CN115083139A - Multi-vehicle scheduling method - Google Patents

Multi-vehicle scheduling method Download PDF

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CN115083139A
CN115083139A CN202110268430.1A CN202110268430A CN115083139A CN 115083139 A CN115083139 A CN 115083139A CN 202110268430 A CN202110268430 A CN 202110268430A CN 115083139 A CN115083139 A CN 115083139A
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李建勋
李滢
金顾敏
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Shanghai Jiaotong University
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Abstract

A multi-vehicle scheduling method includes the steps of constructing a layout map of vehicles and a closed space according to parameter information of current vehicles and layout information of parking environments, designing priority rules, calculating priorities of the vehicles according to the rules, and selecting the vehicles needing to be taken out of a warehouse. Planning a collision-free path which accords with kinematic constraint of each vehicle by adopting a punitive mixed A-algorithm; and according to the vehicle priority, sequentially adopting an s-t diagram strategy to obtain the conflict-free tracks of the multiple vehicles in space-time, and finishing the dispatching of the multiple vehicles for going out of the garage. The invention plans the path of the vehicle, and configures the speed by using the S-T chart strategy, thereby avoiding other vehicles, completing the integral dispatching of a plurality of vehicles and realizing the high-efficiency delivery without collision.

Description

Multi-vehicle scheduling method
Technical Field
The invention relates to a technology in the field of multi-vehicle control, in particular to a punishment-based hybrid A * An algorithm and an S-T diagram strategy multi-vehicle scheduling method.
Background
The existing multi-vehicle scheduling problem can be divided into two aspects of single-vehicle path planning and multi-vehicle scheduling. The path planning problem can be described as: on the premise that the environment is known or partially known, given the starting point and the target end point, a collision-free shortest path from the starting point to the end point is found. The multi-vehicle scheduling aims to solve the problem that the multiple vehicles move simultaneously and interfere with each other when leaving the garage simultaneously, ensure that all vehicles leave the garage orderly and rapidly, reduce the total time of leaving the garage and improve the efficiency. The path planning in the current scheduling method often does not meet the kinematics characteristics of the vehicles, or the phenomenon that the path is not smooth and reasonable enough exists, and the scheduling of multiple vehicles is not rapid enough.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a multi-vehicle scheduling method, which is used for planning the path of a vehicle, configuring the speed by using an S-T chart strategy, avoiding other vehicles, completing the overall scheduling of a plurality of vehicles and realizing the efficient delivery without collision.
The invention is realized by the following technical scheme:
the invention relates to a punishment-based hybrid A * The multi-vehicle scheduling method based on the algorithm and the S-T chart strategy comprises the steps of constructing a layout map of vehicles and a closed space according to parameter information of the current vehicles and layout information of parking environments, designing priority rules, calculating the priority of each vehicle according to the rules, and selecting the vehicles needing to be delivered. With punishment of mixing A * An algorithm, planning a collision-free path of each vehicle according with kinematic constraints; and according to the vehicle priority, sequentially adopting an s-t diagram strategy to obtain the conflict-free tracks of the multiple vehicles in space-time, and finishing the dispatching of the multiple vehicles for going out of the garage.
The invention relates to a system for realizing the method, which comprises the following steps: priority computational element, route planning unit and speed planning unit, wherein: the priority calculating unit is connected with the path planning unit and transmits vehicle priority information and information of vehicles needing to go out of the garage, and the path planning unit is connected with the speed planning unit and transmits the path information of the vehicles.
Technical effects
The invention integrally solves the problems that the planned vehicle running path is not smooth enough and the speed in the running process in the prior artToo frequent switching. The present invention utilizes improved A compared to the prior art * The speed of the vehicle is planned by the algorithm and the S-T diagram, so that the switching of the driving states of the vehicle is reduced under the condition that the overall time is not influenced, the vehicle is more suitable for the actual situation, the speed planning which can not be realized physically is not generated, and the overall speed of multiple vehicles leaving the garage is improved.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a layout map of a plurality of vehicles of the embodiment;
FIG. 3 is a schematic diagram of an S-T diagram of an embodiment;
FIG. 4 is a schematic illustration of a single vehicle path plan of an embodiment;
FIG. 5 is a schematic diagram of an embodiment of speed configuration using an S-T diagram.
Detailed Description
As shown in FIG. 1, the embodiment relates to a penalty-based blending A * The multi-vehicle scheduling method based on the algorithm and the S-T graph strategy specifically comprises the following steps:
and step S1, acquiring the parameter information of the current vehicle and the layout information of the parking environment, and constructing a layout map of the vehicle and the closed space.
As shown in fig. 2, the map of the present embodiment has a length and a width of 54m and 24m, respectively, and 6 vehicles are parked in the map space, and the vehicles are represented by rectangles, and other obstacles are represented by the smallest polygons that can include the obstacles: the vehicle is represented by a rectangle 10m long and 3.5m wide, which is numbered A, B, C, D, E, F from top to bottom and from left to right, and the enclosed space has only a single exit.
And step S2, designing corresponding priority rules according to the task requirements, the parking positions of the vehicles and the performances of the vehicles, calculating the priority of each vehicle according to the rules, and selecting the vehicles needing to be taken out of the warehouse.
The task requirements in this embodiment refer to: and (5) taking the four vehicles out of the garage.
The parking position of the vehicle is as follows: arranged with the vehicle heads facing outward in parallel, as shown in fig. 2, the vehicles are at varying distances from a single exit.
The performance of the vehicle comprises: maintenance condition of the vehicle, equipment assembly condition, and mileage.
The design principle is that vehicles with good state performance meeting the task requirement number are driven out of the garage as soon as possible, namely the overall vehicle out-of-garage time is the minimum, and the obtained priority rule is as follows: e i ＝b 1 P i +b 2 C i Wherein: e i Is the final composite index, C, of each vehicle i The position index of the vehicle is obtained according to P by comprehensively evaluating the performance of the vehicle i ＝(|x i (t fi )-x i (0)|+|y i (t fi )-y i (0) I))/2, wherein: | x i (t fi )-x i (0)|、|y i (t fi )-y i (0) L is the distance of the midpoint of the rear axle of each vehicle relative to the exit midpoint, b 1 ,b 2 Is a weight coefficient representing a tradeoff between vehicle performance and vehicle position.
In the present embodiment, the start and end positions and performance indexes of 6 vehicles are shown in table 1:
TABLE 1 vehicle parameter table
Figure BDA0002973267300000021
Figure BDA0002973267300000031
The weight coefficient is set as b 1 ＝0.4，b 2 At 0.6, the composite index of each vehicle is obtained, and the four vehicles A, E, C, F with the highest composite index are selected for delivery, and their priorities are assigned, as shown in table 2:
TABLE 2 vehicle combination index and priority
Figure BDA0002973267300000032
Step S3, punitive mixing A * And (4) an algorithm is used for planning a collision-free path of each vehicle, which meets the requirements of the position and attitude angle of the terminal point.
The kinematic constraint comprises:
Figure BDA0002973267300000033
Figure BDA0002973267300000034
|a i (t)|≤a maxi ，|ν i (t)|≤ν maxi ，|ω i (t)|≤ω maxi wherein: (x) i (t),y i (t)) is the midpoint coordinate of the rear axle of the vehicle at time t, θ i (t) is the attitude angle of the vehicle in the XOY coordinate system, v i (t) and a i (t) velocity and acceleration of the vehicle along the longitudinal axis of the body,
Figure BDA0002973267300000035
for the front wheel yaw angle of the vehicle, in the positive counter-clockwise direction, correspondingly, ω i (t) is the front wheel yaw angular velocity, L is the front and rear wheel wheelbase.
The requirement for meeting the position and attitude angle of the terminal point is as follows: satisfy (x) i (t fi ),y i (t fi ),θ i (t fi ))＝(x * ,y * ,θ * )。
The punitive mixture A * The algorithm specifically comprises the following steps:
step S3-1: discretizing the map constructed in the step S1, adding the initial pose of the vehicle to be planned at this time as an initial node into an open list, and calculating the heuristic value of the current node.
The discretization treatment refers to the following steps: the map is converted into a plurality of discrete nodes by dividing the map x, y and the vehicle longitudinal axis direction angle theta at minute intervals Δ x, Δ y and Δ theta, respectively.
The open list refers to: for storing a list of nodes that have been sought on a map
The lower priority vehicles are considered static obstacles and the path plan of the vehicle may not reach the nodes occupied by the obstacles.
The calculation of the heuristic value specifically comprises: n is a radical of child .f＝N child .g+N child .h，N child· g＝N current· g+length+penalty，N child .h＝|N child .S-S end L, wherein: the heuristic value f consists of a g function value and an h function value, and the h function value is only the distance between the s value of the node and the s value of the terminal point; the function value of g is the total path length from the starting point to the current node, N current· g is a g function value of a father node, length is the length from the father node to a child node, and penalty value penalty is the penalty of reversing in the S-T graph path searching process.
Step S3-2: selecting the node with the minimum heuristic value from the open list as a current node, removing the open list, adding a close list, and when the current node is a target node, turning to the step S3-5, otherwise, continuing to the step S3-3;
step S3-3: generating a shortest path which is in accordance with kinematic constraint from the current node to the target node by using a Reeds-Shepp curve, and turning to the step S3-5 when the Reeds-Shepp curve can just avoid all barriers; otherwise, continuing to expand the current node into 6 child nodes according to a preset node expansion mode.
The Reeds-Shepp curve refers to: all the arrangement and combination modes of the circular arcs and the straight line segments are summarized into 48 types, so that connection of any starting point and end point poses on a plane can be realized, the shortest pose can be guaranteed in the meaning of vehicle kinematics, and the obstacle avoidance requirement cannot be guaranteed.
The preset node expansion mode is as follows: the vehicle driving system corresponds to two forward and backward moving directions and three steering directions of left turning, right turning and straight going in the moving process of the vehicle, and corresponds to 6 expansion nodes in 6 driving states.
Step S3-4: when the expanded child node collides with the obstacle, namely the expanded child node is overlapped with the node occupied by the obstacle or the child node is already in the close table, ignoring the child node; when the child node is not in the open table, adding the open table, and calculating and recording the heuristic value and the father node of the child node; when the extended child node is already in the open table, the heuristic value of the node in the original open table is compared, and if the current heuristic value is smaller, the node is updated, and then the process goes to step S3-2.
Step S3-5: and (5) successfully searching the path and outputting a collision-free path.
As shown in fig. 4, after the target pose nodes are searched, a path plan satisfying the vehicle kinematic constraint from the starting point to the end point is obtained by backtracking in a mode of taking father nodes one by one.
Step S4: after the path planning is completed, sequentially adopting an s-t diagram strategy to obtain the conflict-free tracks of a plurality of vehicles in space and time according to the vehicle priority, and completing the dispatching of a plurality of vehicles out of the garage, which specifically comprises the following steps:
step S4-1: regarding the vehicle with the speed to be planned at present, according to the collision-free path obtained in the step S3, the vehicle with higher priority is regarded as a moving obstacle, and the path and the time are discretized to obtain an S-T graph in combination with other moving obstacles, wherein S is the set path planned by the vehicle step S3, the path is discretized by a small mileage interval Δ S, the same T is the driving time of the vehicle, and the time is discretized by a small time interval Δ T to obtain an S-T grid graph with T as a horizontal axis and S as a vertical axis. Starting the track of the vehicle with higher priority from the time t equal to 0 to the final time t equal to t end And judging whether the position of the vehicle with high priority at the moment is overlapped with the position of the currently planned vehicle or not at a tiny time interval delta T, combining the overlapped moment and the corresponding driving mileage S into coordinates (S, T) when the overlapping occurs, and marking the coordinates in an S-T diagram to indicate that other vehicles occupy the path at the moment.
Step S4-2: an open list and a close list are first created, and the origin (0, 0) in the S-T graph is added to the open list.
Step S4-3: from modified A * Extracting the node with the minimum heuristic value at the moment from an open list of the algorithm, selecting the node with the minimum heuristic value from the open list as the current node and moving out an open columnTable, adding close list when S ═ S of current node at the moment end Go to step S4-6, otherwise continue to step S4-4;
step S4-4: expanding the current node according to an improved node expansion mode, expanding 3 child nodes and calculating corresponding heuristic values: comparison with conventional A * In the eight-neighborhood expansion mode of the algorithm, because the time can not be backed and the driving speed can not tend to be infinite, three neighborhoods of the upper right, the upper right and the lower right are selected to expand child nodes, and the child nodes correspond to forward driving, parking and backing in the actual driving condition.
Step S4-5: for each child node, when the child node exists in a close table or belongs to an obstacle node in an S-T graph, the child node is ignored; when the child node is not in the open table, adding the open table, and recording the heuristic value and the father node of the child node; when the extension child node is already in the open table, comparing the heuristic value of the node in the original open table, and when the current heuristic value is smaller, updating the node. The iteration continues, proceeding to step S4-3.
Step S4-6: and successfully searching the path in the S-T diagram, and configuring the speed for the vehicle according to the path searched in the S-T diagram.
As shown in fig. 5, a speed configuration is planned for the vehicle using the S-T diagram, and the vehicle can travel along the route planned in step S3 to the destination without collision depending on the result of the speed configuration.
Through specific practical experiments, when the method is operated according to the vehicle parking condition shown in fig. 2 and the parameters in table 1, the vehicle path is smoother under the condition of ensuring the whole time, the switching times of the driving state of the vehicle can be reduced by half, and no reversing behavior occurs.
Compared with the prior art, the method considers the kinematic constraint of the vehicle, can complete path planning for the vehicle with incomplete constraint, has smooth path, and does not frequently have reversing and steering behaviors. The speed configuration of the vehicle of the path meeting the pose requirement of the vehicle leaving warehouse is more reasonable by considering the pose requirement of the vehicle, and the braking and backing behaviors can be reduced under the condition of ensuring the minimum overall time.
The foregoing embodiments may be modified in many different ways by one skilled in the art without departing from the spirit and scope of the invention, which is defined by the appended claims and not by the preceding embodiments, and all embodiments within their scope are intended to be limited by the scope of the invention.

Claims (9)

1. A multi-vehicle scheduling method based on a punishment type mixed A-algorithm and an S-T map strategy is characterized in that a layout map of vehicles and a closed space is constructed according to parameter information of current vehicles and layout information of parking environments, priority rules are designed, the priority of each vehicle is calculated according to the rules, vehicles needing to be delivered out of a warehouse are selected, and a collision-free path which accords with kinematic constraints of each vehicle is planned by adopting the punishment type mixed A-algorithm; and according to the vehicle priority, sequentially adopting an s-t diagram strategy to obtain the conflict-free tracks of the multiple vehicles in space-time, and finishing the dispatching of the multiple vehicles for going out of the garage.
2. The multi-vehicle scheduling method based on the penalty hybrid a-algorithm and S-T graph strategy according to claim 1, which specifically comprises:
step S1: acquiring parameter information of a current vehicle and layout information of a parking environment, and constructing a layout map of the vehicle and a closed space;
step S2: designing a corresponding priority rule according to the task requirement, the parking position of the vehicle and the performance of the vehicle, calculating the priority of each vehicle according to the rule, and selecting the vehicle needing to be taken out of the warehouse;
step S3: a punishment type mixed A-x algorithm is adopted to plan a collision-free path which accords with kinematic constraint of each vehicle, namely the requirements of the position and the attitude angle of a terminal point are met;
step S4: after the path planning is finished, sequentially adopting an s-t diagram strategy to obtain the conflict-free tracks of a plurality of vehicles in space and time according to the vehicle priority, and finishing the dispatching of a plurality of vehicles to leave the garage.
3. The method of claim 1, wherein the parking position is selected from the group consisting of: the vehicle heads are arranged in parallel outwards, as shown in fig. 2, the distances of the vehicles relative to a single outlet are different; the performance of the vehicle comprises: maintenance condition of the vehicle, equipment assembly condition, and mileage;
the design principle is that vehicles with good state performance meeting the task requirement number are driven out of the garage as soon as possible, namely the overall vehicle out-of-garage time is the minimum, and the obtained priority rule is as follows: e i ＝b 1 P i +b 2 C i Wherein: e i Is the final composite index, C, of each vehicle i The position index of the vehicle is obtained according to P by comprehensively evaluating the performance of the vehicle i ＝(|x i (t fi )-x i (0)|+|y i (t fi )-y i (0) I))/2, wherein: | x i (t fi )-x i (0)|、|y i (t fi )-y i (0) L is the distance of the midpoint of the rear axle of each vehicle relative to the exit midpoint, b 1 ，b 2 Is a weight coefficient representing a tradeoff between vehicle performance and vehicle position.
4. A method for multi-vehicle scheduling based on a penalized hybrid a-x algorithm and S-T graph strategy according to claim 1 or 2, wherein said kinematic constraints comprise:
Figure FDA0002973267290000021
Figure FDA0002973267290000022
|a i (t)|≤a maxi ，|ν i (t)|≤ν maxi ，|ω i (t)|≤ω maxi wherein: (x) i (t)，y i (t)) is the midpoint coordinate of the rear axle of the vehicle at time t, θ i (t) is the attitude angle of the vehicle in the XOY coordinate system, v i (t) and a i (t) speed of the vehicle along the longitudinal axis of the body and the sumThe speed of the motor is controlled by the speed of the motor,
Figure FDA0002973267290000023
for the front wheel yaw angle of the vehicle, in the positive counter-clockwise direction, correspondingly, ω i (t) is the front wheel deflection angle speed, L is the front and rear wheel wheelbase;
the requirement for meeting the position and attitude angle of the terminal point is as follows: satisfies (x) i (t fi )，y i (t fi )，θ i (t fi ))＝(x*，y*，θ*)。
5. The method for dispatching multiple vehicles based on penalty hybrid a-algorithm and S-T graph strategy according to any one of claims 1 to 4, wherein the penalty hybrid a-algorithm specifically comprises:
step S3-1: discretizing the map constructed in the step S1, adding the initial pose of the vehicle to be planned as an initial node into an open list, and calculating the heuristic value of the current node;
step S3-2: selecting the node with the minimum heuristic value from the open list as a current node, removing the open list, adding a close list, and when the current node is a target node, turning to the step S3-5, otherwise, continuing to the step S3-3;
step S3-3: generating a shortest path which accords with the kinematic constraint and is from the current node to the target node by using the Reeds-Shepp curve, and turning to the step S3-5 when the Reeds-Shepp curve can just avoid all barriers; otherwise, continuing to expand the current node into 6 child nodes according to a preset node expansion mode;
step S3-4: when the expanded child node collides with the obstacle, namely the expanded child node is overlapped with the node occupied by the obstacle or the child node is already in a close table, ignoring the child node; when the child node is not in the open table, adding the open table, and calculating and recording the heuristic value and the father node of the child node; when the extended child node is already in the open table, comparing the heuristic value of the node in the original open table, if the current heuristic value is smaller, updating the node, and then going to step S3-2;
step S3-5: and (5) successfully searching the path and outputting a collision-free path.
6. The method of claim 5, wherein the discretization process is selected from the group consisting of: dividing the map in the x direction and the y direction and the angle theta of the longitudinal axis direction of the vehicle at intervals of delta x, delta y and delta theta respectively, and converting the map into a plurality of discrete nodes; the lower priority vehicles are considered static obstacles and the path plan of the vehicle may not reach the nodes occupied by the obstacles.
7. The method of claim 5, wherein the heuristic value calculation specifically comprises: n is a radical of child· f＝N child· g+N child· h，N child· g＝N current· g+length+penalty，N child· h＝|N child· S-S end L, wherein: the heuristic value f consists of a g function value and an h function value, and the h function value is only the distance between the s value of the node and the s value of the terminal point; the function value of g is the total path length from the starting point to the current node, N current· g is a g function value of a father node, length is the length from the father node to a child node, and penalty value penalty is the penalty of reversing in the S-T graph path searching process.
8. The method for multi-vehicle scheduling based on the penalty hybrid a-algorithm and S-T graph strategy according to claim 5, wherein the step 4 specifically comprises:
step S4-1: regarding the vehicle with the current speed to be planned, according to the collision-free path obtained in the step S3, regarding the vehicle with higher priority as a moving obstacle, and combining other moving obstacles to discretize the path and time to obtain an S-T diagram, wherein S is the determined path planned by the vehicle step S3, the path is discretized by a minute mileage interval Δ S, the same T is the vehicle travel time, the time is discretized by a minute time interval Δ T to obtain the path with T as the horizontal axis, and S is the horizontal axisAn S-T grid plot of the vertical axis; starting the track of the vehicle with higher priority from the time t equal to 0 to the final time t equal to t end Judging whether the position of the vehicle with high priority at the moment is overlapped with the position of the vehicle planned at present or not at a tiny time interval delta T, combining the overlapped moment and the corresponding driving mileage S into a coordinate (S, T) when the overlapping occurs, and marking the coordinate (S, T) in an S-T diagram to indicate that other vehicles occupy the path at the moment;
step S4-2: firstly, creating an open list and a close list, and adding an origin (0, 0) in an S-T diagram into the open list;
step S4-3: extracting the node with the minimum heuristic value at the moment from an open list of the improved A-star algorithm, selecting the node with the minimum heuristic value from the open list as the current node, removing the open list, adding a close list, and adding S-S of the current node at the moment end Go to step S4-6, otherwise continue to step S4-4;
step S4-4: expanding the current node according to an improved node expansion mode, expanding 3 child nodes and calculating corresponding heuristic values: compared with an eight-neighborhood expansion mode of a common A-star algorithm, due to the characteristic that time cannot be backed off and the driving speed cannot tend to infinity, three neighborhoods of the upper right, the upper right and the lower right are selected to expand child nodes, and the child nodes correspond to forward driving, parking and backing in the actual driving condition;
step S4-5: for each child node, when the child node exists in a close table or belongs to an obstacle node in an S-T graph, the child node is ignored; when the child node is not in the open table, adding the open table, and recording the heuristic value and the father node of the child node; when the expansion child node is already in the open table, comparing the heuristic value of the node in the original open table, and when the current heuristic value is smaller, updating the node; continuing iteration, and turning to the step S4-3;
step S4-6: and successfully searching the path in the S-T diagram, and configuring the speed for the vehicle according to the path searched in the S-T diagram.
9. A system for implementing the method of any preceding claim, comprising: priority computational element, route planning unit and speed planning unit, wherein: the priority calculating unit is connected with the path planning unit and transmits vehicle priority information and information of vehicles needing to go out of the garage, and the path planning unit is connected with the speed planning unit and transmits the path information of the vehicles.
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