CN114625162A - Hybrid algorithm-based optimal path planning method, system and medium for unmanned aerial vehicle - Google Patents
Hybrid algorithm-based optimal path planning method, system and medium for unmanned aerial vehicle Download PDFInfo
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Abstract
The invention discloses an unmanned aerial vehicle optimal path planning method, a system and a medium based on a hybrid algorithm, wherein the method comprises the following steps: loading a global grid map in the flight process of the unmanned aerial vehicle, planning a global static path based on an A-x algorithm according to historical information, acquiring a global sub-target point sequence to determine a flight optimal path, flying through the flight optimal path and sensing the surrounding environment; when the unknown obstacle information is sensed, judging whether the global sub-target point is blocked by the unknown obstacle or not; if the unmanned aerial vehicle is blocked, updating the global planning, otherwise, performing local path planning, detecting collision according to a geometric algorithm, acquiring a local sub-target point sequence, and determining the short-term flight motion of the unmanned aerial vehicle; and judging whether the global sub-target point is a local sub-target point, if so, finishing path planning, and otherwise, updating the local sub-target point for repeated operation. The invention improves the real-time performance of the unmanned aerial vehicle path planning and enhances the survival capability of the unmanned aerial vehicle and the adaptability of the environment.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle optimal path planning method, an unmanned aerial vehicle optimal path planning system and a medium based on a hybrid algorithm.
Background
With the development of modern technology, the types of unmanned aerial vehicles are continuously changed, and the application of the unmanned aerial vehicles is specialized and perfected day by day, for example, the unmanned aerial vehicles are specially used as plant protection unmanned aerial vehicles, unmanned aerial vehicles for street view shooting and monitoring patrolling, underwater unmanned aerial vehicles for underwater rescue and the like. When the unmanned aerial vehicle executes various tasks, the unmanned aerial vehicle can fly according to a path which is calculated in advance and set by a ground task planning center, abnormal factors appearing on a given route can influence the flight performance of the unmanned aerial vehicle, and therefore the path planning of the unmanned aerial vehicle is an extremely important problem in the navigation task of the unmanned aerial vehicle.
Moreover, with the gradual development of the unmanned aerial vehicle technology, the application of the cluster unmanned aerial vehicle is more and more extensive. Higher requirements are provided for path planning of the cluster unmanned aerial vehicle, the measurement precision of the unmanned aerial vehicle, reasonable planning of the path, stability and safety of the unmanned aerial vehicle during working and other changes of the unmanned aerial vehicle have higher and higher requirements for an integrated control system of the unmanned aerial vehicle. The optimal path planning of the cluster unmanned aerial vehicles is a solution for designing an optimal path route in order to ensure that an unmanned aerial vehicle group completes a specific flight task and avoid various obstacles in the task completing process.
In the existing unmanned aerial vehicle path planning scheme, the conventional planning algorithm and the intelligent planning algorithm can be divided according to planning decisions, and some unmanned aerial vehicle path planning schemes adopt a genetic algorithm and are used for a search algorithm for complex system optimization, so that the premature phenomenon is difficult to avoid, and the global optimal solution cannot be obtained; some adopt ant colony algorithm, but need the greater storage space, also adopt particle swarm algorithm, RRT algorithm, Dijkstra algorithm, etc.; various algorithms have characteristics of themselves, and although the problem of path planning of the unmanned aerial vehicle can be solved, algorithm defects exist more or less, such as the fact that the unmanned aerial vehicle is easy to fall into a local optimal solution, the searching capability is poor, the searching time is long, the searching efficiency is low, and the high requirements of the cluster unmanned aerial vehicle on real-time path planning cannot be well met.
Disclosure of Invention
In order to solve the technical problems, the invention provides an unmanned aerial vehicle optimal path planning method and system based on a hybrid algorithm and a storage medium.
The invention provides an unmanned aerial vehicle optimal path planning method based on a hybrid algorithm, which comprises the following steps:
in the flight process of the unmanned aerial vehicle, acquiring a global grid map, performing global static path planning based on an A-x algorithm according to historical information, and acquiring a global sub-target point sequence;
determining the optimal flight path of the unmanned aerial vehicle according to the global sub-target point sequence, flying according to the optimal flight path and sensing the surrounding environment;
when the unknown obstacle information is sensed, judging whether the global sub-target point is blocked by the unknown obstacle or not;
if the unmanned aerial vehicle is blocked, updating the global planning, if the unmanned aerial vehicle is not blocked, performing local path planning, detecting collision according to a geometric algorithm, acquiring a local sub-target point sequence, and determining the short-term flight motion of the unmanned aerial vehicle;
and judging whether the global sub-target point is a local sub-target point, if so, finishing path planning, and if not, updating the local sub-target point for repeated operation.
In this scheme, the global static path planning is performed based on an algorithm a to obtain a global sub-target point sequence, which specifically includes:
acquiring the current position of the unmanned aerial vehicle as a starting node to start path planning, and storing nodes generated in the planning into an OpenList table and a CloseList table;
evaluating each node through an evaluation function, and selecting a node n with the minimum evaluation cost value in each cycle of an A-star algorithm from OpenLists;
if the node n is a target node, acquiring an optimal path, if the node n is not the target node, taking the node n out of an OpenList, storing the node n into a CloseList, and simultaneously checking adjacent nodes of the node n;
judging whether the adjacent node is in an OpenList or not, if not, storing the adjacent node in the OpenList, and taking a node n stored in the CloseList as a father node of a newly stored node;
and acquiring a target node in the OpenList through node extension, moving back to the initial node through pointing to the father node through a pointer, acquiring an optimal path, and generating a global sub-target point sequence according to the optimal path.
In this scheme, each node is evaluated through an evaluation function, and the evaluation function is specifically:
f(n)＝g(n)+h(n)
wherein f (n) represents an evaluation cost from an initial node to a target node, g (n) represents an evaluation cost from a specific initial node to a specific target node, and h (n) represents a predicted evaluation cost from a current node to a target node.
In this scheme, the prediction and evaluation cost h (n) from the current node to the target node is specifically:
h(n)＝D[a·b(xn-xgoal)+a·b(yn-ygoal)]
wherein D represents the Manhattan distance, a · b represents the grid square area, xnAnd ynRespectively representing the horizontal and vertical position, x, of the current grid blockgoalAnd ygoalRespectively representing the horizontal and vertical positions of the target grid square.
In this scheme, the detecting collision according to a geometric algorithm specifically includes:
detecting collision through a geometric algorithm, judging whether barrier occupation information exists in grids occupied by all sampling points on a sampling path, and calculating whether barrier occupation information exists in the grids occupied by corresponding points of the real-time unmanned aerial vehicle on each sampling point;
if the obstacle occupying information exists, the collision is judged and the path information is abandoned, and if the obstacle occupying information does not exist, the evaluation function is continuously executed.
In the scheme, priority setting is carried out on global path planning and local path planning, path control is respectively carried out on the unmanned aerial vehicle, when the priority of the obstacle avoidance behavior in the local path planning is higher than that of the obstacle avoidance behavior advancing along the original path, the local path planning is preferentially executed, and the unmanned aerial vehicle returns to the global planning route of the global path planning after the execution is finished.
The second aspect of the present invention further provides an optimal path planning system for an unmanned aerial vehicle based on a hybrid algorithm, where the system includes: the memory comprises a hybrid algorithm-based optimal path planning method program for the unmanned aerial vehicle, and the processor executes the optimal path planning method program for the unmanned aerial vehicle based on the hybrid algorithm to realize the following steps:
in the flight process of the unmanned aerial vehicle, acquiring a global grid map, performing global static path planning based on an A-x algorithm according to historical information, and acquiring a global sub-target point sequence;
determining the optimal flight path of the unmanned aerial vehicle according to the global sub-target point sequence, flying according to the optimal flight path and sensing the surrounding environment;
when the unknown obstacle information is sensed, judging whether the global sub-target point is blocked by the unknown obstacle or not;
if the unmanned aerial vehicle is blocked, updating the global planning, if the unmanned aerial vehicle is not blocked, performing local path planning, detecting collision according to a geometric algorithm, acquiring a local sub-target point sequence, and determining the short-term flight motion of the unmanned aerial vehicle;
and judging whether the global sub-target point is a local sub-target point, if so, finishing path planning, and if not, updating the local sub-target point for repeated operation.
In this scheme, the global static path planning based on the a-x algorithm to obtain the global sub-target point sequence specifically includes:
acquiring the current position of the unmanned aerial vehicle as a starting node to start path planning, and storing nodes generated in the planning into an OpenList table and a CloseList table;
evaluating each node through an evaluation function, and selecting a node n with the minimum evaluation cost value in each cycle of an A-star algorithm from OpenLists;
if the node n is a target node, acquiring an optimal path, and if the node n is not the target node, taking the node n out of an OpenList, storing the node n into a CloseList, and simultaneously checking adjacent nodes of the node n;
judging whether the adjacent node is in an OpenList or not, if not, storing the adjacent node in the OpenList, and taking a node n stored in the CloseList as a father node of a newly stored node;
and acquiring a target node in the OpenList through node expansion, pointing to a father node through a pointer, moving back to the initial node, acquiring an optimal path, and generating a global sub-target point sequence according to the optimal path.
In this scheme, each node is evaluated through an evaluation function, and the evaluation function is specifically:
f(n)＝g(n)+h(n)
wherein, (n) represents the evaluation cost from the starting node to the target node, (g), (n) represents the evaluation cost from the specific starting node to the specific target node, and (h), (n) represents the predicted evaluation cost from the current node to the target node.
In this scheme, the prediction and evaluation cost h (n) from the current node to the target node is specifically:
h(n)＝D[a·b(xn-xgoal)+a·b(yn-ygoal)]
wherein D represents the Manhattan distance, a · b represents the grid square area, xnAnd ynRespectively representing the horizontal and vertical position, x, of the current grid blockgoalAnd ygoalRespectively representing the horizontal and vertical positions of the target grid square.
In this scheme, the detecting collision according to a geometric algorithm specifically includes:
detecting collision through a geometric algorithm, judging whether barrier occupation information exists in a grid occupied by all sampling points on a sampling path, and then calculating whether barrier occupation information exists in the grid occupied by a corresponding point of the real-time unmanned aerial vehicle on each sampling point;
if the obstacle occupying information exists, the collision is judged and the path information is abandoned, and if the obstacle occupying information does not exist, the evaluation function is continuously executed.
In the scheme, priority setting is carried out on global path planning and local path planning, path control is respectively carried out on the unmanned aerial vehicle, when the priority of the obstacle avoidance behavior in the local path planning is higher than that of the obstacle avoidance behavior advancing along the original path, the local path planning is preferentially executed, and the unmanned aerial vehicle returns to the global planning route of the global path planning after the execution is finished.
The third aspect of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a hybrid algorithm-based optimal path planning method program for an unmanned aerial vehicle, and when the hybrid algorithm-based optimal path planning method program is executed by a processor, the method implements any of the above steps of the hybrid algorithm-based optimal path planning method for an unmanned aerial vehicle.
The invention discloses an unmanned aerial vehicle optimal path planning method, a system and a medium based on a hybrid algorithm, wherein the method comprises the following steps: in the flight process of the unmanned aerial vehicle, loading a global grid map, planning a global static path based on an A-x algorithm according to historical information, acquiring a global sub-target point sequence, determining a flight optimal path, flying through the flight optimal path and sensing the surrounding environment; when the unknown obstacle information is sensed, judging whether the global sub-target point is blocked by the unknown obstacle or not; if the unmanned aerial vehicle is blocked, updating the global planning, otherwise, performing local path planning, detecting collision according to a geometric algorithm, acquiring a local sub-target point sequence, and determining the short-term flight motion of the unmanned aerial vehicle; and judging whether the global sub-target point is a local sub-target point, if so, finishing path planning, and otherwise, updating the local sub-target point for repeated operation. By applying the hybrid algorithm, the invention realizes the global planning and the local planning, and also realizes the dynamic planning and the static planning, and combines the global planning and the local planning, and the composite structure formed by the global planning and the local planning improves the real-time performance of the unmanned aerial vehicle and the navigation stability and safety of the unmanned aerial vehicle; meanwhile, the hybrid algorithm is verified by simulation, and the reliability is high.
Drawings
Fig. 1 shows a flow chart of an optimal path planning method for an unmanned aerial vehicle based on a hybrid algorithm according to the invention;
fig. 2 shows a path planning flow diagram of the drone according to the invention;
fig. 3 shows a block diagram of an optimal path planning system for an unmanned aerial vehicle based on a hybrid algorithm.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and thus the scope of the present invention is not limited by the specific embodiments disclosed below.
Fig. 1 shows a flow chart of an optimal path planning method for an unmanned aerial vehicle based on a hybrid algorithm.
As shown in fig. 1, a first aspect of the present invention provides a hybrid algorithm-based optimal path planning method for an unmanned aerial vehicle, including:
s102, acquiring a global grid map in the flight process of the unmanned aerial vehicle, planning a global static path based on an A-x algorithm according to historical information, and acquiring a global sub-target point sequence;
s104, determining the optimal flight path of the unmanned aerial vehicle according to the global sub-target point sequence, flying according to the optimal flight path and sensing the surrounding environment;
s106, judging whether the global sub-target point is blocked by the unknown obstacle or not when the unknown obstacle information is sensed;
s108, if the unmanned aerial vehicle is blocked, updating the global planning, if the unmanned aerial vehicle is not blocked, performing local path planning, detecting collision according to a geometric algorithm, and acquiring a local sub-target point sequence to determine the short-term flight motion of the unmanned aerial vehicle;
s110, judging whether the global sub-target point is a local sub-target point, if so, finishing path planning, and if not, updating the local sub-target point for repeated operation.
It should be noted that path planning can be divided into global and local path planning according to the range, and can be divided into static and dynamic path planning according to the environmental change, and the combination of global, local and dynamic, static path planning can make the path planning precision of the unmanned aerial vehicle higher, greatly improve the real-time performance of the path planning of the unmanned aerial vehicle.
It should be noted that, the global static path planning based on the a-x algorithm to obtain the global sub-target point sequence specifically includes:
acquiring the current position of the unmanned aerial vehicle as a starting node to start path planning, and storing nodes generated in the planning into an OpenList table and a CloseList table;
evaluating each node through an evaluation function, and selecting a node n with the minimum evaluation cost value in each cycle of an A-star algorithm from OpenList;
if the node n is a target node, acquiring an optimal path, if the node n is not the target node, taking the node n out of an OpenList, storing the node n into a CloseList, and simultaneously checking adjacent nodes of the node n;
judging whether the adjacent node is in an OpenList or not, if not, storing the adjacent node in the OpenList, and taking a node n stored in the CloseList as a father node of a newly stored node;
and acquiring a target node in the OpenList through node expansion, pointing to a father node through a pointer, moving back to the initial node, acquiring an optimal path, and generating a global sub-target point sequence according to the optimal path.
It should be noted that, the evaluation function is used to evaluate each node, and the evaluation function specifically includes:
f(n)＝g(n)+h(n)
wherein, (n) represents the evaluation cost from the starting node to the target node, (g), (n) represents the evaluation cost from the specific starting node to the specific target node, and (h), (n) represents the predicted evaluation cost from the current node to the target node.
It should be noted that the prediction and evaluation cost h (n) from the current node to the target node adopts a manhattan prediction algorithm, which specifically includes:
h(n)＝D[a·b(xn-xgoal)+a·b(yn-ygoal)]
wherein D represents the Manhattan distance, a · b represents the grid square area, xnAnd ynRespectively representing the horizontal and vertical position, x, of the current grid blockgoalAnd ygoalRespectively representing the horizontal and vertical positions of the target grid square.
It should be noted that the detecting collision according to a geometric algorithm specifically includes:
detecting collision through a geometric algorithm, judging whether barrier occupation information exists in a grid occupied by all sampling points on a sampling path, and then calculating whether barrier occupation information exists in the grid occupied by a corresponding point of the real-time unmanned aerial vehicle on each sampling point;
if the obstacle occupying information exists, the collision is determined and the path information is discarded, and if the obstacle occupying information does not exist, the evaluation function is continuously executed.
It should be noted that priority setting is performed on global path planning and local path planning, path control is performed on the unmanned aerial vehicle respectively, when the priority of the obstacle avoidance behavior in the local path planning is higher than that of the obstacle avoidance behavior in the local path planning, local path planning is preferentially performed, and the unmanned aerial vehicle returns to the global planned route of the global path planning after the execution is completed. In the overall structure of the path planning behaviors, global and local planning is based on behavior structure organization, and the path control can be performed on the entity unmanned aerial vehicle respectively according to the setting of priority, so that the real-time adaptability of the unmanned aerial vehicle is improved.
Fig. 3 shows a block diagram of the system for planning the optimal path of the unmanned aerial vehicle based on the hybrid algorithm.
The second aspect of the present invention also provides an unmanned aerial vehicle optimal path planning system 3 based on a hybrid algorithm, which includes: the unmanned aerial vehicle optimal path planning method based on the hybrid algorithm comprises a memory 31 and a processor 32, wherein the memory comprises a hybrid algorithm-based unmanned aerial vehicle optimal path planning method program, and when the processor executes the hybrid algorithm-based unmanned aerial vehicle optimal path planning method program, the following steps are realized:
in the flight process of the unmanned aerial vehicle, acquiring a global grid map, performing global static path planning based on an A-x algorithm according to historical information, and acquiring a global sub-target point sequence;
determining the optimal flight path of the unmanned aerial vehicle according to the global sub-target point sequence, flying according to the optimal flight path and sensing the surrounding environment;
when the unknown obstacle information is sensed, judging whether the global sub-target point is blocked by the unknown obstacle or not;
if the unmanned aerial vehicle is blocked, updating the global planning, if the unmanned aerial vehicle is not blocked, performing local path planning, detecting collision according to a geometric algorithm, acquiring a local sub-target point sequence, and determining the short-term flight motion of the unmanned aerial vehicle;
and judging whether the global sub-target point is a local sub-target point, if so, finishing path planning, and if not, updating the local sub-target point for repeated operation.
It should be noted that path planning can be divided into global and local path planning according to the range, and can be divided into static and dynamic path planning according to the environmental change, and the combination of global, local and dynamic, static path planning can make the path planning precision of the unmanned aerial vehicle higher, greatly improve the real-time performance of the path planning of the unmanned aerial vehicle.
It should be noted that, the global static path planning based on the a-x algorithm to obtain the global sub-target point sequence specifically includes:
acquiring the current position of the unmanned aerial vehicle as a starting node to start path planning, and storing nodes generated in the planning into an OpenList table and a CloseList table;
evaluating each node through an evaluation function, and selecting a node n with the minimum evaluation cost value in each cycle of an A-star algorithm from OpenList;
if the node n is a target node, acquiring an optimal path, if the node n is not the target node, taking the node n out of an OpenList, storing the node n into a CloseList, and simultaneously checking adjacent nodes of the node n;
judging whether the adjacent node is in an OpenList or not, if not, storing the adjacent node in the OpenList, and taking a node n stored in the CloseList as a father node of a newly stored node;
and acquiring a target node in the OpenList through node extension, moving back to the initial node through pointing to the father node through a pointer, acquiring an optimal path, and generating a global sub-target point sequence according to the optimal path.
It should be noted that, the evaluation function is used to evaluate each node, and the evaluation function specifically includes:
f(n)＝g(n)+h(n)
wherein, (n) represents the evaluation cost from the starting node to the target node, (g), (n) represents the evaluation cost from the specific starting node to the specific target node, and (h), (n) represents the predicted evaluation cost from the current node to the target node.
It should be noted that the prediction and evaluation cost h (n) from the current node to the target node adopts a manhattan prediction algorithm, which specifically includes:
h(n)＝D[a·b(xn-xgoal)+a·b(yn-ygoal)]
wherein D represents the Manhattan distance, a · b represents the grid square area, xnAnd ynRespectively representing the horizontal and vertical position, x, of the current grid blockgoalAnd ygoalRespectively representing the horizontal and vertical positions of the target grid square.
It should be noted that the detecting collision according to a geometric algorithm specifically includes:
detecting collision through a geometric algorithm, judging whether barrier occupation information exists in a grid occupied by all sampling points on a sampling path, and then calculating whether barrier occupation information exists in the grid occupied by a corresponding point of the real-time unmanned aerial vehicle on each sampling point;
if the obstacle occupying information exists, the collision is judged and the path information is abandoned, and if the obstacle occupying information does not exist, the evaluation function is continuously executed.
It should be noted that priority setting is performed on global path planning and local path planning, path control is performed on the unmanned aerial vehicle respectively, when the priority of the obstacle avoidance behavior in the local path planning is higher than that of the obstacle avoidance behavior in the local path planning, local path planning is preferentially performed, and the unmanned aerial vehicle returns to the global planned route of the global path planning after the execution is completed. In the overall structure of the path planning behaviors, global and local planning is based on behavior structure organization, and the path control can be performed on the entity unmanned aerial vehicle respectively according to the setting of priority, so that the real-time adaptability of the unmanned aerial vehicle is improved.
The third aspect of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a hybrid algorithm-based optimal path planning method program for an unmanned aerial vehicle, and when the hybrid algorithm-based optimal path planning method program is executed by a processor, the method implements any of the above steps of the hybrid algorithm-based optimal path planning method for an unmanned aerial vehicle.
The invention discloses an unmanned aerial vehicle optimal path planning method, a system and a medium based on a hybrid algorithm, wherein the method comprises the following steps: loading a global grid map in the flight process of the unmanned aerial vehicle, planning a global static path based on an A-x algorithm according to historical information, acquiring a global sub-target point sequence to determine a flight optimal path, flying through the flight optimal path and sensing the surrounding environment; when unknown obstacle information is sensed, whether the global sub-target point is blocked by an unknown obstacle or not is judged; if the unmanned aerial vehicle is blocked, updating the global planning, otherwise, performing local path planning, detecting collision according to a geometric algorithm, acquiring a local sub-target point sequence, and determining the short-term flight motion of the unmanned aerial vehicle; and judging whether the global sub-target point is a local sub-target point, if so, finishing path planning, and otherwise, updating the local sub-target point for repeated operation. By applying the hybrid algorithm, the invention realizes the global planning and the local planning, and also realizes the dynamic planning and the static planning, and combines the global planning and the local planning, and the composite structure formed by the global planning and the local planning improves the real-time performance of the unmanned aerial vehicle and the navigation stability and safety of the unmanned aerial vehicle; meanwhile, the hybrid algorithm is verified by simulation, and the reliability is high.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication between the components shown or discussed may be through some interfaces, indirect coupling or communication between devices or units, and may be electrical, mechanical or other.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a hardware form, and can also be realized in a form of hardware and a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; the storage medium includes: various media capable of storing program codes, such as a removable Memory device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention or portions thereof contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think of the changes or substitutions within the technical scope of the present invention, and shall cover the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. An unmanned aerial vehicle optimal path planning method based on a hybrid algorithm is characterized by comprising the following steps:
in the flight process of the unmanned aerial vehicle, acquiring a global grid map, performing global static path planning based on an A-x algorithm according to historical information, and acquiring a global sub-target point sequence;
determining a flight optimal path of the unmanned aerial vehicle according to the global sub-target point sequence, flying according to the flight optimal path and sensing the surrounding environment;
when the unknown obstacle information is sensed, judging whether the global sub-target point is blocked by the unknown obstacle or not;
if the unmanned aerial vehicle is blocked, updating the global planning, if the unmanned aerial vehicle is not blocked, performing local path planning, detecting collision according to a geometric algorithm, acquiring a local sub-target point sequence, and determining the short-term flight motion of the unmanned aerial vehicle;
and judging whether the global sub-target point is a local sub-target point, if so, finishing path planning, and if not, updating the local sub-target point for repeated operation.
2. The method for planning the optimal path of the unmanned aerial vehicle based on the hybrid algorithm according to claim 1, wherein the global static path planning is performed based on an a-x algorithm to obtain a global sub-target point sequence, and specifically comprises:
acquiring the current position of the unmanned aerial vehicle as a starting node to start path planning, and storing nodes generated in the planning into an OpenList table and a CloseList table;
evaluating each node through an evaluation function, and selecting a node n with the minimum evaluation cost value in each circulation of an A-star algorithm from OpenList;
if the node n is a target node, acquiring an optimal path, and if the node n is not the target node, taking the node n out of an OpenList, storing the node n into a CloseList, and simultaneously checking adjacent nodes of the node n;
judging whether the adjacent node is in an OpenList or not, if not, storing the adjacent node in the OpenList, and taking a node n stored in the CloseList as a father node of a newly stored node;
and acquiring a target node in the OpenList through node extension, moving back to the initial node through pointing to the father node through a pointer, acquiring an optimal path, and generating a global sub-target point sequence according to the optimal path.
3. The hybrid algorithm-based optimal path planning method for unmanned aerial vehicles according to claim 2, wherein each node is evaluated by an evaluation function, and the evaluation function specifically comprises:
f(n)＝g(n)+h(n)
wherein, (n) represents the evaluation cost from the starting node to the target node, (g), (n) represents the evaluation cost from the specific starting node to the specific target node, and (h), (n) represents the predicted evaluation cost from the current node to the target node.
4. The hybrid algorithm-based optimal path planning method for unmanned aerial vehicles according to claim 3, wherein the prediction and evaluation cost h (n) from the current node to the target node is specifically:
h(n)＝D[a·b(xn-xgoal)+a·b(yn-ygoal)]
wherein D represents the Manhattan distance, a · b represents the grid square area, xnAnd ynRespectively representing the horizontal and vertical positions, x, of the current grid squaregoalAnd ygoalRespectively representing the horizontal and vertical positions of the target grid square.
5. The hybrid algorithm-based optimal path planning method for unmanned aerial vehicles according to claim 1, wherein the collision is detected according to a geometric algorithm, specifically:
detecting collision through a geometric algorithm, judging whether barrier occupation information exists in a grid occupied by all sampling points on a sampling path, and calculating whether barrier occupation information exists in a grid occupied by a corresponding point of the real-time unmanned aerial vehicle on each sampling point;
if the obstacle occupying information exists, the collision is judged and the path information is abandoned, and if the obstacle occupying information does not exist, the evaluation function is continuously executed.
6. The optimal path planning method for the unmanned aerial vehicle based on the hybrid algorithm as claimed in claim 1, wherein priority setting is performed on global path planning and local path planning, path control is performed on the unmanned aerial vehicle, when the priority of the obstacle avoidance behavior in the local path planning is higher than that of the obstacle avoidance behavior in the local path planning, the local path planning is preferentially performed, and the unmanned aerial vehicle returns to the global path planning route of the global path planning after the execution is completed.
7. An unmanned aerial vehicle optimal path planning system based on a hybrid algorithm is characterized by comprising: the memory comprises a hybrid algorithm-based optimal path planning method program for the unmanned aerial vehicle, and the processor executes the program to realize the following steps:
in the flight process of the unmanned aerial vehicle, acquiring a global grid map, planning a global static path based on an A-x algorithm according to historical information, and acquiring a global sub-target point sequence;
determining a flight optimal path of the unmanned aerial vehicle according to the global sub-target point sequence, flying according to the flight optimal path and sensing the surrounding environment;
when the unknown obstacle information is sensed, judging whether the global sub-target point is blocked by the unknown obstacle or not;
if the unmanned aerial vehicle is blocked, updating the global planning, if the unmanned aerial vehicle is not blocked, performing local path planning, detecting collision according to a geometric algorithm, acquiring a local sub-target point sequence, and determining the short-term flight motion of the unmanned aerial vehicle;
and judging whether the global sub-target point is a local sub-target point, if so, finishing path planning, and if not, updating the local sub-target point for repeated operation.
8. The system according to claim 7, wherein the global static path planning is performed based on the a-x algorithm to obtain a global sub-target sequence, specifically:
acquiring the current position of the unmanned aerial vehicle as a starting node to start path planning, and storing nodes generated in the planning into an OpenList table and a CloseList table;
evaluating each node through an evaluation function, and selecting a node n with the minimum evaluation cost value in each circulation of an A-star algorithm from OpenList;
if the node n is a target node, acquiring an optimal path, if the node n is not the target node, taking the node n out of an OpenList, storing the node n into a CloseList, and simultaneously checking adjacent nodes of the node n;
judging whether the adjacent node is in an OpenList or not, if not, storing the adjacent node in the OpenList, and taking a node n stored in the CloseList as a father node of a newly stored node;
and acquiring a target node in the OpenList through node expansion, pointing to a father node through a pointer, moving back to the initial node, acquiring an optimal path, and generating a global sub-target point sequence according to the optimal path.
9. The hybrid algorithm-based optimal path planning system for unmanned aerial vehicles according to claim 7, wherein the collision is detected according to a geometric algorithm, specifically:
detecting collision through a geometric algorithm, judging whether barrier occupation information exists in a grid occupied by all sampling points on a sampling path, and then calculating whether barrier occupation information exists in the grid occupied by corresponding points of the real-time unmanned aerial vehicle on each sampling point;
if the obstacle occupying information exists, the collision is judged and the path information is abandoned, and if the obstacle occupying information does not exist, the evaluation function is continuously executed.
10. A computer-readable storage medium characterized by: the computer readable storage medium includes a hybrid algorithm-based optimal path planning method program for the unmanned aerial vehicle, which when executed by a processor, implements the steps of the hybrid algorithm-based optimal path planning method for the unmanned aerial vehicle according to any one of claims 1 to 6.
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| CN114925937A (en) * | 2022-06-27 | 2022-08-19 | 东北大学 | Stage scene point cloud scanning site selection and path planning method |
| CN116576865A (en) * | 2023-07-07 | 2023-08-11 | 民航成都电子技术有限责任公司 | Flight area path planning method, device, equipment and medium |
| CN117055601A (en) * | 2023-09-05 | 2023-11-14 | 广东工业大学 | Unmanned aerial vehicle meal delivery path planning method, unmanned aerial vehicle meal delivery path planning device, unmanned aerial vehicle meal delivery path planning equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114925937A (en) * | 2022-06-27 | 2022-08-19 | 东北大学 | Stage scene point cloud scanning site selection and path planning method |
| CN116576865A (en) * | 2023-07-07 | 2023-08-11 | 民航成都电子技术有限责任公司 | Flight area path planning method, device, equipment and medium |
| CN116576865B (en) * | 2023-07-07 | 2023-10-17 | 民航成都电子技术有限责任公司 | Flight area path planning method, device, equipment and medium |
| CN117055601A (en) * | 2023-09-05 | 2023-11-14 | 广东工业大学 | Unmanned aerial vehicle meal delivery path planning method, unmanned aerial vehicle meal delivery path planning device, unmanned aerial vehicle meal delivery path planning equipment and storage medium |
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