CN118068854A - Unmanned aerial vehicle obstacle avoidance path planning method based on artificial potential field - Google Patents
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Abstract
The invention discloses an unmanned aerial vehicle obstacle avoidance path planning method based on an artificial potential field, which comprises the following steps: constructing a gravitational field and a repulsive field; constructing and initializing a planning task table, and storing a graph Network of a planning result; traversing the planning task table, acquiring sub-target points of the obstacle, and iteratively updating the planning task table; according to the planning task table, carrying out iterative loop planning paths based on an artificial potential field method; traversing all paths from a starting point to an ending point in the Network, and screening according to set evaluation conditions to obtain an optimal path. According to the invention, the sub-target points are introduced based on the artificial potential field method, so that the probability of the artificial potential field algorithm sinking into the local minimum is greatly reduced, the problem of the local minimum of the traditional artificial potential field is solved, and the global path planning of the unmanned aerial vehicle under the complex obstacle environment is realized.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle path planning, in particular to an unmanned aerial vehicle obstacle avoidance path planning method based on an artificial potential field.
Background
Conventional path planning representative algorithms include an a-star algorism (a-star algorism), dikstra Algorithm, artificial potential field method, and bionic ant colony Algorithm Q. The artificial potential field method is a simple and effective method in a path planning algorithm.
In unmanned aerial vehicle path planning applications, the basic idea of the artificial potential field method is to construct an artificial potential field in the working environment of an unmanned aerial vehicle, wherein the potential field comprises a repulsive force pole and an attractive force pole, the area and the obstacle into which the unmanned aerial vehicle is not expected to enter are defined as the repulsive force pole, and the target and the area into which the unmanned aerial vehicle is recommended to enter are defined as the attractive force pole, so that the unmanned aerial vehicle in the potential field is subjected to the combined action of the target pose gravitational field and the repulsive force field around the obstacle, and advances towards the target.
The artificial potential field method is used as a current common path planning algorithm, and is widely applied to vehicle obstacle avoidance planning and unmanned plane path planning due to low algorithm complexity and simple calculation. However, the existing algorithm structure has obvious defects that the planning is failed due to the fact that local minimum values are very easy to be trapped, complex obstacle conditions cannot be processed, and iterative optimization cannot be achieved. These disadvantages result in that the artificial potential field method can only be applied to local path planning, and is not suitable for global path planning in complex obstacle environments.
Disclosure of Invention
In order to solve the problems, the invention provides the unmanned aerial vehicle obstacle avoidance path planning method based on the artificial potential field, introduces the idea of global planning based on the artificial potential field method, expands the planning capacity of the artificial potential field method, improves the performance of an algorithm, and can be applied to the global path planning of the unmanned aerial vehicle in a complex obstacle environment.
The invention provides an unmanned aerial vehicle obstacle avoidance path planning method based on an artificial potential field, which comprises the following specific technical scheme:
s1: constructing an artificial potential field, including constructing a gravitational field and constructing a repulsive field;
S2: the generation path is specifically as follows:
S201: constructing and initializing a planning task table Graph Network (graph theory data structure) storing the planning result;
In the planning task table, O represents a starting point and G represents an ending point;
In Network, nodes are a start point, an end point and sub-targets, and edges store paths between nodes.
The planning task table is a set of task start and task end tuple elements.
S202: traversing the planning task table, and ending if the planning task table is empty; if the task is not empty, traversing and taking out a task, and setting a task starting point and a task end point, wherein the task starting point is the current position of the unmanned aerial vehicle;
S203: connecting a task starting point and a task ending point to obtain a connecting line segment, and detecting whether an obstacle intersected with the connecting line segment exists in the repulsive force field; if there is an intersecting obstacle, acquiring sub-target points of the intersecting obstacle, and if there is no intersecting obstacle, executing step S205;
s204: taking the sub-target point as an end point, taking the set task start point as a start point, adding the set task start point into the planning task table, and returning to the step S202;
s205: according to the planning task table, carrying out iterative loop planning paths based on an artificial potential field method;
S3: traversing all paths from a starting point to an ending point in the Network, and screening according to set evaluation conditions to obtain an optimal path.
Further, the unmanned aerial vehicle is in the gravitational fieldThe gravitational potential field at this point is expressed as follows:
wherein, Representing the position of unmanned plane in the repulsive field,/>Representing the position of the target point.
Further, the repulsive field is constructed as follows:
s101: setting a safety distance, a working distance and an obstacle vector;
The safety distance refers to the minimum distance between the unmanned aerial vehicle and the obstacle during path planning, namely the distance between the unmanned aerial vehicle and the obstacle during path planning is always greater than or equal to the set safety distance;
The action distance refers to the distance of the unmanned aerial vehicle when the unmanned aerial vehicle is close to the obstacle, namely the distance is used for describing how far away the obstacle is from the unmanned aerial vehicle when the unmanned aerial vehicle is close to the obstacle;
the obstacle vector refers to a vector which forms a threat obstacle environment and comprises a polygon, a circle and a sector.
S102: each obstacle vector is regarded as an obstacle object, and the distance between every two obstacles is calculated. If the obstacles intersect, the distance is considered to be 0.
S103: establishing an R-tree spatial index of the obstacle object;
S104: according to the spatial index, acquiring all barriers with the distance smaller than or equal to the action distance from the unmanned aerial vehicle, and calculating the nearest points of all barriers from the unmanned aerial vehicle;
s105: the repulsive force potential field of the unmanned aerial vehicle in the repulsive force field is constructed and expressed as follows:
Wherein m represents the number of obstacles within the effective working distance, T represents the working distance, S represents the safety distance, The euclidean distance between the closest point of the obstacle i from the position of the unmanned aerial vehicle and the position of the unmanned aerial vehicle is represented.
Further, in step S102, the obstacle having a distance less than twice the safety distance is merged into one obstacle.
Further, the acquiring the sub-target points of the intersected obstacle is specifically as follows:
judging whether the intersected obstacle generates a sub-target point or not, if so, returning to the execution step S202, and traversing the next task; if the intersected obstacle does not generate the sub-target point, generating the sub-target point based on the intersected obstacle.
Further, the generation of the sub-target point is as follows:
Obtaining an obstacle nearest to a task starting point, obtaining two tangents from the task starting point to the obstacle, and obtaining two sub-target points based on two points of safe distances between the two tangents to the outer side of the obstacle respectively in the direction perpendicular to the tangents.
Each obstacle vector may only generate a sub-target once.
Further, in step S203, if there are a plurality of intersecting obstacles in the obstacle field intersecting with the connecting line segment, a step of acquiring an obstacle closest to the task start point and executing a step of acquiring a sub-target point of the intersecting obstacle based on the obstacle.
Further, in step S205, a path is planned based on the artificial potential field method, and the specific process is as follows:
Initializing the current position of the unmanned aerial vehicle as a task starting point;
calculating the negative derivative of the potential field at the current position of the unmanned aerial vehicle;
the unmanned aerial vehicle moves a fixed step length along the direction of the negative derivative, the position of the unmanned aerial vehicle is updated, and the negative derivative of the potential field at the position after the unmanned aerial vehicle moves the fixed step length is calculated again after the unmanned aerial vehicle moves, until the unmanned aerial vehicle reaches a task end point;
And adding the task starting point and the task ending point to nodes in the Network, judging whether the task ending point is a path ending point or not, if not, adding a tuple consisting of the task ending point and the path ending point to the planning task table, and returning to the step S202.
Further, in step S205, after updating the position of the unmanned plane, the method further includes determining a local minimum value;
If the unmanned aerial vehicle is sunk into the local minimum value, the unmanned aerial vehicle cannot reach the task end point; searching the nearest obstacle according to the position of the local minimum value, judging whether the obstacle generates a sub-target point or not, and returning to the step S202 if the sub-target point is generated; if not, generating a sub-target point according to the obstacle, adding a tuple consisting of the task starting point and the sub-target point into the planning task table, and returning to the step S202.
Further, in step S3, the set evaluation conditions include shortest total path length and minimum steering;
The total path length is calculated as follows:
statistics of the number of turns are calculated as follows:
Where n represents the number of nodes on the total path.
The beneficial effects of the invention are as follows:
1. The safety distance during obstacle avoidance can be accurately defined through the constructed gravitational field function and the repulsive force field function, the obstacle avoidance maneuvering region is defined, the problem that the targets of the traditional artificial potential field cannot be reached is solved, and meanwhile a basis is provided for creating sub-target points.
2. In path generation, by introducing sub-target points, the probability of trapping a local minimum value of an artificial potential field algorithm is greatly reduced, and even if the artificial potential field algorithm is trapped in the local minimum value, a local minimum value area can be jumped out by calculating the sub-target points, so that the problem of the local minimum value of the traditional artificial potential field is solved; meanwhile, by acquiring all paths from the starting point to the end point and searching the optimal path line according to the set conditions, the algorithm has a global field of view and is not affected by local, and global planning of the paths is realized.
3. In the path generation, whether the obstacle generates a sub-target point is judged, and based on the obstacle which does not generate the sub-target point and is intersected closest to the task starting point, a sub-target point is generated, and the algorithm can adapt to a complex obstacle environment by generating the sub-target point.
Drawings
FIG. 1 is a schematic overall flow diagram of the method of the present invention.
Fig. 2 is a schematic diagram of generation of sub-target points under the polygonal obstacle of the present invention.
FIG. 3 is a schematic diagram of generation of sub-target points under a circular obstacle of the present invention.
Fig. 4 is a schematic diagram of generation of a sub-target point under a fan-shaped obstacle of the present invention.
FIG. 5 is a schematic diagram of generation of sub-target points under the combined obstacle of the present invention.
Detailed Description
In the following description, the technical solutions of the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the embodiments of the present invention, it should be noted that, the indicated orientation or positional relationship is based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship conventionally put in use of the product of the present invention as understood by those skilled in the art, merely for convenience of describing the present invention and simplifying the description, and is not indicative or implying that the apparatus or element to be referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like, are used merely for distinguishing between descriptions and not for understanding as indicating or implying a relative importance.
In the description of the embodiments of the present invention, it should also be noted that, unless explicitly specified and limited otherwise, the terms "disposed," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; may be directly connected or indirectly connected through an intermediate medium. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
The invention provides a preferred embodiment, and discloses an unmanned aerial vehicle obstacle avoidance path planning method based on an artificial potential field.
As shown in fig. 1, the method mainly comprises three parts of constructing an artificial potential field, generating a path and evaluating and selecting the path.
The method comprises the following specific steps:
S1: constructing an artificial potential field;
wherein the construction of the artificial potential field comprises the construction of a gravitational field and the construction of a repulsive field;
The gravitational field is constructed as follows:
The target point is recorded as The arbitrary position of the unmanned plane is/>The attractive potential field of the drone at position U is then represented as follows:
The negative derivative of the gravitational potential field is:
the repulsive field is constructed as follows:
s101: setting a safety distance S, a working distance T and an obstacle vector;
The safety distance S refers to the minimum distance between the unmanned aerial vehicle and the obstacle during path planning, namely the distance between the unmanned aerial vehicle and the obstacle during path planning is always greater than or equal to the set safety distance;
The action distance T refers to the distance when the unmanned aerial vehicle is in the process of approaching the obstacle, namely the distance is used for describing how far away the obstacle is from the unmanned aerial vehicle in the process of approaching the obstacle;
The obstacle vector refers to a vector which forms a threat to the obstacle environment and comprises a polygon, a circle, a fan and the like.
S102: each obstacle vector is regarded as an obstacle object, and the distance between every two obstacles is calculated.
If the obstacles intersect, the distance is considered to be 0.
In a preferred embodiment, in step S102, obstacles having a distance less than twice the safety distance are combined into one obstacle.
S103: establishing an R-tree spatial index of the obstacle object;
S104: according to the spatial index, acquiring all barriers with the distance less than or equal to the action distance T from the unmanned aerial vehicle, namely searching any position where the unmanned aerial vehicle is located as follows Is less than or equal to the action distance T, and is denoted asWherein m represents the number of obstacles within the effective working distance,/>Representing an obstacle set, namely, a set of all obstacles with the distance from the unmanned plane less than or equal to the action distance T;
Calculation of Distance of all obstacle/noIs marked as the nearest point of (2); N is/>Distance of all obstaclesIs a set of points of the closest points of (a),
According to any position of unmanned aerial vehicleAnd the coordinates of the closest points of all obstacles from the unmanned plane position, the Euclidean distance between the two is calculated as follows:
wherein, Represents the i-th obstacle distance/>X-axis coordinates of closest point of/>Representation of i-th obstacle distance/>The y-axis coordinate of the nearest point of (2);
s105: constructing a gravitational field;
at any position of the unmanned plane The gravitational potential field at this point is expressed as follows:
the negative derivative of the repulsive potential field is:
S2: the generation path is specifically as follows:
S201: constructing and initializing a planning task table Graph Network (graph theory data structure) storing the planning result;
In the planning task table, O represents a starting point and G represents an ending point;
In Network, nodes are a start point, an end point and sub-targets, and edges store paths between nodes.
The planning task table is a set of task start and task end tuple elements.
S202: traversing the planning task table, and judging whether the planning task table is empty;
If the planning task table is empty, ending;
If the task is not empty, a task is taken out, a task starting point and a task ending point are set, the task starting point is recorded as U, the task ending point is recorded as V, and the task starting point is the current position of the unmanned aerial vehicle.
S203: connecting a task starting point U and a task ending point V to obtain a connecting line segment UV;
detecting whether an obstacle intersected with a connecting line segment UV exists in the repulsive force field;
If one or more intersected barriers exist, searching for the intersected barrier closest to U, and judging whether the barrier generates a sub-target point or not;
if the intersected obstacle generates a sub-target point, returning to the execution step S202, and traversing the next task;
if the intersecting obstacles do not generate sub-target points, generating sub-target points based on the intersecting obstacles 、/>；
As a preferred embodiment, in conjunction with fig. 2,3,4 and 5, the generation process of the sub-targets is as follows:
obtaining the obstacle nearest to the task starting point U, and obtaining two tangents from the task starting point U to the obstacle And/>；
The area range of the included angle extension formed by the tangent lines can cover the obstacle;
taking two points based on the safe distance between the two tangent points towards the outer side of the obstacle respectively in the direction perpendicular to the tangent line to obtain two sub-target points And/>。
Wherein each obstacle vector may only be generated once sub-objects according to the above-described manner.
If there is no obstacle to intersection, the jump proceeds to step S205;
In a preferred embodiment, in the above-mentioned process, if there are a plurality of intersecting obstacles in the repulsive field intersecting with the connecting line segment, the obstacle closest to the task start point U is acquired, and the step of acquiring the sub-target point of the intersecting obstacle is performed based on the obstacle.
S204: adding to the planning task tableAnd/>Returning to step S202;
s205: according to the planning task table, carrying out iterative loop planning paths based on an artificial potential field method;
Specifically, initializing the current position of the unmanned aerial vehicle to a task starting point U, and in path planning, the current position can be called as a planning starting point;
calculating the negative derivative of the potential field at the current position of the unmanned aerial vehicle;
The unmanned aerial vehicle moves a fixed step length along the direction of the negative derivative, the position of the unmanned aerial vehicle is updated, the negative derivative of the potential field at the position after the unmanned aerial vehicle moves the fixed step length is calculated again after the unmanned aerial vehicle moves, until the unmanned aerial vehicle reaches a task end point V, and the unmanned aerial vehicle can be called as a planning end point in path planning;
if the unmanned aerial vehicle successfully reaches a planning terminal V, U, V nodes are added into the Network, and a planning result of an artificial potential field method is stored at the UV side;
and judging whether the planning terminal V is the terminal G or not, if not, adding a tuple consisting of V, G to the planning task table, and returning to the step S202.
In this embodiment, after updating the position of the unmanned aerial vehicle, determining a local minimum value is further included;
If the unmanned aerial vehicle is sunk into the local minimum value, the unmanned aerial vehicle cannot reach the planning terminal point V, and the nearest obstacle is searched according to the position of the local minimum value;
Judging whether the obstacle generates a sub-target point, and returning to the step S202 if the sub-target point is generated; if the sub-target point is not generated, generating a sub-target point according to the obstacle, adding the tuple consisting of U and the sub-target point to the planning task table, and returning to step S202.
Specifically, the local minimum value is judged by judging whether the gravitational potential energy of the unmanned aerial vehicle before and after movement is increased, namely whether the gravitational potential energy meets the requirement; Wherein k represents the number of times the unmanned plane moves when the path is generated;
If it meets And describing the situation that potential energy is increased, and sinking the unmanned aerial vehicle into a local minimum value.
S3: traversing all paths from a starting point to an ending point in the Network, and screening according to set evaluation conditions to obtain an optimal path.
The set evaluation conditions comprise shortest total path length and least steering;
for the shortest total path, all the airlines on the path are spliced together to form a waypoint point set Wherein n is the number of waypoints.
The total path length is calculated as follows:
For the minimum cost of steering, the number of turns is calculated as follows:
wherein, Representing the previous node coordinates of the current path,/>Representing the coordinates of the following nodes of the current path,/>Representing the previous node coordinates of the last path, i.e./>Coordinates of a previous node;
and selecting the route with the least cost as a final planning result.
The invention is not limited to the specific embodiments described above. The invention extends to any novel one, or any novel combination, of the features disclosed in this specification, as well as to any novel one, or any novel combination, of the steps of the method or process disclosed.
Claims (10)
1. The unmanned aerial vehicle obstacle avoidance path planning method based on the artificial potential field is characterized by comprising the following steps of:
s1: constructing an artificial potential field, including constructing a gravitational field and constructing a repulsive field;
S2: the generation path is specifically as follows:
S201: constructing and initializing a planning task table Storing a graph Network of the planning result;
In the planning task table, O represents a starting point and G represents an ending point;
In the Network, nodes are a starting point, an end point and various sub-targets, and the edges store paths between the nodes;
S202: traversing the planning task table, and ending if the planning task table is empty; if the task is not empty, traversing and taking out a task, and setting a task starting point and a task end point, wherein the task starting point is the current position of the unmanned aerial vehicle;
S203: connecting a task starting point and a task ending point to obtain a connecting line segment, and detecting whether an obstacle intersected with the connecting line segment exists in the repulsive force field; if there is an intersecting obstacle, acquiring sub-target points of the intersecting obstacle, and if there is no intersecting obstacle, executing step S205;
s204: taking the sub-target point as an end point, taking the set task start point as a start point, adding the set task start point into the planning task table, and returning to the step S202;
s205: according to the planning task table, carrying out iterative loop planning paths based on an artificial potential field method;
S3: traversing all paths from a starting point to an ending point in the Network, and screening according to set evaluation conditions to obtain an optimal path.
2. The unmanned aerial vehicle obstacle avoidance path planning method based on artificial potential field as claimed in claim 1, wherein the unmanned aerial vehicle is in a gravitational fieldThe gravitational potential field at this point is expressed as follows:
wherein, Representing the position of unmanned plane in the repulsive field,/>Representing the position of the target point.
3. The unmanned aerial vehicle obstacle avoidance path planning method based on an artificial potential field according to claim 1, wherein the repulsive field is constructed as follows:
s101: setting a safety distance, a working distance and an obstacle vector;
s102: each obstacle vector is regarded as an obstacle object, and the distance between every two obstacles is calculated;
s103: establishing an R-tree spatial index of the obstacle object;
S104: according to the spatial index, acquiring all barriers with the distance smaller than or equal to the action distance from the unmanned aerial vehicle, and calculating the nearest points of all barriers from the unmanned aerial vehicle;
s105: the repulsive force potential field of the unmanned aerial vehicle in the repulsive force field is constructed and expressed as follows:
Wherein m represents the number of obstacles within the effective working distance, T represents the working distance, S represents the safety distance, The euclidean distance between the closest point of the obstacle i from the position of the unmanned aerial vehicle and the position of the unmanned aerial vehicle is represented.
4. A method of unmanned aerial vehicle obstacle avoidance path planning based on artificial potential fields as claimed in claim 3, wherein in step S102, obstacles less than twice the safe distance are combined into one obstacle.
5. The unmanned aerial vehicle obstacle avoidance path planning method based on artificial potential field according to claim 1, wherein the acquiring the sub-target points of the intersecting obstacle is specifically as follows:
judging whether the intersected obstacle generates a sub-target point or not, if so, returning to the execution step S202, and traversing the next task; if the intersected obstacle does not generate the sub-target point, generating the sub-target point based on the intersected obstacle.
6. The unmanned aerial vehicle obstacle avoidance path planning method based on artificial potential field of claim 5, wherein the generation of the sub-target points is as follows:
Obtaining an obstacle nearest to a task starting point, obtaining two tangents from the task starting point to the obstacle, and obtaining two sub-target points based on two points of safe distances between the two tangents to the outer side of the obstacle respectively in the direction perpendicular to the tangents.
7. The unmanned aerial vehicle obstacle avoidance path planning method according to any one of claims 1 to 6, wherein in step S203, if there are a plurality of intersecting obstacles in the obstacle repellent field intersecting with the connecting line segment, the obstacle nearest to the task start point is acquired, and the step of acquiring the sub-target point of the intersecting obstacle is performed based on the obstacle.
8. The unmanned aerial vehicle obstacle avoidance path planning method based on artificial potential field of claim 1, wherein in step S205, the path is planned based on artificial potential field method, and the specific process is as follows:
Initializing the current position of the unmanned aerial vehicle as a task starting point;
calculating the negative derivative of the potential field at the current position of the unmanned aerial vehicle;
the unmanned aerial vehicle moves a fixed step length along the direction of the negative derivative, the position of the unmanned aerial vehicle is updated, and the negative derivative of the potential field at the position after the unmanned aerial vehicle moves the fixed step length is calculated again after the unmanned aerial vehicle moves, until the unmanned aerial vehicle reaches a task end point;
And adding the task starting point and the task ending point to nodes in the Network, judging whether the task ending point is a path ending point or not, if not, adding a tuple consisting of the task ending point and the path ending point to the planning task table, and returning to the step S202.
9. The unmanned aerial vehicle obstacle avoidance path planning method based on artificial potential field of claim 8, wherein in step S205, after updating the position of the unmanned aerial vehicle, the method further comprises determining a local minimum value;
If the unmanned aerial vehicle is sunk into the local minimum value, the unmanned aerial vehicle cannot reach the task end point; searching the nearest obstacle according to the position of the local minimum value, judging whether the obstacle generates a sub-target point or not, and returning to the step S202 if the sub-target point is generated; if not, generating a sub-target point according to the obstacle, adding a tuple consisting of the task starting point and the sub-target point into the planning task table, and returning to the step S202.
10. The unmanned aerial vehicle obstacle avoidance path planning method based on artificial potential field of claim 1, wherein in step S3, the set evaluation conditions include shortest total path length and minimum steering;
The total path length is calculated as follows:
statistics of the number of turns are calculated as follows:
where n represents the number of nodes on the total path, Representing the previous node coordinates of the current path,/>Representing the coordinates of the following nodes of the current path,/>Representing the previous node coordinates of the last path.
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