CN116449826A - Mobile robot path planning method based on path smoothing and bidirectional jump point search - Google Patents
Mobile robot path planning method based on path smoothing and bidirectional jump point search Download PDFInfo
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Abstract
The invention relates to a mobile robot path planning method based on path smoothing and bidirectional jump point searching, which comprises the following steps: step S1, establishing a grid map representing a working space of the mobile robot, confirming an initial point and a target point of the mobile robot, and initializing prior information; s2, adopting a bidirectional jump point searching strategy to plan a better feasible path from a starting point to an end point by utilizing grid map information in two directions of the starting point and the target point; and S3, optimizing the uneven corners of the positions of the jump points in the better feasible paths by adopting a B spline curve to obtain a final path planning result. Compared with the prior art, the method has the advantages of higher search efficiency, easiness in realization, strong adaptability to complex environments and effective protection of the mechanical structure of robot hardware by path secondary continuity.
Description
Technical Field
The invention relates to the field of path planning, in particular to a mobile robot path planning method based on path smoothing and bidirectional jump point searching.
Background
At present, mobile robots such as Automatic Guided Vehicles (AGVs) are rapidly developed and landed, become an important component of equipment intelligence, and are applied to the fields of industry, agriculture, service, medical treatment and the like. The path planning is one of main research content and basic technology of robot motion control, the path planning takes accessibility as a core, and based on path constraint (such as an obstacle), an optimal space path { theta } of collision-free travel between the head and the tail positions of the robot is planned, wherein theta is the generalized coordinates of the robot, and { theta } is a path point sequence.
Common path planning methods include visual methods, topological methods, heuristic algorithms, intelligent algorithms, local planning algorithms, and the like. Among heuristic algorithms, the a-algorithm is one of the very efficient and commonly used information path planning algorithms, but evaluates a large number of unnecessary nodes when expanding the neighborhood. The jump point search algorithm (JPS) improves the performance of the a-algorithm by screening out valuable nodes, called jump points, and pruning out nonsensical redundant nodes in the exploration space. However, the traditional JPS algorithm can only expand the exploration domain from the starting point and cannot utilize the effective local map information near the end point, so that the searching efficiency is often lower when the traditional JPS algorithm faces a large-scale map or high-density irregular obstacles; meanwhile, the planned path has uneven corners, so that the defects of large memory consumption, long running time, damage to mechanical structures and the like are caused in application.
Therefore, there is a need to design a mobile robot path planning method which can solve the problems of low map information utilization rate and unsmooth path of the traditional algorithm, and has the advantages of reliability, easy implementation and strong adaptability to complex environments
Disclosure of Invention
The invention aims to overcome the defects of low map information utilization rate and unsmooth path in the prior art and provides a mobile robot path planning method based on path smoothing and bidirectional jump point search.
The aim of the invention can be achieved by the following technical scheme:
according to a first aspect of the present invention, there is provided a mobile robot path planning method based on path smoothing and bidirectional jump point search, the method comprising the steps of:
step S1, establishing a grid map representing a working space of the mobile robot, and confirming an initial point and a target point of the mobile robot;
s2, a two-way jump point searching algorithm is adopted, and a better feasible path from the starting point to the end point is planned by utilizing grid map information in two directions of the starting point and the target point;
and S3, optimizing the uneven corners of the positions of the jump points in the better feasible paths by adopting a B spline curve to obtain a final path planning result.
Preferably, said step S2 comprises the following sub-steps:
step S21, initializing prior information of a bidirectional jump point search algorithm, wherein the prior information comprises a grid cost function and a heuristic function;
step S22, initializing a data structure table, which comprises a forward and backward open node table OpenF, an OpenB, a forward and backward closed node table ClosedF, closedB, and forward and backward current optimal jump points curF and curB;
step S23, a parent node of the best jump point currently estimated from the Open table is selected as a target point of the other direction principal iteration, and the current direction jump point is taken in the other direction Closed table as a circulation ending mark, so that information interaction is generated in search of two directions;
and step S24, backtracking the feasible paths according to the parent-child node relation of the ClosedF or ClosedB table to obtain the better feasible paths represented by the jump point set.
Preferably, the mathematical expression of the grid cost function is:
cost＝exp(-αd)×254
wherein alpha is an expansion coefficient, d is the difference between the shortest distance from the geometric center of the mobile robot to the obstacle and the inscribed radius of the mobile robot, and the risk of collision of the robot is represented.
Preferably, the mathematical expression of the heuristic function is:
f(n * )＝h(n * )+g(n * )
where g (n) is a path dissipation function and the expression isn * For the currently estimated node, n is the last estimated node, c is the cost coefficient; h (n) is a heuristic function, and the expression is h (n) =c· (|n) by using Manhattan distance x -goal x |+|n y -goal y I), c is a cost coefficient, and gold is a target point.
Preferably, said step S3 comprises the following sub-steps:
step S31, aiming at a certain specific jump point in the jump point set in the step S24, determining a control node set according to the existence of obstacles around;
s32, adopting Cox-deBoor recursion definition to calculate a B spline basis function N corresponding to each node in the control node set i，k And (t) calculating the whole B spline curve defined by the control node set, traversing the obtained jump point set, generating a smooth path curve and generating a smooth path curve.
Preferably, the step S31 specifically includes:
when no obstacle exists around the current jump point, the current jump point p is used c The midpoint between the adjacent hop point and the last adjacent hop point is the starting node p s Taking the midpoint between the current jump point and the next adjacent jump point as an end node p e The control node set is composed of { p } s ，(p s +p c )/2，p c ，(p c +p e )/2，p e }；
When an obstacle is arranged around the current jump point, if the original track is tangent to the obstacle, the tangent point is taken as a starting point p s Or end point p e Otherwise, the endpoint selects the same as above, and the control node set is { p } s ，(p s +p c )/2，p c ，(p c +p e )/2，p e }。
Preferably, the B-spline basis function N i，k The mathematical expression of (t) is:
where i=0, 1..n, the number of curves k is equal to or greater than 1 and 0/0=0 is defined, satisfying the following conditions
Preferably, the B-spline curve generation expression is as follows:
in the method, in the process of the invention,is a new control node generated by inserting the repeated node for the first time, and meets the following conditions:
wherein,,
according to a second aspect of the present invention there is provided an electronic device comprising a memory and a processor, the memory having stored thereon a computer program, the processor implementing the method of any one of the above when executing the program.
According to a third aspect of the present invention, there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the method of any one of the above.
Compared with the prior art, the invention has the following advantages:
1) The invention effectively utilizes the map information of the starting point and the target point in two directions, has strong adaptability to complex environments, adopts a path planning method combining B spline curve path smoothing and a bidirectional jump point searching algorithm, greatly reduces the path searching space and greatly improves the searching efficiency;
2) According to the invention, the father node of the optimal jump point evaluated from the table Open is selected as the target point of the current round iteration of the other direction, and the jump point of the current direction appears in the Closed table of the other direction as the cycle ending mark, so that information interaction is generated in the search of the two directions, convergence is ensured, and the search efficiency is improved;
3) Taking the jump points as corner points with changed directions, directly generating a control node set, and performing B spline curve path smoothing, so that the method is high in reliability and easy to realize;
4) The second-order continuity of the uniform B spline curve improves the speed and acceleration track of the mobile robot during operation, and protects the mechanical structure of the mobile robot.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a flowchart of an algorithm in an embodiment;
FIG. 3 is a graph of results of an algorithm simulation in an embodiment; wherein, figures 3 a-3 b are respectively graphs of overall results and partial simulation results;
FIG. 4 is a graph comparing the performance of the method of the present invention with the conventional algorithm in average planning of time-consuming indicators;
FIG. 5 is a graph comparing the performance of the method of the present invention with that of the conventional algorithm in terms of average search point index;
FIG. 6 is a graph comparing the performance of the method of the present invention with the conventional algorithm in the average path length index.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Examples
As shown in fig. 1, the present embodiment provides a mobile robot path planning method based on path smoothing and bidirectional jump point search, which includes the following steps:
step S1, establishing a grid map representing a working space of the mobile robot, and confirming an initial point and a target point of the mobile robot;
s2, a two-way jump point searching algorithm is adopted, and a better feasible path from the starting point to the end point is planned by utilizing grid map information in two directions of the starting point and the target point;
and S3, optimizing the uneven corner of the jump point in the preferred feasible path by adopting a B spline curve.
The electronic device of the present invention includes a Central Processing Unit (CPU) that can perform various appropriate actions and processes according to computer program instructions stored in a Read Only Memory (ROM) or computer program instructions loaded from a storage unit into a Random Access Memory (RAM). In the RAM, various programs and data required for the operation of the device can also be stored. The CPU, ROM and RAM are connected to each other by a bus. An input/output (I/O) interface is also connected to the bus.
A plurality of components in a device are connected to an I/O interface, comprising: an input unit such as a keyboard, a mouse, etc.; an output unit such as various types of displays, speakers, and the like; a storage unit such as a magnetic disk, an optical disk, or the like; and communication units such as network cards, modems, wireless communication transceivers, and the like. The communication unit allows the device to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processing unit performs the respective methods and processes described above, for example, the methods S1 to S3. For example, in some embodiments, methods S1-S3 may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device via the ROM and/or the communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of the methods S1 to S3 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to perform methods S1-S3 in any other suitable manner (e.g., by means of firmware).
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a load programmable logic device (CPLD), etc.
Program code for carrying out methods of the present invention may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Example 2
The embodiment provides a mobile robot path planning B based on path smoothing and bidirectional jump point search 2 A JPS method, the method comprising:
1. preparing.
A 20m x 20m static map was prepared, where the start = (2, 1) and end = (20, 19) were the starting points were, and a number of irregular obstacles were designed, including corridor structures (preferences for misleading heuristic functions), U-shaped traps (for testing whether loop iterations would be involved), turns and bumps (for increasing path planning difficulty), as shown in fig. 3.
As shown in fig. 2, providing the start point, the end point and the static grid map data specifically includes the steps of:
step 1, storing start in a forward open node table OpenF, storing gold in a reverse open node table OpenB, respectively initializing a forward and backward closed node table ClosedF, closedB, setting optimal jump points currently evaluated in two directions as curF and curB respectively, setting a B spline curve as a quasi-uniform mode, setting the times as k=3, and setting a smooth point as n=20;
step 2, judging whether the front and back open node tables OpenF and OpenB are empty, if so, no feasible path from the starting point to the target point exists in the working space, and path planning fails;
step 3, judging whether curF is in a reverse open node table OpenB or curB is in a forward open node table OpenF, if at least one condition is met, a feasible path from a starting point to a target point exists, path planning is successful, at the moment, the two directions trace back a path F and a path B according to the parent-child node relation of a forward closed node table ClosedF, closedB respectively, and the path= < path F and path B are spliced to complete planning;
step 4, taking out the jump point with the minimum cost f (n) in the OpenF as curF, and adding ClosedF; taking out the jump point with the minimum cost f (n) in OpenB as curB, and adding ClosedB;
step 5, exploring neighborhood skip points of curF and curB by using a skip point pruning strategy and evaluating, wherein the method specifically comprises the following steps:
A. if the node x is the end point, the node x is the jump point;
B. if there is at least one forced neighbor point of the node x, the node x is a jump point, wherein when the neighbor of the node x has an obstacle, if any path that does not pass through the node x to reach n < p, & gt, n|x > is smaller than the path that passes through the node x to reach n < p, & gt, x, & gt, n > is long, and the node n is not a natural neighbor point of the node x, the neighbor point n is said to be the forced neighbor point of the node x when the parent node is p;
C. in the oblique search, if the node x has a jump point on a horizontal or vertical component, the node x is the jump point;
the node neighborhood adopts an eight neighborhood method;
step 6, if the forward node neighborhood neighbor (curF) exists that the node belongs to ClosedF, not updating OpenF, and similarly, if the backward node neighborhood neighbor (curB) exists that the node belongs to ClosedB, not updating OpenB;
step 7, if the forward node neighborhood neighbor (curF) does not have nodes belonging to ClosedF, further judging whether the forward node neighborhood neighbor (curF) has nodes belonging to OpenF, if not, adding neighbor (curF) to OpenF, if so, judging whether neighbor (curF) is better than the corresponding nodes in OpenF, if so, updating the father node relation and other information of the corresponding nodes in OpenF, and reversely judging the same;
step 8, repeating the steps 2-7 until the path is returned, stopping the embodiment if the path does not exist, and smoothing the next path if the path exists;
step 9, keeping the first and last points of the smooth path unchanged, namely path [0] =start, path [ end ] =gol;
step 10, taking the jump point path- (i) as the current evaluation point p c Let us examine p c The surrounding obstacle situation generates a control node set, and the specific rules are as follows:
when there is no obstacle around the current jump point. At this time with the current jump point p c The midpoint between the adjacent hop point and the last adjacent hop point is the starting node p s Taking the midpoint between the current jump point and the next adjacent jump point as an end node p e The control node set is composed of { p } s ，(p s +p c )/2，p c ，(p c +p e )/2，p e }；
When there is an obstacle around the current jump point. If the original track is tangent to the obstacle, the tangent point is taken as a starting point p s Or end point p e Otherwise, the endpoint selects the same as above, and the control node set is { p } s ，(p s +p c )/2，p c ，(p c +p e )/2，p e }；
Step 11, generating the segment of smooth curve line=deboor (P, k, 10), wherein the deBoor generating algorithm of the B-spline curve specifically includes: initialization iteration number iterThe operation=50, the quasi-uniform node vector T, in the definition domain [ T ] k ，t n+1 ) Internally generating a parameter vector; determining node interval [ t ] where parameter t (r) is located i ，t i+1 ) The method comprises the steps of carrying out a first treatment on the surface of the Calculating new control node generated by inserting repeated node for the first timeThe expression is
Wherein the method comprises the steps ofFurther to a point t on the definition domain r Calculating curve value, the expression is
The smoothed point P (t r ) Is added to the smooth curve line r ))；
Step 12, repeating step 11 until the path jump point set before the smoothing is traversed, and obtaining a smooth path, as shown in fig. 3.
The pseudocode of the bi-directional dynamic skip point search algorithm is specifically shown in table 1 below.
TABLE 1
Mobile robot path planning B based on path smoothing and bidirectional jump point search provided by the invention for verification 2 And the JPS algorithm performance is randomly initialized by selecting grid maps with different scales according to the obstacle rate gamma=0.15. Carrying out 500 path planning experiments on the topography of each scale, and respectively comparing and analyzing an A-time algorithm, a JPS algorithm and a B-time algorithm 2 The JPS algorithm is used for three indexes of average time consumption, average search node number and average path planning length.
As shown in fig. 4, when the grid map size is small, three kinds of calculations are performedThe difference in process performance is not obvious. As the map size increases, the time complexity of the a algorithm increases exponentially, and in a 1000 x 1000 scale map, the time ratio B is planned 2 The JPS algorithm is 20.87 times higher; whereas JPS, B 2 The JPS algorithm is stable in time consumption and amplification, belongs to polynomial complexity, and is characterized by B in large-scale search 2 The advantages of JPS bidirectional searching and quick expansion are reflected and are superior to JPS algorithm.
Another embodiment of the long search time is the number of search points, as shown in fig. 5, the a-algorithm spends far more time on node evaluation than JPS and B 2 JPS algorithm.
As shown in fig. 6, the a algorithm is generally superior to JPS and B in optimality 2 The JPS algorithm because it sacrifices more time to compare more nodes. In a map of 100×100 scale, B 2 The optimality of the JPS algorithm is only 0.97% lost compared to the a-algorithm; in a 1000 x 1000 scale map, B 2 The optimality of the JPS algorithm is 2.68% lost compared to the a-algorithm. B (B) 2 The sacrifice in optimality of the JPS algorithm is negligible compared to the substantial improvement in search time.
Other settings in this embodiment are the same as those in embodiment 1.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. A mobile robot path planning method based on path smoothing and bidirectional jump point searching is characterized by comprising the following steps:
step S1, establishing a grid map representing a working space of the mobile robot, and confirming an initial point and a target point of the mobile robot;
s2, a two-way jump point searching algorithm is adopted, and a better feasible path from the starting point to the end point is planned by utilizing grid map information in two directions of the starting point and the target point;
and S3, optimizing the uneven corners of the positions of the jump points in the better feasible paths by adopting a B spline curve to obtain a final path planning result.
2. A mobile robot path planning method based on path smoothing and bi-directional jump point search according to claim 1, characterized in that said step S2 comprises the sub-steps of:
step S21, initializing prior information of a bidirectional jump point search algorithm, wherein the prior information comprises a grid cost function and a heuristic function;
step S22, initializing a data structure table, which comprises a forward and backward open node table OpenF, an OpenB, a forward and backward closed node table ClosedF, closedB, and forward and backward current optimal jump points curF and curB;
step S23, a parent node of the best jump point currently estimated from the Open table is selected as a target point of the other direction principal iteration, and the current direction jump point is taken in the other direction Closed table as a circulation ending mark, so that information interaction is generated in search of two directions;
and step S24, backtracking the feasible paths according to the parent-child node relation of the ClosedF or ClosedB table to obtain the better feasible paths represented by the jump point set.
3. The mobile robot path planning method based on path smoothing and bidirectional jump point search according to claim 2, wherein the mathematical expression of the grid cost function is:
cost＝exp(-αd)×254
wherein alpha is an expansion coefficient, d is the difference between the shortest distance from the geometric center of the mobile robot to the obstacle and the inscribed radius of the mobile robot, and the risk of collision of the robot is represented.
4. The mobile robot path planning method based on path smoothing and bidirectional jump point search according to claim 2, wherein the mathematical expression of the heuristic function is:
f(n * )＝h(n * )+g(n * )
where g (n) is a path dissipation function and the expression isn * For the currently estimated node, n is the last estimated node, c is the cost coefficient; h (n) is a heuristic function, and the expression is h (n) =c· (|n) by using Manhattan distance x -goal x |+|n y -goal y I), c is a cost coefficient, and gold is a target point.
5. A mobile robot path planning method based on path smoothing and bi-directional jump point search according to claim 2, characterized in that said step S3 comprises the sub-steps of:
step S31, aiming at a certain specific jump point in the jump point set in the step S24, determining a control node set according to the existence of obstacles around;
s32, adopting Cox-deBoor recursion definition to calculate a B spline basis function N corresponding to each node in the control node set i，k And (t) calculating the whole B spline curve defined by the control node set, traversing the obtained jump point set, generating a smooth path curve and generating a smooth path curve.
6. The mobile robot path planning method based on path smoothing and bidirectional jump point search according to claim 5, wherein the step S31 is specifically:
when no obstacle exists around the current jump point, the current jump point p is used c The midpoint between the adjacent hop point and the last adjacent hop point is the starting node p s Taking the midpoint between the current jump point and the next adjacent jump point as an end node p e The control node set is composed of { p } s ，(p s +p c )/2，p c ，(p c +p e )/2，p e }；
If there is an obstacle around the current jump point, if the original trackTangent to the obstacle with the tangent point as the starting point p s Or end point p e Otherwise, the endpoint selects the same as above, and the control node set is { p } s ，(p s +p c )/2，p c ，(p c +p e )/2，p e }。
7. The mobile robot path planning method based on path smoothing and bi-directional jump point search of claim 5, wherein the B-spline basis function N i，k The mathematical expression of (t) is:
where i=0, 1..n, the number of curves k is equal to or greater than 1 and 0/0=0 is defined, satisfying the following conditions
8. The mobile robot path planning method based on path smoothing and bi-directional jump point search of claim 7, wherein the B-spline curve generation expression is as follows:
in the method, in the process of the invention,is a new control node generated by inserting the repeated node for the first time, and meets the following conditions:
wherein,,
9. an electronic device comprising a memory and a processor, the memory having stored thereon a computer program, characterized in that the processor, when executing the program, implements the method according to any of claims 1-8.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any one of claims 1-8.
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| CN116922398A (en) * | 2023-09-15 | 2023-10-24 | 华侨大学 | Rope robot and path planning method and device thereof |
| CN116922398B (en) * | 2023-09-15 | 2023-12-22 | 华侨大学 | Rope robot and path planning method and device thereof |
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