CN117636641A - Inter-vehicle cooperative carrying method and device for vehicle carrying robot - Google Patents
Inter-vehicle cooperative carrying method and device for vehicle carrying robot Download PDFInfo
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Abstract
The invention discloses a cooperative inter-vehicle carrying method and device for a vehicle carrying robot. The inter-vehicle cooperative transportation method for the vehicle transportation robot comprises the following steps: receiving a target vehicle and a parking space allocation; calculating a path initialization index of the first event path; calculating a carrying path planning index of the second event path; acquiring other AGV robot data, and calculating a path condition index and a collaborative decision index; and determining and correcting the path in real time according to the index. According to the invention, whether other robots need to wait for and switch paths is determined by using the collaborative decision index by the robots, and decisions are made according to the predicted collision time, the task priority and the path overlapping length, so that collaborative operation among the robots is ensured by utilizing information sharing and collaborative decisions, the occurrence rate of collaborative path conflict is reduced, and the problem that the collaborative paths of the robots are easy to conflict in the prior art is solved.
Description
Technical Field
The invention relates to the technical field of intelligent driving movement planning, in particular to a cooperative vehicle-to-vehicle carrying method and device for a vehicle carrying robot.
Background
With the increasing problem of urban traffic, garage management is becoming an increasingly important challenge. Garages are often an important facility for the storage of vehicles in cities, especially in busy commercial and residential areas. A vehicle handling robot is an innovative technology aimed at automating the parking and retrieval process of vehicles in a garage. A method of inter-vehicle co-transportation within a garage involves how to co-operate a plurality of transportation robots to effectively move vehicles from a parking space to a storage area, or vice versa, to ensure efficient, safe parking procedures of the garage and reduce collision risk between vehicles.
In the prior art, a cooperative transportation method between vehicles in a garage generally depends on comprehensive application of an autonomous robot technology, an automatic navigation technology, a sensing technology and a communication technology. Each transfer robot is typically equipped with a sensor such as a lidar or camera for sensing the vehicle and environment. The automated navigation system allows the robot to plan a path within the garage, avoid obstacles, position the vehicle, and safely move it to a target parking space. Communication techniques are used for collaborative operations between robots so that they can share information, coordinate actions to ensure the efficiency of garage operations. The inter-vehicle collaborative handling method in the garage can be combined with a garage management system to realize comprehensive management of parking space management, charging and garage operation.
For example, publication No.: the invention patent of CN116382295A discloses a multi-AGV-based cooperative conveying method and system, comprising the following steps: acquiring specification data of a transport object corresponding to a transport task; determining the number of AGVs for the transport task and a coordinated transport strategy according to the specification data; and controlling the AGVs to carry out carrying operation on the carrying objects according to the cooperative carrying strategy. According to the scheme, the AGVs in the conveying scene are not required to be equipped according to the highest conveying capability, the AGVs with uniform specifications can be equipped according to different use scenes, and when the conveying performance of the AGVs cannot cope with a conveying object, a certain number of AGVs can be scheduled for cooperative conveying based on specific specification data of the object to be conveyed.
For example, publication No.: the invention patent of CN114967666A discloses a collaborative planning method of a parking robot, which comprises the following steps: s1, according to a carrying task requirement and a key path node topological map of a parking lot, a global planning path is prepared; s2, obtaining respective reference paths of the four parking robots according to the global planning paths; s3, enabling each parking robot to reach each conveying point according to the reference path and carrying out conveying work of the vehicle; s4, returning each parking robot to the starting point.
However, in the process of implementing the technical scheme of the invention in the embodiment of the application, the application finds that the above technology has at least the following technical problems:
in the prior art, as the collaborative path planning needs to consider a plurality of factors including the position, the target position, the speed, the obstacle and the like of the robot, the path conflict and the congestion are easy to occur, so that the robots mutually interfere and the efficiency is reduced, and in summary, the problem that the collaborative path of the robot is easy to collide exists in the prior art.
Disclosure of Invention
According to the inter-vehicle collaborative handling method and device for the vehicle handling robot, the problem that the robot collaborative paths are easy to conflict in the prior art is solved, and the occurrence rate of the collaborative path conflict is reduced.
The embodiment of the application provides a vehicle-to-vehicle cooperative conveying method for a vehicle conveying robot, which comprises the following steps: the AGV vehicle carrying robot receives a target vehicle to be carried and a target parking space distributed to the AGV vehicle carrying robot by the central control system; the AGV vehicle carrying robot calculates a path initialization index according to a route node topological graph of the garage, and analyzes a first event path from the current position to the parking room position according to the path initialization index; the AGV vehicle carrying robot calculates a carrying path planning index according to the target vehicle to be carried and the target parking space, and analyzes a second event path from the parking room to the target parking space according to the carrying path planning index; the AGV vehicle transfer robot acquires positioning data and task data of other AGV vehicle transfer robots in the garage, and calculates a path condition index and a collaborative decision index; the AGV vehicle carrying robot determines a first event path and a second event path according to the path condition index, and corrects the first event path and the second event path in real time according to the collaborative decision index in the running process of the AGV vehicle carrying robot.
Further, the specific allocation method of the target to-be-carried vehicle and the target parking space refers to: when a new vehicle to be carried exists in a parking room, the central control system selects an AGV vehicle carrying robot with the shortest Euclidean distance from the parking room, and the vehicle to be carried is set as a target vehicle to be carried of the AGV vehicle carrying robot; the central control system calculates a parking allocation index according to the vehicle data in the current parking room and the parking space data in the garage, and allocates a target parking space for the target vehicle to be carried according to the parking allocation index.
Further, the specific calculation method of the parking allocation index comprises the following steps: extracting vehicle type attribute data from vehicle data in a current parking garage, and extracting vehicle type attribute data, space number around a parking space and idle parking space position data which can be borne by a parking space from the parking space data in the garage; respectively numbering parking spaces and vehicle type attributes in the garage, and preprocessing and normalizing the extracted data; a parking distribution index formula model is built, and the specific parking distribution index model is as follows:in the formula, PASI k The parking allocation index for the kth parking space in the garage, e is a natural constant, K is the number of the parking space in the garage, k=1, 2.. >For the kth parking space currently being in the free state, < > for>For the corresponding number of the kth parking space in the garage in an idle state, < >> For the total number of free parking spaces, k and +.>One-to-one mapping of values of ε 1 Weight ratio in parking allocation index for kth parking space currently in idle state, +.>N-th vehicle type attribute supported for the kth parking space, N being a vehicle type attribute number, n=1, 2 2 Weight ratio of total number of vehicle type attributes supported for kth parking space in parking allocation index, +.>The number of idle parking spaces in the adjacent positions around the kth parking space is the total number of idle parking spaces in the adjacent positions around the parking space, epsilon 3 Weight ratio, LD, of the number of free parking spaces in the parking allocation index for the neighboring positions around the kth parking space k For the Euclidean distance from the kth parking space to the parking house, epsilon 4 And (3) the weight ratio of the Euclidean distance from the kth parking space to the parking room in the parking allocation index is calculated, and ζ is a correction coefficient of the parking allocation index.
Further, the specific analysis method of the path initialization index comprises the following steps: finding out an initial path which can be successfully travelled to a parking room by the current AGV vehicle carrying robot according to a route node topological graph of the garage; constructing a path initialization index model formula, and calculating a path initialization index of an initial path; the specific path initialization index model formula is: In CS D Initializing an index for a path corresponding to the D-th initial path, D being the number of the initial path, d=1, 2,.. 0 ，D 0 For the initial pathTotal number (S)/(S)>For the maximum allowed driving time length on the path, XY is the expected driving time length, alpha 1 JD for weighting factors in path initialization index for predicted travel duration D JD is the total number of nodes corresponding to the D-th initial path 0 Alpha is the minimum total number of nodes allowed by the path 2 And (3) as a weight factor of the total number of nodes in the path initialization index, and ζ is a correction coefficient of the path initialization index corresponding to the D-th initial path.
Further, the specific analysis method of the transportation path planning index comprises the following steps: acquiring position data of a target vehicle to be carried and a target parking space, and analyzing a carrying path by combining a route node topological graph of the garage; acquiring the position data of occupied parking spaces in a garage and numbering; constructing a carrying path planning index model formula, and calculating a carrying path planning index of a carrying path; the specific carrying path planning index model formula is as follows:in the formula GH B Planning an index for a conveyance path corresponding to the B-th conveyance path, B being the number of the conveyance path, b=1, 2, & gt, B 0 ，B 0 For the total number of carrying paths, +. >Representing that the kth parking space on the B-th carrying path is currently in an occupied state; />For the corresponding number of the k parking space in the garage in the occupied state, the number of the k parking space is +.> For the total number of occupied parking spaces, k and +.>Is mapped one by->When->When (I)>Otherwise, the same is done; ω is a correction coefficient of the conveyance path planning index.
Further, the specific analysis method of the path condition index comprises the following steps: acquiring the current positions of other AGV vehicle transfer robots and the current running paths of the other AGV vehicle transfer robots, and analyzing path situation indexes corresponding to the current running paths of the current AGV vehicle transfer robots according to the current positions; constructing a path condition index model formula, and calculating a path condition index; the specific path condition index model formula is:in the formula, RO E For the path condition index corresponding to the path in the first event path and the second event path set, E is the set of the first event path and the second event path, SG E SG for total number of crossings of the paths in the first event path and the second event path set with the current travel paths of other AGV vehicle transfer robots 0 To allow for a cross minimum total value χ 0 Is a correction coefficient for the path situation index.
Further, the specific calculation formula of the collaborative decision index is as follows: Wherein, XT is the collaborative decision index corresponding to the current AGV vehicle transfer robot, CAI is the conflict avoidance index corresponding to the current AGV vehicle transfer robot, YI is the yield index corresponding to the current AGV vehicle transfer robot.
Further, the collision avoidance fingerThe specific analysis method of the number is as follows: acquiring real-time positions, running speeds and running paths of the current AGV vehicle carrying robot and other AGV vehicle carrying robots in the garage; according to the estimated collision time obtained by the collision algorithm; constructing a collision avoidance index model formula, and calculating a collision avoidance index; the specific collision avoidance index model formula is:wherein TD is the estimated collision time of the current AGV vehicle transfer robot and other AGV vehicle transfer robots on the travel path, and TD 0 Minimum safe collision time for current AGV vehicle handling robot, < > for>And (5) a correction coefficient for the collision avoidance index.
Further, the specific analysis method of the yield index comprises the following steps: acquiring real-time positions and running paths of the current AGV vehicle carrying robot and other AGV vehicle carrying robots in the garage, and calculating the length of the coincident path according to the real-time positions and the running paths; constructing a yield index model formula, and calculating a yield index; the specific yield index model formula is as follows: In the RE 0 For the task priority of the current AGV vehicle transfer robot, RE is the task priority of the nearest AGV vehicle transfer robot on the current AGV vehicle transfer robot travel path, LF is the overlapping path length of the current AGV vehicle transfer robot and other AGV vehicle transfer robots on the travel path, and LF' is the overlapping path allowed waiting length in the in-situ waiting allowed range of the current AGV vehicle transfer robot.
The embodiment of the application provides a vehicle-to-vehicle cooperative conveying device for a vehicle conveying robot, which comprises a target distribution module, a path initialization calculation module, a conveying path planning module, a path condition and cooperative decision calculation module and a path correction module: the target allocation module: the system comprises a central control system, a AGV vehicle transfer robot, a parking space management system and a parking space management system, wherein the AGV vehicle transfer robot is used for receiving a target vehicle to be transferred and a target parking space distributed to the AGV vehicle transfer robot by the central control system; the path initialization calculation module: the method comprises the steps that a path initialization index is calculated according to a route node topological graph of a garage by an AGV vehicle carrying robot, and a first event path from a current position to a parking room position is analyzed according to the path initialization index; the carrying path planning module is used for: the AGV vehicle transfer robot is used for calculating a transfer path planning index according to a target vehicle to be transferred and a target parking space, and analyzing a second event path from the parking room to the target parking space according to the transfer path planning index; the path condition and collaborative decision calculation module: the method comprises the steps that the AGV vehicle transfer robot is used for acquiring positioning data and task data of other AGV vehicle transfer robots in a garage, and calculating a path condition index and a collaborative decision index; the path correction module: the method comprises the steps that the AGV vehicle carrying robot is used for determining a first event path and a second event path according to the path condition index, and the first event path and the second event path are corrected in real time according to the collaborative decision index in the running process of the AGV vehicle carrying robot.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
1. the robot uses the cooperative decision index to determine whether other robots and switching paths need to be waited, and makes a decision according to the predicted collision time, the task priority and the path overlapping length, so that cooperative operation among the robots is ensured by utilizing information sharing and cooperative decision, further, the occurrence rate of cooperative path conflict is reduced, and the problem that the robot cooperative paths are easy to conflict in the prior art is effectively solved.
2. Multiple initial paths, including the shortest path and other passable paths, are found through the route node topology diagram of the garage, so that the robot is ensured to reach a destination, flexibility and selectivity are further provided for the robot, the probability of path conflict is reduced, and meanwhile, the adaptability of the paths is improved.
3. The most suitable target parking spaces are distributed according to the characteristics of the vehicles and the parking spaces through the central control system, so that the utilization rate of the parking spaces is maximized, the vehicles are ensured to be distributed to the most suitable parking spaces, and congestion and resource waste are reduced.
Drawings
Fig. 1 is a flowchart of a method for inter-vehicle cooperative conveyance for a vehicle conveyance robot according to an embodiment of the present application;
fig. 2 is a block diagram of an inter-vehicle cooperative conveyance device for a vehicle conveyance robot according to an embodiment of the present application.
Detailed Description
According to the inter-vehicle collaborative handling method and device for the vehicle handling robot, the problem that in the prior art, a robot collaborative path is easy to conflict is solved, whether other robots need to wait for and switch paths is determined by using a collaborative decision index, and decision is made according to the predicted collision time, task priority and path overlapping length, so that collaborative operation among the robots is ensured by utilizing information sharing and collaborative decision, and further the occurrence rate of collaborative path conflict is reduced.
The technical scheme in the embodiment of the application aims at solving the problem that the robot cooperative paths are easy to conflict in the prior art, and the overall thought is as follows:
the AGV vehicle transfer robot receives target vehicle and parking space information distributed by the central control system in the cooperative transfer process, and determines a first event path and a second event path from the current position to the parking room and from the parking room to the target parking space by calculating a path initialization index and a transfer path planning index. The robot also acquires data of other AGV vehicle carrying robots, calculates a path condition index and a collaborative decision index, so that the two event paths are adjusted in real time according to the collaborative decision index in the driving process, and the occurrence rate of collaborative path conflict is reduced.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.
As shown in fig. 1, a flow chart of a method for cooperative inter-vehicle handling of a vehicle handling robot according to an embodiment of the present application is provided, and the method is applied to a cooperative inter-vehicle handling device of a vehicle handling robot, and includes the following steps: receiving a target vehicle and a parking space allocation: the AGV vehicle carrying robot receives a target vehicle to be carried and a target parking space distributed to the AGV vehicle carrying robot by the central control system; calculating a path initialization index of the first event path: the AGV vehicle carrying robot calculates a path initialization index according to a route node topological graph of the garage, and analyzes a first event path from the current position to the parking room position according to the path initialization index; calculating a handling path planning index of the second event path: the AGV vehicle carrying robot calculates a carrying path planning index according to the target vehicle to be carried and the target parking space, and analyzes a second event path from the parking room to the target parking space according to the carrying path planning index; acquiring other AGV robot data, and calculating a path condition index and a collaborative decision index: the AGV vehicle transfer robot acquires positioning data and task data of other AGV vehicle transfer robots in the garage, and calculates a path condition index and a collaborative decision index; determining and correcting a path in real time according to the index: the AGV vehicle carrying robot determines a first event path and a second event path according to the path condition index, and corrects the first event path and the second event path in real time according to the collaborative decision index in the running process of the AGV vehicle carrying robot.
In this embodiment, the first event path refers to a path of the AGV vehicle handling robot from the current position to the target vehicle to be handled in the parking room, and the second event path refers to a path of the AGV vehicle handling robot to handle the target vehicle from the parking room to the target parking space.
Further, the specific allocation method of the target to-be-carried vehicle and the target parking space refers to: when a new vehicle to be carried exists in a parking room, the central control system selects an AGV vehicle carrying robot with the shortest Euclidean distance from the parking room, and the vehicle to be carried is set as a target vehicle to be carried of the AGV vehicle carrying robot; the central control system calculates a parking allocation index according to the vehicle data in the current parking room and the parking space data in the garage, and allocates a target parking space for the target vehicle to be carried according to the parking allocation index.
In this embodiment, a parking space with the highest parking allocation index is selected as the allocated target parking space, and the parking space with the highest parking allocation index means that the parking space is in an idle state, and supports the stored type attribute of the vehicle without including the type attribute corresponding to the target vehicle to be carried, and further supports more type attributes, and in addition, the number of empty parking spaces around the parking space is the largest, and the euclidean distance from the parking space to the parking space is the current nearest. The parking space with the highest parking allocation index represents that the availability of the parking space is the highest at present and is the optimal allocation object.
Further, the specific calculation method of the parking allocation index comprises the following steps: extracting vehicle type attribute data from vehicle data in a current parking garage, and extracting vehicle type attribute data, space number around a parking space and idle parking space position data which can be borne by a parking space from the parking space data in the garage; respectively numbering parking spaces and vehicle type attributes in the garage, and preprocessing and normalizing the extracted data; a parking distribution index formula model is built, and the specific parking distribution index model is as follows:in the formula, PASI k The parking allocation index for the kth parking space in the garage, e is a natural constant, K is the number of the parking space in the garage, k=1, 2..>For the kth parking space currently being in the free state, < > for>For the corresponding number of the kth parking space in the garage in an idle state, < >> Is the total of free parking spacesNumber, k and->One-to-one mapping of values of ε 1 Weight ratio in parking allocation index for kth parking space currently in idle state, +.>N-th vehicle type attribute supported for the kth parking space, N being a vehicle type attribute number, n=1, 2 2 Weight ratio of total number of vehicle type attributes supported for kth parking space in parking allocation index, +.>The number of idle parking spaces in the adjacent positions around the kth parking space is the total number of idle parking spaces in the adjacent positions around the parking space, epsilon 3 Weight ratio, LD, of the number of free parking spaces in the parking allocation index for the neighboring positions around the kth parking space k For the Euclidean distance from the kth parking space to the parking house, epsilon 4 And (3) the weight ratio of the Euclidean distance from the kth parking space to the parking room in the parking allocation index is calculated, and ζ is a correction coefficient of the parking allocation index.
In this embodiment ε 1 +ε 2 +ε 3 +ε 4 =1, k is the number of a parking space in the garage, which has two states: idle stateAnd occupied state->For example, parking space No. 2 is currently in an idle state, and this parking space is then denoted +.>If the parking space No. 2 is currently in an occupied state, this parking space is denoted +.> And->Mutually exclusive values of>I.e. one parking space k at a time allows only one of the two states to occur, for example when +.>In the time-course of which the first and second contact surfaces,when->When (I)>The parking spaces with many surrounding gaps are preferentially selected, so that the parking spaces are convenient and fast to store, move or take the vehicle. The euclidean distance from a parking space to a parking room specifically refers to the spatial linear distance from a last measurement point of the parking space to a corresponding measurement point of the parking room. The Euclidean distance can display the shortest path from the parking space to the parking room to a certain extent, so that the vehicle can be carried to the parking room more quickly when the vehicle is conveniently taken and no vehicle is arranged on the path corresponding to the Euclidean distance. The vehicle type attribute is formulated according to some specifications of the vehicle, such as size, height, weight, etc., to consider whether the parking capability can support the storage of the current target vehicle to be carried. m is the total number of idle parking spaces in adjacent positions around the parking space, wherein the idle parking spaces are used for a double-layer garage, a corridor is arranged before and after the default, and m=1, 2,3 and 4 respectively represent left, right, upper and lower; when there are spaces in front and behind, m=1, 2,3,4,5,6 represent left, right, up, down, front and back, respectively.
Further, the specific analysis method of the path initialization index comprises the following steps: finding out an initial path which can be successfully travelled to a parking room by the current AGV vehicle carrying robot according to a route node topological graph of the garage; constructing a path initialization index model formula, and calculating a path initialization index of an initial path; the specific path initialization index model formula is:in CS D Initializing an index for a path corresponding to the D-th initial path, D being the number of the initial path, d=1, 2,.. 0 ，D 0 For the total number of initial paths +.>For the maximum allowed driving time length on the path, XY is the expected driving time length, alpha 1 JD for weighting factors in path initialization index for predicted travel duration D JD is the total number of nodes corresponding to the D-th initial path 0 Alpha is the minimum total number of nodes allowed by the path 2 And (3) as a weight factor of the total number of nodes in the path initialization index, and ζ is a correction coefficient of the path initialization index corresponding to the D-th initial path.
In the present embodiment, α 1 +α 2 =1, the nodes in the route node topology refer to nodes in which the AGV vehicle transfer robot can make a turn in the travel path. According to the route node topological graph of the garage, a plurality of initial paths from the current AGV vehicle carrying robot position to the parking room can be obtained through a path planning algorithm. The initial path includes all trafficable paths, including not only the shortest path. The path planning algorithm comprises: an algorithm, a depth-first search-based algorithm, a breadth-first search-based algorithm, etc. for finding all traversable paths. And calculating a path initialization index of the initial path according to the total number of nodes on the initial path and the expected running time (i.e. the expected running time) when the AGV vehicle carrying robot parks to adopt the initial path. The importance of the number of nodes on the path is much higher in the path initialization index than in the path initialization index Estimated travel time period: the higher the node number, the greater the path initialization index, the less suitable the initial path is as a path to a parking garage; the longer the estimated travel time, the closer to the maximum allowed travel time on the path, the greater the path initialization index, the less suitable the initial path will be as a path to the parking garage. The smaller the number of nodes on the initial path, the smaller the estimated travel time of the selected vehicle, and the smaller the value of the path initialization index, and the more suitable the initial path is as the first event path. When calculating the path initialization index corresponding to each initial path, setting a threshold value U 0 The path initialization index is lower than the threshold value U 0 Is discarded, only the path initialization index is kept not lower than a certain threshold U 0 And the remaining plurality of initial paths are noted as the first event path.
Further, the specific analysis method of the transportation path planning index comprises the following steps: acquiring position data of a target vehicle to be carried and a target parking space, and analyzing a carrying path by combining a route node topological graph of the garage; acquiring the position data of occupied parking spaces in a garage and numbering; constructing a carrying path planning index model formula, and calculating a carrying path planning index of a carrying path; the specific carrying path planning index model formula is as follows: In the formula GH B Planning an index for a conveyance path corresponding to the B-th conveyance path, B being the number of the conveyance path, b=1, 2, & gt, B 0 ，B 0 For the total number of carrying paths, +.>Representing that the kth parking space on the B-th carrying path is currently in an occupied state; />For the corresponding number of the k parking space in the garage in the occupied state, the number of the k parking space is +.> For the total number of occupied parking spaces, k and +.>Is mapped one by->When->When (I)>Otherwise, the same is done; ω is a correction coefficient of the conveyance path planning index.
In this embodiment, according to the route node topology diagram of the garage, in combination with position data of the target vehicle to be carried and the target parking space, a plurality of carrying paths from the target vehicle to be carried to the target parking space in the current parking garage are obtained through a path planning algorithm. The carrying path includes all trafficable paths, including not only the shortest path. When calculating the planning index of the transport path corresponding to each transport path, setting a threshold value U 1 The transportation path planning index is lower than the threshold value U 1 Is discarded, and only the carrying path planning index is kept not lower than a certain threshold U 1 And the remaining plurality of transport paths are referred to as a second event path.
Further, the specific analysis method of the path condition index comprises the following steps: acquiring the current positions of other AGV vehicle transfer robots and the current running paths of the other AGV vehicle transfer robots, and analyzing path situation indexes corresponding to the current running paths of the current AGV vehicle transfer robots according to the current positions; constructing a path condition index model formula, and calculating a path condition index; the specific path condition index model formula is:in the formula, RO E For the path condition index corresponding to the path in the first event path and the second event path set, E is the set of the first event path and the second event path, SG E SG for total number of crossings of the paths in the first event path and the second event path set with the current travel paths of other AGV vehicle transfer robots 0 To allow for a cross minimum total value χ 0 Is a correction coefficient for the path situation index.
In this embodiment, the path situation index of each path in the set formed by the first event path and the second event path is calculated, and according to the current positions of other AGV vehicle transfer robots, the total number of intersections of the remaining part of the corresponding travel path and the travel path of the current AGV vehicle transfer robot is calculated, the greater the total number of intersections is, the higher the path situation index is, the more obstacles the current AGV vehicle transfer robot encounters on the travel path, the more the number of times that collaborative decisions need to be made is, the more the calculation resources are wasted, and the higher the path situation index is, the more the path is unsuitable as the travel path. And determining the first event path or the second event path with the lowest path condition index as the first event path or the second event path to be driven by the current AGV vehicle carrying robot, and retaining other undetermined first event paths or second event paths. The minimum total allowable crossing value may be 0 at a minimum and must not exceed the total number of AGV vehicle handling robots in the garage at a maximum.
Further, the specific calculation formula of the collaborative decision index is as follows:wherein, XT is the collaborative decision index corresponding to the current AGV vehicle transfer robot, CAI is the conflict avoidance index corresponding to the current AGV vehicle transfer robot, YI is the yield index corresponding to the current AGV vehicle transfer robot.
In this embodiment, if the current AGV vehicle handling robot encounters another AGV vehicle handling robot in the determined first event path or second event path, the travel path collision is generated according to the collaborative decisionAnd judging whether the current AGV vehicle carrying robot needs to wait in situ or avoid the switching path by the index. Travel path collisions refer to other AGV vehicle transfer robots that appear in front of the current AGV vehicle transfer robot's travel path that will obstruct the current AGV vehicle transfer robot and interfere with its normal speed travel. When RE (rare earth) 0 When the collision avoidance index is larger than RE, the current AGV vehicle transfer robot does not need to give way, meanwhile, if the collision avoidance index is smaller than zero, the AGV vehicle transfer robot is enabled to run at a reduced speed, and if the collision avoidance index is not smaller than zero, the AGV vehicle transfer robot is enabled to run at an original speed. When RE (rare earth) 0 No greater than RE: if the yield index is lower than zero and the collision avoidance index is lower than zero, the speed is reduced and the other AGV vehicle carrying robots continue to run after passing through; if the yield index is lower than zero and the collision avoidance index is not lower than zero, keeping the original speed to run on the path and enabling other AGV vehicle carrying robots to smoothly pass through the path crossing position while running, and finishing the waiting condition in the running process; if the yield index is not lower than zero and the conflict avoidance index is lower than zero, the speed is reduced to a newly determined path for running; if the yield index is not lower than zero and the collision avoidance index is not lower than zero, the vehicle runs on the newly determined path while maintaining the original speed.
Further, the specific analysis method of the collision avoidance index comprises the following steps: acquiring real-time positions, running speeds and running paths of the current AGV vehicle carrying robot and other AGV vehicle carrying robots in the garage; according to the estimated collision time obtained by the collision algorithm; constructing a collision avoidance index model formula, and calculating a collision avoidance index; the specific collision avoidance index model formula is:wherein TD is the estimated collision time of the current AGV vehicle transfer robot and other AGV vehicle transfer robots on the travel path, and TD 0 Minimum safe collision time for current AGV vehicle handling robot, < > for>And (5) a correction coefficient for the collision avoidance index.
In this embodiment, the estimated collision time may be obtained by a TTC ranging collision algorithm; the minimum safe collision time refers to the time when the distance between the two is just enough to avoid collision. The predicted collision time can reflect how long the current AGV vehicle handling robot will collide with other AGV vehicle handling robots during travel. The shorter the expected collision time, the smaller the collision avoidance index, meaning that the greater the likelihood that collision avoidance will occur by the AGV vehicle handling robot. When the collision avoidance index is lower than zero, the current AGV vehicle carrying robot is made to run at a reduced speed; when the collision avoidance index is not lower than zero, the current AGV vehicle transfer robot will keep the current speed running.
Further, the specific analysis method of the yield index comprises the following steps: acquiring real-time positions and running paths of the current AGV vehicle carrying robot and other AGV vehicle carrying robots in the garage, and calculating the length of the coincident path according to the real-time positions and the running paths; constructing a yield index model formula, and calculating a yield index; the specific yield index model formula is as follows:in the RE 0 For the task priority of the current AGV vehicle transfer robot, RE is the task priority of the nearest AGV vehicle transfer robot on the current AGV vehicle transfer robot travel path, LF is the overlapping path length of the current AGV vehicle transfer robot and other AGV vehicle transfer robots on the travel path, and LF' is the overlapping path allowed waiting length in the in-situ waiting allowed range of the current AGV vehicle transfer robot.
In the embodiment, the tasks comprise parking, picking up, moving and idle homing, and the task priority is parking>Vehicle taking-out device>Vehicle for moving>Idle homing; the parking finger goes to the parking house and carries the vehicle in the parking house into the target parking space; the vehicle taking finger goes to the parking space and carries the vehicle on the parking space into the parking house; the vehicle moving means that the vehicle on the parking space is transferred to other parking spaces; idle homing refers to the default initial position that an empty task will return to the AGV vehicle transfer robot. When RE (rare earth) 0 When being larger than RE, the current AGV vehicle carrying robot does not existYield for other AGV vehicle transfer robots, namely No fusion; when RE (rare earth) 0 And if the yield index is not greater than RE, waiting for other AGV vehicle carrying robots to pass through, and if the yield index is greater than zero, switching paths. The overlapping path length reflects the path length that overlaps in the travel paths of the current AGV vehicle transfer robot and the other AGV vehicle transfer robots. When the length of the overlapped path is larger than the allowable waiting length of the overlapped path, the current AGV vehicle transfer robot switches paths, namely, a new first event path or second event path is selected from other first event paths or second event paths according to the path condition index, and the selected paths need to be overlapped with the current AGV vehicle transfer robot position.
As shown in fig. 2, in order to provide a structure diagram of a cooperative vehicle-to-vehicle transfer apparatus for a vehicle transfer robot according to an embodiment of the present application, the cooperative vehicle-to-vehicle transfer apparatus for a vehicle transfer robot according to an embodiment of the present application includes: the system comprises a target distribution module, a path initialization calculation module, a carrying path planning module, a path condition and collaborative decision calculation module and a path correction module: the target allocation module: the system comprises a central control system, a AGV vehicle transfer robot, a parking space management system and a parking space management system, wherein the AGV vehicle transfer robot is used for receiving a target vehicle to be transferred and a target parking space distributed to the AGV vehicle transfer robot by the central control system; the path initialization calculation module: the method comprises the steps that a path initialization index is calculated according to a route node topological graph of a garage by an AGV vehicle carrying robot, and a first event path from a current position to a parking room position is analyzed according to the path initialization index; the carrying path planning module is used for: the AGV vehicle transfer robot is used for calculating a transfer path planning index according to a target vehicle to be transferred and a target parking space, and analyzing a second event path from the parking room to the target parking space according to the transfer path planning index; the path condition and collaborative decision calculation module: the method comprises the steps that the AGV vehicle transfer robot is used for acquiring positioning data and task data of other AGV vehicle transfer robots in a garage, and calculating a path condition index and a collaborative decision index; the path correction module: the method comprises the steps that the AGV vehicle carrying robot is used for determining a first event path and a second event path according to the path condition index, and the first event path and the second event path are corrected in real time according to the collaborative decision index in the running process of the AGV vehicle carrying robot.
The technical solution in the embodiments at least has the following technical effects or advantages: relative to publication No.: according to the collaborative handling method and system based on the multiple AGVs disclosed by the invention patent of CN116382295A, whether other robots and switching paths need to be waited or not is determined by using collaborative decision indexes through the robots, and decisions are made according to the predicted collision time, task priority and path overlapping length, so that collaborative operation among the robots is ensured by utilizing information sharing and collaborative decisions, and further the occurrence rate of collaborative path conflict is reduced; relative to publication No.: according to the collaborative planning method for the parking robot disclosed by the invention patent of CN114967666A, a plurality of initial paths including the shortest path and other passable paths are found through the route node topological graph of the garage, so that the robot can reach a destination, flexibility and selectivity are provided for the robot, the probability of path conflict is reduced, and meanwhile, the adaptability of the paths is improved.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. A vehicle-to-vehicle cooperative conveyance method for a vehicle conveyance robot, comprising the steps of:
The AGV vehicle carrying robot receives a target vehicle to be carried and a target parking space distributed to the AGV vehicle carrying robot by the central control system;
the AGV vehicle carrying robot calculates a path initialization index according to a route node topological graph of the garage, and analyzes a first event path from the current position to the parking room position according to the path initialization index;
the AGV vehicle carrying robot calculates a carrying path planning index according to the target vehicle to be carried and the target parking space, and analyzes a second event path from the parking room to the target parking space according to the carrying path planning index;
the AGV vehicle transfer robot acquires positioning data and task data of other AGV vehicle transfer robots in the garage, and calculates a path condition index and a collaborative decision index;
the AGV vehicle carrying robot determines a first event path and a second event path according to the path condition index, and corrects the first event path and the second event path in real time according to the collaborative decision index in the running process of the AGV vehicle carrying robot.
2. The inter-vehicle cooperative transportation method for a vehicle transportation robot according to claim 1, wherein the specific allocation method of the target vehicle to be transported and the target parking space means:
When a new vehicle to be carried exists in a parking room, the central control system selects an AGV vehicle carrying robot with the shortest Euclidean distance from the parking room, and the vehicle to be carried is set as a target vehicle to be carried of the AGV vehicle carrying robot;
the central control system calculates a parking allocation index according to the vehicle data in the current parking room and the parking space data in the garage, and allocates a target parking space for the target vehicle to be carried according to the parking allocation index.
3. The inter-vehicle cooperative transportation method for a vehicle transportation robot according to claim 2, wherein the specific calculation method of the parking allocation index is:
extracting vehicle type attribute data from vehicle data in a current parking garage, and extracting vehicle type attribute data, space number around a parking space and idle parking space position data which can be borne by a parking space from the parking space data in the garage;
the parking spaces and the vehicle type attributes in the garage are respectively numbered,
preprocessing and normalizing the extracted data;
a parking distribution index formula model is built, and the specific parking distribution index model is as follows:
in the formula, PASI k The parking allocation index for the kth parking space in the garage, e is a natural constant, K is the number of the parking space in the garage, k=1, 2, K is the total number of parking spaces, For the kth parking space currently being in the free state, < > for>For the corresponding number of the kth parking space in the garage in an idle state, < >> For the total number of free parking spaces, k and +.>One-to-one mapping of values of ε 1 Weight ratio in parking allocation index for kth parking space currently in idle state, +.>N-th vehicle type attribute supported for the kth parking space, N being a vehicle type attribute number, n=1, 2Total number, epsilon 2 Weight ratio of total number of vehicle type attributes supported for kth parking space in parking allocation index, +.>The number of idle parking spaces in the adjacent positions around the kth parking space is the total number of idle parking spaces in the adjacent positions around the parking space, epsilon 3 Weight ratio, LD, of the number of free parking spaces in the parking allocation index for the neighboring positions around the kth parking space k For the Euclidean distance from the kth parking space to the parking house, epsilon 4 And (3) the weight ratio of the Euclidean distance from the kth parking space to the parking room in the parking allocation index is calculated, and ζ is a correction coefficient of the parking allocation index.
4. A vehicle-to-vehicle cooperative transportation method for a vehicle transportation robot according to claim 3, wherein the specific analysis method of the path initialization index is:
Finding out an initial path which can be successfully travelled to a parking room by the current AGV vehicle carrying robot according to a route node topological graph of the garage;
constructing a path initialization index model formula, and calculating a path initialization index of an initial path;
the specific path initialization index model formula is:
in CS D Initializing an index for a path corresponding to the D-th initial path, D being the number of the initial path, d=1, 2,.. 0 ，D 0 As a total number of the initial paths,for the maximum allowed driving time length on the path, XY is the expected driving time length, alpha 1 JD for weighting factors in path initialization index for predicted travel duration D For the D-th initial pathCorresponding total number of nodes, JD 0 Alpha is the minimum total number of nodes allowed by the path 2 And (3) as a weight factor of the total number of nodes in the path initialization index, and ζ is a correction coefficient of the path initialization index corresponding to the D-th initial path.
5. A vehicle-to-vehicle cooperative transportation method for a vehicle transportation robot according to claim 3, wherein the specific analysis method of the transportation path planning index is:
acquiring position data of a target vehicle to be carried and a target parking space, and analyzing a carrying path by combining a route node topological graph of the garage;
Acquiring the position data of occupied parking spaces in a garage and numbering;
constructing a carrying path planning index model formula, and calculating a carrying path planning index of a carrying path;
the specific carrying path planning index model formula is as follows:
in the formula GH B Planning an index for a conveyance path corresponding to the B-th conveyance path, B being the number of the conveyance path, b=1, 2, & gt, B 0 ，B 0 In order to carry the total number of paths,representing that the kth parking space on the B-th carrying path is currently in an occupied state; />For the corresponding number of the k parking space in the garage in the occupied state, the number of the k parking space is +.> For the total number of occupied parking spaces, k and +.>Is mapped one by->When->When (I)>Otherwise, the same is done; ω is a correction coefficient of the conveyance path planning index.
6. A vehicle-to-vehicle cooperative transportation method for a vehicle transportation robot according to claim 3, wherein the specific analysis method of the path situation index is:
acquiring the current positions of other AGV vehicle transfer robots and the current running paths of the other AGV vehicle transfer robots, and analyzing path situation indexes corresponding to the current running paths of the current AGV vehicle transfer robots according to the current positions;
constructing a path condition index model formula, and calculating a path condition index;
the specific path condition index model formula is:
In the formula, RO E For the path condition index corresponding to the path in the first event path and the second event path set, E is the set of the first event path and the second event path, SG E SG for total number of crossings of the paths in the first event path and the second event path set with the current travel paths of other AGV vehicle transfer robots 0 To allow for a cross minimum total value χ 0 Correction coefficient for path condition index。
7. A vehicle-to-vehicle cooperative transportation method for a vehicle transportation robot according to claim 3, wherein the specific calculation formula of the cooperative decision index is:
wherein, XT is the collaborative decision index corresponding to the current AGV vehicle transfer robot, CAI is the conflict avoidance index corresponding to the current AGV vehicle transfer robot, YI is the yield index corresponding to the current AGV vehicle transfer robot.
8. The inter-vehicle cooperative transportation method for a vehicle transportation robot according to claim 7, wherein the specific analysis method of the collision avoidance index is:
acquiring real-time positions, running speeds and running paths of the current AGV vehicle carrying robot and other AGV vehicle carrying robots in the garage;
according to the estimated collision time obtained by the collision algorithm;
Constructing a collision avoidance index model formula, and calculating a collision avoidance index;
the specific collision avoidance index model formula is:
wherein TD is the estimated collision time of the current AGV vehicle transfer robot and other AGV vehicle transfer robots on the travel path, and TD 0 For the current AGV vehicle transfer robot minimum safe collision time,and (5) a correction coefficient for the collision avoidance index.
9. The inter-vehicle co-transportation method for a vehicle transportation robot according to claim 7, wherein the specific analysis method of the yield index is:
acquiring real-time positions and running paths of the current AGV vehicle carrying robot and other AGV vehicle carrying robots in the garage, and calculating the length of the coincident path according to the real-time positions and the running paths;
constructing a yield index model formula, and calculating a yield index;
the specific yield index model formula is as follows:
in the RE 0 For the task priority of the current AGV vehicle transfer robot, RE is the task priority of the nearest AGV vehicle transfer robot on the current AGV vehicle transfer robot travel path, LF is the overlapping path length of the current AGV vehicle transfer robot and other AGV vehicle transfer robots on the travel path, and LF' is the overlapping path allowed waiting length in the in-situ waiting allowed range of the current AGV vehicle transfer robot.
10. The inter-vehicle collaborative handling device for the vehicle handling robot is characterized by comprising a target distribution module, a path initialization calculation module, a handling path planning module, a path condition and collaborative decision calculation module and a path correction module:
the target allocation module: the system comprises a central control system, a AGV vehicle transfer robot, a parking space management system and a parking space management system, wherein the AGV vehicle transfer robot is used for receiving a target vehicle to be transferred and a target parking space distributed to the AGV vehicle transfer robot by the central control system;
the path initialization calculation module: the method comprises the steps that a path initialization index is calculated according to a route node topological graph of a garage by an AGV vehicle carrying robot, and a first event path from a current position to a parking room position is analyzed according to the path initialization index;
the carrying path planning module is used for: the AGV vehicle transfer robot is used for calculating a transfer path planning index according to a target vehicle to be transferred and a target parking space, and analyzing a second event path from the parking room to the target parking space according to the transfer path planning index;
the path condition and collaborative decision calculation module: the method comprises the steps that the AGV vehicle transfer robot is used for acquiring positioning data and task data of other AGV vehicle transfer robots in a garage, and calculating a path condition index and a collaborative decision index;
The path correction module: the method comprises the steps that the AGV vehicle carrying robot is used for determining a first event path and a second event path according to the path condition index, and the first event path and the second event path are corrected in real time according to the collaborative decision index in the running process of the AGV vehicle carrying robot.
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