the neighbor robot*。
The number of iterations is denoted by k, initially assuming a charging station sjLagrange multiplier lambda of the ith nearby idle robotji(0)＝T。
Lagrange multiplier sequence lambdaji(k) Updated from the following equation:
Figure BDA0002868259870000131
wherein N isjiIndicating an idle robot rjiSet of communicating neighbor robots, rlIs and robot rjiCommunicating robot, lambdalIs a robot rlLagrange multiplier coefficients. Alpha is alphatAnd betatIs an updated series of weight numbers for the lagrange multiplier sequence and decreases with time t, i.e. when t → ∞ then αt→0，βt→ 0, and βt/αt→ ∞. Wherein alpha istAnd betatThe following were selected:
Figure BDA0002868259870000132
Figure BDA0002868259870000133
obtained by iterative calculation
Figure BDA0002868259870000134
Calculating a charging station s under the constraint of a given total time TjOptimal charging time t of nearby ith idle robotji *。
By
Figure BDA0002868259870000135
Calculating a charging station sjOptimal charging time t of nearby ith idle robotji *(k) The formula (c) is as follows:
Figure BDA0002868259870000136
the condition for the termination of the update iteration of the lagrange multiplier sequence is λji(k+1)-λji(k) I < epsilon, epsilon is the given error precision.
And 3-3, calculating the charging time of each idle robot in the neighbor set by using the obtained optimal Lagrange multiplier.
And 3-4, after each idle robot calculates the charging time of the idle robot, the idle robot in the neighbor set selecting the same charging station can select a leader robot again at the moment, and the charging time of the idle robot is sent to the leader robot. After each idle robot calculates the charging time of the idle robot, the leader robot in the neighbor set of the same charging station synchronizes the charging time of all idle robots in the neighbor set before charging, so that when the charged robot is disconnected for a long time and cannot contact with the leader robot after charging is completed, the leader robot can judge whether charging is completed or not by using the charging starting time and the charging time of the charging robot, and further judge whether a next robot to be charged is specified or not. And after the robot finishes charging, sending a request for completing charging to the leader robot, and once receiving the request, the leader robot appoints the next robot to charge in the same way until all the idle robots in the neighbor set finish charging.
And 3-5, after the leader robot in each neighbor set receives the charging time of the idle robots, synchronizing the charging time to more than half of the robots in the cluster until the charging time of all the idle robots is saved by the cluster.
And 3-6, sequencing the numbers of all idle robots in an ascending order by the leader robot in each neighbor set, then updating the numbers of the robots to be charged and the time for starting charging to the cluster by the leader robot, and after receiving the agreement of more than half of the robots in the cluster. And the robot with the largest designated number is charged.
And 3-7, after the charging robot finishes charging, sending a request for finishing charging to the leader robot in the neighbor set, and then repeating the step 3-6 by the leader robot to appoint the next idle robot to charge.
And 3-8, repeating the steps 3-6 and 3-7 until the charging is completed.
The above-described embodiments are intended to illustrate rather than to limit the invention, and any modifications and variations of the present invention are within the spirit of the invention and the scope of the appended claims.

Claims (8)

1. A method for solving the problem of distributed multi-robot charging station distribution is characterized by comprising three stages, wherein the first stage is the charging station distribution of a robot with a task and needing to be charged, the second stage is the charging station distribution of a robot without the task and in an idle state, and the third stage is the electric quantity balance of the idle robot after being charged:
the specific steps of the charging station allocation of the robot which has the task and needs to be charged are as follows:
step 1-1, each charging robot broadcasts own position information, residual electric quantity and charging state to own neighbor robots and receives the information from the neighbor robots;
step 1-2, each robot with tasks calculates the charging priority of the robot and the neighboring robot according to the residual electric quantity and the charging state of the neighboring robot; the robot with the high priority preferentially selects the charging station;
step 1-3, after the robot with high priority selects a charging station with the shortest charging time, broadcasting the number and the charging time of the charging station to the neighboring robots; then, the user does not participate in the selection of the charging station any more, and directly goes to the selected charging station to queue for charging;
step 1-4, after the neighbor robots receive the broadcast messages from the step 1-3, the queue time of the selected charging station is updated, and the rest robots continue to perform the next round of selection and charging according to the priority level until each robot with tasks and needing charging completes the selection and charging of the charging station;
the specific steps of the charging station allocation of the task-free and idle robot are as follows:
step 2-1, the idle robot calculates the distances between the idle robot and all charging stations, and the distances are sorted from small to large to form a distance list;
step 2-2, all idle robots are used as a cluster, a leader robot is selected, the leader robot calculates the maximum capacity of each charging station according to the number of the charging stations and the number of the idle robots, the result is updated to other idle robots, and when the leader robot receives more than half of the robot responses in the cluster, the information is synchronized in the cluster;
step 2-3, the idle robot selects the nearest charging station from the distance list and sends a request to the leader robot, after the leader robot receives the request, whether the selected charging station reaches the maximum capacity is judged, if the selected charging station does not reach the maximum capacity of the charging station, the request of the robot is agreed, meanwhile, the leader robot reduces the capacity of the number of the corresponding charging station and updates the capacity information of the charging station to the cluster, otherwise, the leader robot refuses the request of the idle robot;
step 2-4, if the idle robot receives the rejection request from the leader robot, the next closest charging station is selected, the request is continuously sent, otherwise, the idle robot marks the completion of the distribution;
in a given time, the steps of equalizing the electric quantity after the idle robot is charged are as follows:
step 3-1, after the charging station distribution of the task-free and idle robots is completed, a plurality of idle robots are arranged near each charging station, and all the idle robots selecting the same charging station form a neighbor set;
3-2, the idle robot in each neighbor set calculates the optimal Lagrange multiplier of the idle robot by interactive iteration with the neighbor robot by utilizing a distributed optimization algorithm based on a Lagrange multiplier method;
3-3, each idle robot in the neighbor set calculates the charging time of the idle robot by using the obtained optimal Lagrange multiplier;
3-4, after each idle robot calculates the charging time of the idle robot, the idle robot in the neighbor set selecting the same charging station selects a leader robot again at the moment, and sends the charging time of the idle robot to the leader robot;
3-5, after the leader robot in each neighbor set receives the charging time of the idle robots, synchronizing more than half of the robots in the cluster with the charging time until the charging time of all the idle robots is saved by the cluster;
3-6, sequencing the numbers of all idle robots by the leader robot in each neighbor set in an ascending order, then updating the numbers of the robots to be charged and the time for starting charging to the cluster by the leader robot, and after receiving the agreement of more than half of the robots in the cluster; the robot with the largest serial number is appointed to charge;
3-7, after the charging of the charged robot is finished, sending a request for finishing charging to the leader robot in the neighbor set, and then repeating the step 3-6 by the leader robot to appoint the next idle robot for charging;
and 3-8, repeating the steps 3-6 and 3-7 until the charging is completed.
2. The method for solving the problem of the distributed multi-robot charging station distribution according to claim 1, wherein in step 1-2, the following two assumptions are made for the robot to be charged and has a task:
1) the robot with the task is in the state that the electric quantity is lower than the electric quantity threshold value theta1All tasks need to be abandoned and defined as charging;
2) the electric quantity of the robot which is charging and has a task is higher than the electric quantity threshold value theta1But below the threshold theta2And then, the charging is considered to be required; determining a priority of the robot based on the robot power, the priority of charging of the robot being determined by the following formula:
Figure FDA0003531765880000021
wherein theta is1Is the lowest amount of power, θ, that the robot can perform a task2Minimum charge for robot, xiThe residual capacity of the ith robot which has a task and needs to be charged is calculated; the robot calculates the function y according to the electric quantity of the robotiA value of (b), representing a robot priority for which there is a task and which needs to be charged; y isi1 is highest in priority, and indicates that the electric quantity of the robot which is in charge and has a task is less than or equal to theta2。
3. The method for solving the problem of the distribution of the charging stations of the distributed multiple robots based on the claim 1, wherein in the steps 1 to 3, the ith robot r which has tasks and needs to be chargediThe higher the priority of (2), the charging station will be selected preferentially; if the priorities of the robots are consistent, the number value of the robot is used as an auxiliary factor for judging the priority, and the priority is higher when the number of the robot is larger, so that the priorities of the robots are not equal;
robot riSelecting the charging station with the shortest total charging time from all the charging station sets S, and preferentially selecting the robot with the highest priority; suppose robot riCharging stations are preferentially selected and the selected charging station is skCharging station skThe selection of (A) is realized by the following steps:
1) all robots to be charged can always reach all charging stations under the constraint of the electric quantity of the robots; robot for charging immediatelyiThe reachable charging station set is S; robot riThe time taken to go to one charging station in the set S of charging stations and start charging is:
Jij＝t′ij+qij，
Figure FDA0003531765880000031
wherein JijIndicating robot riTo charging station s numbered jjTime to start charging, t'ijIndicating robot riTo charging station sjTime of travel of qijIndicating robot riTo charging station sjThe queuing time of (c);
2) robot r with highest priorityiIs aimed at all JijFinding a charging station s with minimal time consumptionk；
Figure FDA0003531765880000032
When the robot riAfter the selection of the charging station is completed, the selected charging station s is selectedkThe number of (c) is not involved in the selection after being broadcast to its neighbor robots.
4. The method for solving the problem of the distribution of the charging stations based on the distributed multiple robots as claimed in claim 1, wherein in step 2-2, the leader robots in the cluster calculate the maximum number of the robots allowed by each charging station according to the method of dividing the number of the charging stations by the number of the idle robots and rounding up;
the leader robot in the cluster firstly selects a charging station with the closest distance from the distance list, reduces the capacity of the charging station with the corresponding number by one, and then sends the updated capacity information of the charging station to the cluster; the method comprises the steps that other idle robots except for a leader robot in a cluster are follower robots, the follower robots select a charging station with the closest distance from a distance list of the robot and send confirmation requests to the leader robots, when the confirmation from the leader is received, the follower robots finish the distribution of the charging stations, otherwise, the follower robots select a suboptimal charging station and continue to send confirmation requests to the leader until the distribution of the charging stations is finished;
if the charging station closest to the robot is not unique, the robot is preferably selected to be the charging station with the smallest number.
5. The method for solving the problem of the distribution of the charging stations of the distributed multi-robot based on the claim 1, wherein in step 3-2, after the idle robots are uniformly distributed near each charging station, the charging stations s are assumed to bejWith K free robots, charging stations sjFor set of nearby idle robots
Figure FDA0003531765880000033
It is shown that the process of the present invention,
Figure FDA0003531765880000034
rjKindicating charging station sjA K-th robot in the vicinity;
and (3) equalizing the electric quantity of all idle robots in a given time T, and giving an objective function as follows:
Figure FDA0003531765880000035
Figure FDA0003531765880000036
wherein p isjiIndicating an idle robot rjiThe amount of remaining power of the battery,
Figure FDA0003531765880000037
v denotes the charging speed of the charging stationThe ratio; w represents a penalty factor and w > 0; t is tjiIndicating robot rjiThe charging time of (c); t is a given charging time;
because of Σ tjiT is a given time constant and w > 0, so the objective function can be converted to the following equation:
Figure FDA0003531765880000041
Figure FDA0003531765880000042
the above equation introduces the lagrangian function for the optimization problem with equality constraints:
Figure FDA0003531765880000043
wherein λ is lagrange multiplier, and is known from first-order optimal condition:
Figure FDA0003531765880000044
wherein a isji＝2·v2，bji＝2·v·pji；
The maximum charging time t of each idle robot is obtained through the conversionji *Dual conversion is carried out to obtain the optimal Lagrange multiplier lambda of each idle robot*。
6. The method as claimed in claim 5, wherein each robot is configured to obtain its own estimated value λ of Lagrangian multiplier by iterative computation of Lagrangian multipliers of neighboring robots*；
The number of iterations is denoted by k,initial assumption charging station sjLagrange multiplier lambda of the ith nearby idle robotji(0)＝T；
Lagrange multiplier sequence lambdaji(k) Updated by:
Figure FDA0003531765880000045
wherein N isjiIndicating an idle robot rjiSet of communicating neighbor robots, rlIs and robot rjiCommunicating robot, lambdalIs a robot rlLagrange multiplier coefficients; alpha is alphatAnd betatIs an updated series of weight numbers for the lagrange multiplier sequence and decreases with time t, i.e. when t → ∞ then αt→0，βt→ 0, and βt/αt→ infinity; wherein alpha istAnd betatThe following were selected:
Figure FDA0003531765880000046
Figure FDA0003531765880000051
7. the method for solving the problem of distribution of charging stations based on multiple distributed robots as recited in claim 6, wherein the solution is obtained by iterative calculation in step 3-2
Figure FDA0003531765880000052
Calculating a charging station s under the constraint of a given total time TjOptimal charging time t of nearby ith idle robotji *；
By
Figure FDA0003531765880000053
Calculating a charging station sjOptimal charging time t of nearby ith idle robotji *(k) The formula of (1) is as follows:
Figure FDA0003531765880000054
8. the method for solving the problem of distribution of the charging stations of the distributed multiple robots based on the claim 1 is characterized in that, in the step 3-4, after each idle robot calculates the charging time of the idle robot, the leader robot in the neighbor set of the same charging station synchronizes the charging time of all the idle robots in the neighbor set before charging so that when the charged robot is disconnected for a long time and cannot get in contact with the leader robot after charging is completed, the leader robot can judge whether charging is completed or not by using the charging start time and the charging time of the charging robot, and therefore whether the next robot to be charged is specified or not is judged; after the robot finishes charging, a request for completing charging is sent to the leader robot, and once the leader robot receives the request, the next robot is designated to be charged in the same way until all idle robots in the neighbor set finish charging.
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104578296A (en) * 2014-12-31 2015-04-29 深圳市科松电子有限公司 Robot charging method, device and system
CN106042963A (en) * 2016-06-17 2016-10-26 广东顺德中山大学卡内基梅隆大学国际联合研究院 Cooperative optimization method and system for electrified traffic network and electric system
WO2017106104A1 (en) * 2015-12-13 2017-06-22 Tara Chand Singhal Systems and methods for battery charge replenishment in an electric vehicle
CN107832138A (en) * 2017-09-21 2018-03-23 南京邮电大学 A kind of implementation method of the High Availabitity namenode models of flattening
CN109787304A (en) * 2017-11-14 2019-05-21 中国电力科学研究院有限公司 A kind of solar charging power station distributed energy management solutions method and system
CN111885550A (en) * 2020-06-06 2020-11-03 浙江中力机械有限公司 Distributed autonomous mobile robot scheduling system
CN112070341A (en) * 2020-07-24 2020-12-11 杭州电子科技大学 Distributed solving method for multi-robot charging strategy

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104578296A (en) * 2014-12-31 2015-04-29 深圳市科松电子有限公司 Robot charging method, device and system
WO2017106104A1 (en) * 2015-12-13 2017-06-22 Tara Chand Singhal Systems and methods for battery charge replenishment in an electric vehicle
CN106042963A (en) * 2016-06-17 2016-10-26 广东顺德中山大学卡内基梅隆大学国际联合研究院 Cooperative optimization method and system for electrified traffic network and electric system
CN107832138A (en) * 2017-09-21 2018-03-23 南京邮电大学 A kind of implementation method of the High Availabitity namenode models of flattening
CN109787304A (en) * 2017-11-14 2019-05-21 中国电力科学研究院有限公司 A kind of solar charging power station distributed energy management solutions method and system
CN111885550A (en) * 2020-06-06 2020-11-03 浙江中力机械有限公司 Distributed autonomous mobile robot scheduling system
CN112070341A (en) * 2020-07-24 2020-12-11 杭州电子科技大学 Distributed solving method for multi-robot charging strategy

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Cooperative Robot Control and Concurrent Synchronization of Lagrangian Systems;Soon-Jo Chung;《 IEEE Transactions on Robotics 》;20090605;全文 *
Leaderless and Non-static Position Consensus Algorithm for Multi-manipulator Systems Under Undirected Communication Topology;Zhao Tan;《 2018 37th Chinese Control Conference (CCC)》;20180727;全文 *
基于任务优先的移动机器人充电优化策略;王静等;《杭州电子科技大学学报(自然科学版)》;20180915(第05期);全文 *

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