CN112965500B - Path planning method and device with must-pass point set and additional hard constraints - Google Patents
Path planning method and device with must-pass point set and additional hard constraints Download PDFInfo
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
Abstract
The invention relates to a path planning method and equipment with a must-pass point set and additional hard constraints, wherein the path planning method comprises the following steps: s1: in the preprocessing stage, the actual application environment information is converted into mathematical description, and the processing and modeling of the undirected weighted graph of the problem are completed; s2: and in the shortest path solving stage, a random search algorithm is used for solving the shortest path, the feasibility of the path is judged in real time in the searching process, and the shortest path problem of the necessary point set is solved under the condition of meeting the requirement of extra hard constraint, so that the shortest path meeting the requirement is finally obtained. The method can add extra hard constraint, and can ensure to obtain a calculation result meeting the engineering requirement under the actual physical limitation condition in shorter calculation time.
Description
Technical Field
The invention relates to the fields of operational research, computer science, geographic information science and transportation, in particular to a method and equipment for planning a path which has a must-pass point set and additional hard constraints.
Background
The shortest path problem is always a research hotspot in the fields of operation research, computer science, geographic information science, transportation and the like, and is widely applied to the actual problems of public transportation network planning, unmanned automatic driving, robot autonomous navigation and the like. In real life, many problems can be abstractly converted into a shortest path problem, and path planning requires finding a route with the minimum cost from a starting position to a target position in a given real environment according to certain optimization criteria. How to effectively calculate and solve the problem of the shortest path, the difficulty of research is how to find a relatively complete solution in a relatively short time. According to different mastery degrees of the environment information, path planning can be divided into global path planning and local path planning, and the global path planning and the local path planning are not essentially different. The classical global path planning algorithm comprises a Dijkstra algorithm, an A-x algorithm and the like, and can statically plan an optimal or suboptimal route. In recent years, scholars at home and abroad introduce heuristic algorithms, bionic algorithms and the like on the basis of the above, provide various shortest path improvement algorithms such as a simulated annealing method, an ant colony algorithm, a genetic algorithm, a particle swarm algorithm, a depth priority algorithm, a breadth priority algorithm and the like, and have good effect on solving the problem of the shortest path of a must-pass point set. In the process of exploring the path planning algorithm, an additional hard constraint condition exists in an actual application scenario due to the limitation of physical conditions, and when the algorithm including the granted invention patent CN201710535060.7, "a method for solving k shortest path in consideration of multiple types of constraints" is used for processing such problems, since all possible sequences are selected and then the shortest path is solved, the method has a very large amount of calculation, and has the problems of high solution difficulty, slow solution speed, unreliable solution result, even infeasibility, and the like.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method and equipment for path planning with a necessary point set and additional hard constraints.
The purpose of the invention can be realized by the following technical scheme:
a path planning method with a must-pass point set and additional hard constraints comprises the following steps:
s1: in the preprocessing stage, a corresponding undirected weighted graph and additional hard constraints are obtained according to actual application environment information;
s2: and a shortest path solving stage, namely solving the shortest path on the undirected weighted graph by using a random search algorithm based on depth priority, judging the feasibility of the path in real time in the searching process, and solving to obtain the shortest path of the must-pass point set under the condition of meeting the requirement of additional hard constraint.
Preferably, the step S1 includes:
converting actual application environment information into mathematical description, modeling and abstracting the mathematical description into a non-directional weighted graph, and storing graph information and node information of the non-directional weighted graph through a two-dimensional adjacency matrix;
and defining variables including a starting point, an end point, a must-pass point set and additional hard constraints according to the path planning requirement and the practical application environment information.
Preferably, the two-dimensional adjacency matrix stores the distance between any two adjacent points in the undirected weighted graph, and when the two points are not adjacent, the distance value is-1, and the distance value between the point and the two points is 0.
Preferably, the additional hard constraints are obtained based on physical condition limits in practical application scenarios, the physical condition limits including unmanned vehicle body mechanical dimensions, turning limits, and road restriction states.
Preferably, the initial parameters in the shortest path solving include a start point start, a face point next, an end point destination, a must pass point set point _ list [ ] and an additional hard constraint set point _ constraint [ ] storing sub-paths containing additional hard constraints.
Preferably, the element contained in the additional hard constraint set point _ constraint [ ] is a sub-path formed by N consecutive nodes, where N is a set length.
Preferably, the step S2 includes:
s2.1: initializing a searching state, enabling the number of times of effective paths to return to zero, and clearing all temporary path variables and node variables;
s2.2: judging whether the path length L is smaller than the upper limit L _ limit of the path length, if not, returning to the step S2.1, and if so, executing the step S2.3;
s2.3: randomly exploring the next non-previous point and replacing the searching state;
s2.4: judging whether the terminal is reached, if not, returning to the step S2.2, and if so, executing the step S2.5;
s2.5: judging whether all the essential points pass through, if not, returning to the step S2.2, and if so, executing the step S2.6;
s2.6: judging whether the additional hard constraint is met, if so, executing the step S2.7, otherwise, returning to the step S2.2;
s2.7: comparing the length of the currently obtained path with the length of the known shortest path, if the current path is shorter, updating the current path to be the shortest path, otherwise, keeping the original shortest path;
s2.8: the number t of effective paths is increased by 1;
s2.9: and judging whether the effective path frequency t is less than the upper limit t _ limit of the effective path frequency, if so, returning to the step S2.1, and if not, outputting the shortest path obtained by final resolving.
Preferably, the judging whether the additional hard constraint is satisfied is specifically:
and randomly intercepting a sub-path with the length of N from the current path, judging whether the sub-path is an element in the extra hard constraint set, if not, meeting the extra hard constraint, and if so, not meeting the extra hard constraint, wherein N is a set length.
Preferably, the set length is 3.
The present invention also provides an electronic device comprising:
one or more processors, memory, and one or more programs stored in the memory, the one or more programs including instructions for performing a path planning method as described with a set of must-pass points and with additional hard constraints.
The present invention also provides a computer readable storage medium comprising one or more programs for execution by one or more processors of an electronic device, the one or more programs including instructions for performing the method for path planning with an additional hard constraint on the must-pass set of points.
Compared with the prior art, the invention has the following beneficial effects:
1. in consideration of physical condition limitation in an actual application scene, the method introduces an additional hard constraint condition and models the additional hard constraint condition into a corresponding sub-path number object, and successfully finds an optimal or suboptimal scheme of a must-pass point set under the condition of meeting the requirement of the additional hard constraint;
2. the method converts an actual application scene into mathematical description, adopts a random search algorithm based on depth-first search, judges the feasibility of a path in real time in the search process, and finds a route with the minimum cost from an initial position to a target position in a given real environment;
3. the method has the advantages of simple calculation, high operation speed, good real-time performance and accurate judgment.
Drawings
FIG. 1.1 is a schematic view of an application scenario for unmanned vehicle path planning of the present invention;
FIG. 1.2 is a schematic view of a plurality of connected areas on a track;
FIG. 1.3 is a non-directional weighted graph of the present invention;
FIG. 2 is a schematic flow chart of the present invention.
Fig. 3 is a schematic diagram of a shortest path solution result of a set of unnecessary points after an additional hard constraint condition is introduced in the embodiment.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Referring to fig. 2, the present embodiment provides a path planning method with a must-pass point set and additional hard constraints, including:
step S1: and in the preprocessing stage, the actual application environment information is converted into mathematical description, and the processing and modeling of the undirected weighted graph of the problem are completed.
S1.1: the method comprises the steps of converting actual application environment information into mathematical description, modeling and abstracting a region position graph into a non-directional weighted graph, wherein a weight is a distance between a path point and a path point in the region graph, converting a time optimization target into a total path optimization target and abstracting the problem into a shortest path problem of a necessary passing point set on the assumption that the driving speeds of unmanned vehicles on all the paths are consistent.
S1.2: variables are defined according to the path planning requirements, including a starting point, an end point, a must-pass point set and an additional hard constraint condition of the path planning.
In the embodiment, the additional hard constraint condition is modeled as a corresponding sub-path number object in a list in consideration of physical condition limitations in an actual application scenario, such as a mechanical size, a turning limitation and a road restriction state of the unmanned vehicle body, and is set as an initial parameter, as shown in table 1.
Table 1 variable definitions
S1.3: converting the graph information and the node information in the undirected weighted graph into a two-dimensional adjacency matrix for storage, specifically:
1) obtaining the number length of the nodes in the graph, and then using a list with the dimension of length to store adjacent points of each point;
2) searching any non-traversed point in the graph, judging the distance between the point and the rest points, if the distance is greater than 0, representing that two points are adjacent, and adding the rear point into an adjacent point list of the front point;
3) repeating the steps until all the points in the graph are traversed;
4) obtaining a complete neighbor point list and storing the complete neighbor point list into choices [ ] [ ] list variables;
5) obtaining initial parameters: the method comprises the steps of starting a start point, a next facing point, an end point destination and a must-pass point sequence number list stored in a point _ list [ ], and the information of the graph is stored in a file in a two-dimensional adjacent matrix dis [ ] [ ] form and is read.
S1.4: and modeling according to the additional hard constraint condition to obtain the sub-path containing the additional hard constraint, numbering the object for the corresponding sub-path, storing the object in a list point _ constraint [ ], and setting the object as an initial parameter. Typically, three consecutive points are used as constraints (N ═ 3) and the midpoint is used as an index. For example, in practice, path 2 → 3 → 11 is not feasible, and point _ constraint [3] contains [2,3,11 ]; path 3 → 11 → 5 is not feasible, so point _ constraint [11] contains [3,11,5], and so on.
Step S2: and in the shortest path solving stage, a random search algorithm based on depth priority is used for solving the shortest path of the undirected weighted graph completed by modeling, the feasibility of the path is judged in real time in the searching process, and the shortest path problem of the unnecessary point set is solved under the condition of meeting the requirement of extra hard constraint, so that the shortest path meeting the requirement is finally obtained.
S2.1: initializing a searching state, enabling the number of times of effective paths to return to zero, and clearing all temporary path variables and node variables;
s2.2: judging whether the path length L is smaller than the upper limit L _ limit of the path length, if not, returning to the step S2.1, and if so, executing the step S2.3;
s2.3: randomly exploring a next non-previous point variable, replacing the searched state, wherein the current distance d is increased by 1, and the exploring mode is to randomly select one from a list of adjacent points of the current point after the previous point is excluded;
s2.4: judging whether the terminal is reached, if not, returning to the step S2.2, and if so, executing the step S2.5;
s2.5: executing a must-pass point set judgment function, judging whether all must-pass point sets point _ list [ ] have passed, judging each point in the must-pass point sets one by one, if not, returning to the step S2.2, and if so, executing the step S2.6;
s2.6: executing a hard constraint condition state judgment function, judging whether an additional hard constraint condition point _ constraint is met, specifically, randomly intercepting a sub-path with the length of 3, judging whether the sub-path exists in the point _ constraint [ ], if not, returning to the step S2.2, and if so, executing the step S2.7;
s2.7: executing a shortest path solving function and comparing the length of the currently obtained path with the length of the known shortest path, if the current path is shorter, updating the current path to be the shortest path, and if not, keeping the original shortest path;
s2.8: the number t of effective paths is increased by 1;
s2.9: and judging whether the effective path frequency t exceeds the effective path frequency upper limit t _ limit, if so, returning to the step S2.1, and if not, outputting the shortest path obtained by final resolving.
The present embodiment applies the above method to a path planning for unmanned vehicles.
Fig. 1.1 is a schematic view of an application scenario of unmanned vehicle path planning. The unmanned vehicle model plays a match in the field of the scene with the length and width of 17m multiplied by 10.5m, and the track is formed by communicating a plurality of areas and is distributed as shown in figure 1.2. The departure direction of the unmanned vehicle and a starting area are designated at random, and the starting area, the ending area, and a plurality of areas to be passed by midway are given. And requiring the unmanned vehicle to start from the starting area and start timing, planning a reasonable path, passing through a plurality of designated areas on the track and then reaching a designated end area, stopping timing, judging the completion of the competition, and calculating the achievement according to the completion time.
The track area position map can be abstracted into an undirected weighted map, as shown in fig. 1.3, and the weight is the distance between points in the track map. And if the driving speeds of the unmanned vehicles on the track sections are consistent, converting the time optimization goal into a total distance optimization goal, and abstracting the problem into the shortest path problem of the necessary passing point set.
Meanwhile, there are additional hard constraints in this embodiment scenario: due to the limitation of the width size of the track, the unmanned vehicle cannot turn around to run in the running process; due to the physical condition limitation of the turning radius of the unmanned vehicle model, the unmanned vehicle cannot turn a curve with the turning angle less than 90 degrees on the actual track. Therefore, the solution to the shortest path problem over the set of must-pass points is limited by the additional hard constraints. Defining related algorithm variables, setting a starting point node, a point-facing node, a must-pass point set, a destination area and the like of path planning in an algorithm, and calculating a feasible optimal path according to the parameters; in the calculation process, the shortest path among the currently searched path, the known path and the final optimal shortest path are respectively defined. In order to facilitate the processing of a program and the calculation of data, all the abstracted information of the forward-band-free right-weight graph is converted into a two-dimensional adjacent matrix, and the distance between any two adjacent points in the graph is stored in the matrix. When the two points are not adjacent, the distance value is set to-1; the distance value of a point from itself is set to 0.
The calculation results of the specific example are shown in fig. 3. In this embodiment, the starting point, the facing point, the end point, and the inevitable point set are set as follows according to the parameters in fig. 1: starting point start is 1, facing point next is 2, end destination is 1, must pass point set point _ list is [3,4,5,6,7,8,9,10,11], and hard constraint point _ constraint is [ (2,3,11), (3,11,5) ]. Compared with algorithms such as a hill climbing method, a simulated annealing method (SA), a Genetic Algorithm (GA) and the like, the algorithms can find that the shortest path is [1,2,10,11,3,4,5,6,7,8,9], as shown by a dotted line in fig. 3, but obviously seen in the figure, the intermediate passing path [. multidot., [2, 10,11,. ] does not meet the extra hard constraint condition, and the unmanned vehicle cannot rotate the racing track corner at the position. The optimal result of the shortest path given by the algorithm provided by the invention is as follows: [1,2,10,7,6,5,4,3,11,10,8,9], as shown by the solid line in FIG. 3, wherein all paths do not violate the extra hard constraint, the must-pass point set shortest path problem with extra hard constraints is solved in a targeted manner.
The above-described functions, if implemented in the form of software functional units and sold or used as a separate product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations can be devised by those skilled in the art in light of the above teachings. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (7)
1. A path planning method with a must-pass point set and additional hard constraints is characterized by comprising the following steps:
s1: in the preprocessing stage, a corresponding undirected weighted graph and additional hard constraints are obtained according to actual application environment information;
s2: a shortest path solving stage, which is to solve the shortest path on the undirected weighted graph by using a random search algorithm based on depth priority, judge the feasibility of the path in real time in the searching process, solve and obtain the shortest path of the must-pass point set under the condition of meeting the requirement of extra hard constraint, wherein the initial parameters during the shortest path solving comprise a starting point start, a facing point next, an end point destination, a must-pass point set point _ list [ ] and an extra hard constraint set point _ constraint [ ] for storing sub-paths containing extra hard constraint;
the step S1 includes:
converting actual application environment information into mathematical description, modeling and abstracting the mathematical description into a non-directional weighted graph, and storing graph information and node information of the non-directional weighted graph through a two-dimensional adjacency matrix;
defining variables according to the path planning requirement and the practical application environment information, wherein the variables comprise a starting point, an end point, a must-pass point set and additional hard constraints of the path planning;
the step S2 includes:
s2.1: initializing a searching state, enabling the number of times of effective paths to return to zero, and clearing all temporary path variables and node variables;
s2.2: judging whether the path length L is smaller than the upper limit L _ limit of the path length, if not, returning to the step S2.1, and if so, executing the step S2.3;
s2.3: randomly exploring the next non-previous point and replacing the searching state;
s2.4: judging whether the terminal is reached, if not, returning to the step S2.2, and if so, executing the step S2.5;
s2.5: judging whether all the essential points pass through, if not, returning to the step S2.2, if so, executing the step S2.6;
s2.6: judging whether the additional hard constraint is met, if so, executing the step S2.7, otherwise, returning to the step S2.2;
s2.7: comparing the length of the currently obtained path with the length of the known shortest path, if the current path is shorter, updating the current path to be the shortest path, otherwise, keeping the original shortest path;
s2.8: the number t of effective paths is increased by 1;
s2.9: and judging whether the effective path frequency t is less than the upper limit t _ limit of the effective path frequency, if so, returning to the step S2.1, and if not, outputting the shortest path obtained by final resolving.
2. The method of claim 1, wherein the two-dimensional adjacency matrix stores the distance between any two adjacent points in the undirected weighted graph, and when two points are not adjacent, the distance is-1 and the distance between the point and itself is 0.
3. A method for path planning with a set of must-pass points and additional hard constraints according to claim 1, wherein the additional hard constraints are obtained based on physical constraints in a practical application scenario, the physical constraints including unmanned vehicle body mechanical dimensions, turn constraints, and road restriction status.
4. The method as claimed in claim 1, wherein the additional hard constraint set point _ constraint [ ] includes elements of N consecutive nodes forming a sub-path, where N is a predetermined length.
5. The method of claim 1, wherein the determining whether the extra hard constraint is satisfied is specifically:
and randomly intercepting a sub-path with the length of N from the current path, judging whether the sub-path is an element in the extra hard constraint set, if not, meeting the extra hard constraint, and if so, not meeting the extra hard constraint, wherein N is a set length.
6. A method for path planning with extra hard constraints on a must-pass point set according to claim 4 or 5, wherein the set length is 3.
7. An electronic device, comprising:
one or more processors, memory, and one or more programs stored in the memory, the one or more programs including instructions for performing the method for path planning with overrequired set of points and additional hard constraints as recited in any of claims 1-6.
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