CN115562074A - Simulation system and method for rapidly generating optimal planned path of unmanned aerial vehicle - Google Patents
Simulation system and method for rapidly generating optimal planned path of unmanned aerial vehicle Download PDFInfo
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Abstract
The invention relates to a simulation system for rapidly generating an optimal planned path of an unmanned aerial vehicle, which comprises a data input module, a simulation map module, an unmanned aerial vehicle path planning module, a gradient descent parameter optimization module and a six-axis semi-physical simulation module; the unmanned aerial vehicle path planning module comprises a path planning module, a self-adaptive factor unit, a six-axis semi-physical simulation mathematical model and a particle swarm optimization algorithm; the unmanned aerial vehicle path planning module adopts a particle swarm optimization algorithm to plan a path based on data input by the data input module and a three-dimensional map imported by the simulation map module, performs simulated flight according to the path obtained by the path planning module through a six-axis semi-physical simulation mathematical model, and optimizes the particle swarm optimization algorithm after importing path information into a self-adaptive factor to obtain an initial optimal path; the six-axis semi-physical simulation module simulates based on the initial optimal path to obtain motion information, optimizes the particle swarm optimization algorithm through the gradient descent parameter optimization module, and obtains the final optimal path based on the final particle swarm optimization algorithm.
Description
Technical Field
The invention relates to the field of unmanned aerial vehicle path planning, in particular to a simulation system and method for quickly generating an optimal planned path of an unmanned aerial vehicle.
Background
With the development of unmanned flight technology, unmanned aircraft have been increasingly used in complex and hazardous military environments. The unmanned aerial vehicle path planning method is used as an important component of an unmanned aerial vehicle flight control and autonomous navigation system, and the response speed and the planning precision of the unmanned aerial vehicle path planning method have great influence on the navigation result. Conventional path planning methods include simulated annealing methods, a-x methods, and the like, which are not suitable for complex military environments with various constraints.
Conventional path planning methods lack constraints on fuel consumption and other conditions. The method omits the constraint condition in the actual system, so that the confidence of the simulation result is greatly reduced. Meanwhile, the unmanned aerial vehicle path planning algorithm has the defects of local optimization and global optimization choice.
Disclosure of Invention
In view of this, the present invention provides a simulation system and a method for quickly generating an optimal planned path of an unmanned aerial vehicle, which can quickly generate the optimal planned path of the unmanned aerial vehicle under the constraint of fuel consumption.
In order to achieve the purpose, the invention adopts the following technical scheme:
a simulation system for rapidly generating an optimal planned path of an unmanned aerial vehicle comprises a data input module, a simulation map module, an unmanned aerial vehicle path planning module, a gradient descent parameter optimization module and a six-axis semi-physical simulation module; the unmanned aerial vehicle path planning module comprises a six-axis semi-physical simulation mathematical model and a particle swarm optimization algorithm; the unmanned aerial vehicle path planning module adopts a particle swarm optimization algorithm to plan a path based on data input by the data input module and a three-dimensional map imported by the simulation map module, performs simulated flight according to the path obtained by the path planning module through a six-axis semi-physical simulation mathematical model, optimizes the particle swarm optimization algorithm after importing path information into a self-adaptive factor, and further obtains an initial optimal path; the six-axis semi-physical simulation module simulates based on the initial optimal path to obtain motion information, further optimizes the particle swarm optimization algorithm through the gradient descent parameter optimization module, and obtains the final optimal path based on the final particle swarm optimization algorithm.
Further, an optimal path output module is further arranged and connected with the unmanned aerial vehicle path planning simulation system for outputting a final optimal path;
further, the data input module comprises a waypoint given input unit and a fuel constraint input unit;
the waypoint given input unit is connected with the unmanned aerial vehicle path planning module and is used for setting a starting point, an end point and other expected waypoints of a path;
the fuel constraint input unit is connected with the unmanned aerial vehicle path planning module, and fuel consumption in unmanned aerial vehicle path planning is regulated through fuel constraint input.
Further, the simulation map module comprises a map unit and a threat/obstacle unit; the map unit generates a three-dimensional map where the unmanned aerial vehicle simulates flight, and the threat/obstacle input unit generates obstacles and emergency situations.
Further, the path planning module adopts a particle swarm optimization algorithm to optimize the path; the six-axis semi-physical simulation mathematical model simulates flight by using the obtained optimal path, and then the obtained path information is led into an adaptive factor; the self-adaptive factors are used for measuring the weight of global or local search in simulated flight, optimizing the parameters of the weight factors in the algorithm, strengthening the global/local search and realizing local closed loop; and the unmanned aerial vehicle path planning module repeatedly operates to obtain an initial optimal path.
Further, the six-axis semi-physical simulation module comprises a conversion module, a motion controller and a six-axis simulation platform; the conversion module is connected with the motion controller; the conversion module is connected with the path planning module to obtain an initial optimal path for setting specific motion and track information of the unmanned aerial vehicle, wherein the specific motion and track information comprises a position, a speed and an attitude angle corresponding to each moment, then the motion and track information is converted into a control signal, and the six-axis simulation platform is controlled by the motion controller; and after the position, speed and attitude information acquired by the six-axis simulation platform is received by the motion controller, the adaptive factors are optimized by adopting a gradient descent parameter optimization module.
Further, the gradient descent parameter optimization module comprises a loss function and a gradient descent method; the loss function is a function for optimizing adaptive factor parameters; the loss function is respectively connected with the motion controller and the gradient descent method and used for receiving path information; and importing path information into the loss function, and optimizing parameters of the adaptive factors by a gradient descent method.
A path generation method of a simulation system for rapidly generating an optimal planned path of an unmanned aerial vehicle comprises the following steps:
step S1: setting the motion trail, scene, barrier and burst condition of the unmanned aerial vehicle through a simulation map module, adding fuel consumption constraint to a particle swarm optimization algorithm, and setting a required waypoint;
step S2: after determining a scene, fuel constraint and a waypoint, planning a path through a particle swarm optimization algorithm, simulating flight by using the obtained path through a six-axis semi-physical simulation mathematical model, introducing path information into a self-adaptive factor, and then optimizing an initial algorithm; repeating the steps and outputting an initial optimal path;
and step S3: the conversion module obtains an optimal path, sends a motion control command and transmits a control signal through the motion controller to control the six-axis simulation platform to realize corresponding linear and rotary motion;
and step S4: the six-axis simulation platform collects motion information and feeds the motion information back to the motion controller, and the motion information is transmitted to a loss function in the gradient descent parameter optimization module through the motion controller;
step S5: the loss function receives path information such as the position, the speed and the attitude of the semi-physical simulation motion of the unmanned aerial vehicle, and the adaptive factor parameters are optimized through the gradient descent method; updating the optimized parameters by the self-adaptive factors to realize a global closed loop;
step S6: and operating the optimized unmanned aerial vehicle path planning module to obtain a final optimized path, and outputting the final optimized path.
Compared with the prior art, the invention has the following beneficial effects:
1. the method considers fuel constraint and self-adaptive factor optimization, is closer to reality, and aims at quickly generating the optimal path plan. The system can continuously update and iterate in the path planning simulation fast flight, double optimization is carried out on the algorithm and the adaptive factor, and closed-loop optimization is achieved.
2. The method has the advantages of strong practicability, high calculation efficiency, support of extension and updating and wide application, and can be used as a high-practicability semi-physical simulation system for rapidly generating the optimal path planning for the unmanned aerial vehicle.
Drawings
Fig. 1 is a system block diagram of an embodiment of the present invention, in which 1 is a waypoint given input unit, 2 is a fuel constraint input unit, 3 is a simulation map module, 4 is a particle swarm optimization algorithm, 5 is a path planning module, 6 is a six-axis semi-physical simulation mathematical model, 7 is an adaptive factor unit, 8 is a transformation module, 9 is a motion controller, 10 is a six-axis simulation platform, and 11 is a gradient descent parameter optimization module;
fig. 2 is a system operation flow chart according to an embodiment of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
Referring to fig. 1, the present invention provides a semi-physical simulation system for rapidly generating an optimal planned path of an unmanned aerial vehicle under fuel constraint conditions, comprising: the method comprises the following steps: the system comprises a waypoint given input unit 1, a fuel constraint input unit 2, a map unit 3, an unmanned aerial vehicle path planning module, a six-axis semi-physical simulation module and a gradient descent parameter optimization module 11.
In this embodiment, the waypoint given input unit 1 is connected to the unmanned aerial vehicle route planning module, and is used for setting a starting point, an end point and other expected waypoints of a route. The fuel constraint input unit 2 is connected with the unmanned aerial vehicle path planning module, and the fuel constraint input unit 2 prescribes fuel consumption in unmanned aerial vehicle path planning; the fuel constraint conditions generated by the fuel constraint input unit 2 are input into the unmanned aerial vehicle path planning module and are used for algorithm optimization under the fuel constraint conditions; the simulation map module 3 is connected with the unmanned aerial vehicle path planning module, and the simulation map module 3 comprises a map unit and a threat/obstacle unit; the map unit generates a three-dimensional map where the unmanned aerial vehicle simulates flight, and the threat/obstacle unit generates obstacles and possible emergencies such as threats; the unmanned aerial vehicle path planning module is respectively connected with the optimal path output and the six-axis semi-physical simulation module, and the unmanned aerial vehicle path planning module optimizes the algorithm to quickly generate an optimal path; the optimal path output is connected with the unmanned aerial vehicle path planning simulation system and used for outputting a final optimal path; the six-axis semi-physical simulation module is respectively connected with the unmanned aerial vehicle path planning module and the gradient descent parameter optimization module 11 and is used for inputting the obtained optimal path into the six-axis semi-physical simulation module to carry out semi-physical simulation flight so as to obtain path information in simulated flight under relatively real conditions; the gradient descent parameter optimization module 11 is connected with the unmanned aerial vehicle path planning module, and is used for receiving path information obtained by semi-physical simulation and optimizing the unmanned aerial vehicle path planning module through the path information of the relative real flight.
In this embodiment, the unmanned aerial vehicle path planning module includes four parts, namely, a particle swarm optimization algorithm 4, a path planning module 5, a six-axis semi-physical simulation mathematical model 6 and an adaptive factor 7; selecting the particle swarm optimization algorithm 4 by the unmanned aerial vehicle path planning module path planning algorithm; leading the particle swarm optimization algorithm 4 into the path planning module 5 to obtain an optimal path; the six-axis semi-physical simulation module mathematical model 6 uses the obtained optimal path to carry out simulated flight, and then guides the obtained path information into the adaptive factor 7; the self-adaptive factor 7 is used for measuring the weight of global or local search in simulated flight, optimizing the parameters of the weight factor in the algorithm, strengthening global/local search and realizing local closed loop; and the unmanned aerial vehicle path planning module repeatedly operates to obtain an initial optimal path.
In the present embodiment, the six-axis semi-physical simulation module includes a transformation module 8, a motion controller 9, and a six-axis simulation platform 10.
Preferably, the motion controller 9 is a uk TRIO sixteen-axis motion controller; the six-axis simulation platform 10 is built by aviation aluminum and comprises a three-axis truss platform and a three-axis rotating platform; the motor brand of the three-axis truss platform adopts Japan Panasonic, the rack brand adopts German Alpha, and the line rail brand adopts Taiwan HIWIN; the motor brand of the three-axis rotating platform adopts the Japanese Hammernace FHA series, and the bearing brand adopts the Swedish SKF series; the six-axis simulation platform is connected with the motion controller 9 by adopting the conversion module 8; the conversion module 8 is connected with the path planning module 5 to obtain an initial optimal path for setting specific motion and trajectory information of the unmanned aerial vehicle, including a position, a speed and an attitude angle corresponding to each moment, and then converts the motion and trajectory information into a control signal, and controls the six-axis simulation platform 10 through the motion controller 9; the position, speed and attitude information acquired by the six-axis simulation platform 10 is received by the motion controller 9 and then transmitted to the gradient descent parameter optimization module 11 to optimize the adaptive factor 7.
In the present embodiment, the gradient descent parameter optimization module 11 includes a loss function and a gradient descent method; the loss function is a function for optimizing the adaptive factor 7 parameter; the loss function is connected to the motion controller 9 and the gradient descent method, respectively, and is configured to receive acquired path information such as position, speed, and attitude. And introducing path information into the loss function, and optimizing the parameters of the adaptive factor 7 by the gradient descent method.
As shown in fig. 2, preferably, the embodiment further provides a working method of the semi-physical simulation system for quickly generating the optimal planned path of the unmanned aerial vehicle under the fuel constraint condition, including the following steps:
step S1: setting a motion track, a scene, an obstacle, a possible threat and the like of the unmanned aerial vehicle through a simulation map module, adding fuel consumption constraint to the particle swarm optimization algorithm 4, and setting a required waypoint;
step S2: after determining a scene, fuel constraint and a waypoint, introducing a particle swarm optimization algorithm 4 into a path planning module 5 for path planning, simulating flight by using an obtained path by using a six-axis semi-physical simulation mathematical model 6, introducing path information into the adaptive factor 7, and then optimizing an initial algorithm. Repeating the steps and outputting an initial optimal path;
and step S3: the conversion module 8 obtains an optimal path, sends a motion control command, transmits a control signal through the motion controller 9, and controls the six-axis simulation platform 10 to realize corresponding linear and rotary motion;
and step S4: the six-axis simulation platform 10 collects motion information and feeds the motion information back to the motion controller 9, and the motion information is transmitted to the loss function in the gradient descent parameter optimization module 11 through the motion controller 9;
step S5: the loss function receives path information such as the position, the speed and the attitude of the semi-physical simulation motion of the unmanned aerial vehicle, and the parameters of the adaptive factor 7 are optimized by the gradient descent method; the adaptive factor 7 updates the optimized parameters to realize global closed loop;
step S6: and operating the optimized unmanned aerial vehicle path planning module to obtain a final optimized path, and outputting the final optimized path.
Preferably, in this embodiment, the local closed loop means that, on one hand, the particle swarm optimization algorithm 4 in the unmanned aerial vehicle path planning module can realize the simulated flight in the six-axis semi-physical simulation mathematical model 6, and on the other hand, the algorithm can feed back the parameters to the particle swarm optimization algorithm 4 after the six-axis semi-physical simulation mathematical model 6 finishes operating and the parameters of the algorithm are optimized by the adaptive factor 7.
Preferably, in this embodiment, the global closed loop means that the unmanned aerial vehicle path planning module may import the obtained initial optimal planned path into the conversion module 8, and convert the path information into a control signal through the conversion module 8 and transmit the control signal into the motion controller 9. The motion controller 9 receives the control signal to enable the six-axis simulation platform 10 to move according to the motion instruction of the optimal planned path, and on the other hand, the position, the speed, the posture and the path information of the six-axis simulation platform 10 can be fed back to the motion controller 9. And the motion controller 9 guides the information obtained by the six-axis simulation platform 10 into a gradient descent optimization module to optimize the parameters of the adaptive factor 7. And updating the optimized parameters of the adaptive factor 7 into an unmanned aerial vehicle path planning module to form a closed loop.
The preferred, six shaft semi-physical simulation modules of this embodiment have used the motion simulation mechanism of a 6 degrees of freedom, can realize the change of high accuracy simulation unmanned aerial vehicle in the position change of all directions and the change of attitude angle, and the simulation effect more is close to actual conditions. The method increases fuel constraint and self-adaptive factor optimization, is closer to reality and aims at a fast flight optimization algorithm. The system can continuously update and iterate in the path planning simulation fast flight, double optimization is carried out on the algorithm and the adaptive factor, and closed-loop optimization is achieved. The system is strong in practicability, high in calculation efficiency and wide in application, supports extension and updating, and can become a high-practicability semi-physical simulation system for rapidly generating the optimal path plan by the unmanned aerial vehicle.
The map module is used for setting various scenes such as tall buildings, mountains and the like for unmanned aerial vehicle simulated flight.
Preferably, in this embodiment, the method has the following workflow:
the method comprises the following steps: experiment conditions are set from aspects of simulating flight scenes, fuel constraint, waypoints, obstacles, threats, attitude information and the like, an experiment scheme is formulated, and one experiment is selected from various condition combinations to optimize the particle swarm optimization algorithm 4.
Step two: the particle swarm optimization algorithm 4 obtains an optimal path through the path planning module 5, inputs the optimal path into the six-axis semi-physical simulation mathematical model 6 for simulated flight, and optimizes parameters of the path planning algorithm through the adaptive factor 7. The optimization algorithm suitable for rapidly generating the optimal planned path of the unmanned aerial vehicle under the constraint of fuel oil is obtained.
Step three: the unmanned aerial vehicle path planning module outputs an optimal path, and the conversion module 8 obtains the optimal path and converts the motion information into a control signal to be transmitted to the motion controller 9. The motion controller 9 receives the control signal to control the six-axis simulation platform 10 to simulate the flight state of the unmanned aerial vehicle.
Step four: the motion controller 9 guides the semi-physical simulation information obtained by the six-axis simulation platform 10 into the gradient descent parameter optimization module 11 to optimize the parameters of the adaptive factors 7. And updating the optimized parameters of the adaptive factor 7 into an unmanned aerial vehicle path planning module to form a closed loop.
Step five: and the unmanned plane path planning module operates again to finally output the optimal path.
The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.
Claims (7)
1. A simulation system for rapidly generating an optimal planned path of an unmanned aerial vehicle is characterized by comprising a data input module, a simulation map module, an unmanned aerial vehicle path planning module, a gradient descent parameter optimization module and a six-axis semi-physical simulation module; the unmanned aerial vehicle path planning module comprises a path planning module, a self-adaptive factor unit, a six-axis semi-physical simulation mathematical model and a particle swarm optimization algorithm; the unmanned aerial vehicle path planning module adopts a particle swarm optimization algorithm to plan a path based on data input by the data input module and a three-dimensional map imported by the simulation map module, performs simulated flight according to the path obtained by the path planning module through a six-axis semi-physical simulation mathematical model, optimizes the particle swarm optimization algorithm after importing path information into a self-adaptive factor, and further obtains an initial optimal path; the six-axis semi-physical simulation module simulates based on the initial optimal path to obtain motion information, further optimizes the particle swarm optimization algorithm through the gradient descent parameter optimization module, and obtains the final optimal path based on the final particle swarm optimization algorithm.
2. The simulation system for rapidly generating the optimal planned path of the unmanned aerial vehicle as claimed in claim 1, wherein the data input module comprises a waypoint given input unit and a fuel constraint input unit;
the waypoint given input unit is connected with the unmanned aerial vehicle path planning module and is used for setting a starting point, an end point and other expected waypoints of a path;
the fuel constraint input unit is connected with the unmanned aerial vehicle path planning module, and fuel consumption in unmanned aerial vehicle path planning is regulated through fuel constraint input.
3. The simulation system for rapidly generating the optimal planned path of the unmanned aerial vehicle according to claim 1, wherein the simulation map module comprises a map unit and a threat/obstacle unit; the map unit generates a three-dimensional map where the unmanned aerial vehicle simulates flight, and the threat/obstacle input unit generates obstacles and emergency situations.
4. The simulation system for rapidly generating the optimal planned path of the unmanned aerial vehicle according to claim 1, wherein the path planning module performs path optimization by using a particle swarm optimization algorithm; the six-axis semi-physical simulation mathematical model simulates flight by using the obtained optimal path, and then the obtained path information is led into an adaptive factor; the adaptive factors are used for weighing the weight of global or local search in simulated flight, optimizing the parameters of the weight factors in the algorithm, strengthening the global/local search and realizing local closed loop; and the unmanned aerial vehicle path planning module repeatedly operates to obtain an initial optimal path.
5. The simulation system for rapidly generating the optimal planned path of the unmanned aerial vehicle as claimed in claim 4, wherein the six-axis semi-physical simulation module comprises a conversion module, a motion controller and a six-axis simulation platform; the conversion module is connected with the motion controller; the conversion module is connected with the path planning module to obtain an initial optimal path for setting specific motion and track information of the unmanned aerial vehicle, wherein the specific motion and track information comprises a position, a speed and an attitude angle corresponding to each moment, then the motion and track information is converted into a control signal, and the six-axis simulation platform is controlled by the motion controller; and after the position, speed and attitude information acquired by the six-axis simulation platform is received by the motion controller, the adaptive factors are optimized by adopting a gradient descent parameter optimization module.
6. The simulation system for rapidly generating the optimal planned path of the unmanned aerial vehicle according to claim 5, wherein the gradient descent parameter optimization module comprises a loss function and a gradient descent method; the loss function is a function for optimizing adaptive factor parameters; the loss function is respectively connected with the motion controller and the gradient descent method and used for receiving path information; and importing the path information into the loss function, and optimizing the parameters of the adaptive factors by a gradient descent method.
7. A path generation method of a simulation system for rapidly generating an optimal planned path of an unmanned aerial vehicle is characterized by comprising the following steps:
step S1: setting the motion trail, scene, barrier and burst condition of the unmanned aerial vehicle through a simulation map module, adding fuel consumption constraint to a particle swarm optimization algorithm, and setting a required waypoint;
step S2: after determining a scene, fuel constraint and a waypoint, planning a path through a particle swarm optimization algorithm, simulating flight by using the obtained path through a six-axis semi-physical simulation mathematical model, introducing path information into a self-adaptive factor, and then optimizing an initial algorithm; repeating the steps and outputting an initial optimal path;
and step S3: the conversion module obtains an initial optimal path, sends a motion control command, transmits a control signal through the motion controller and controls the six-axis simulation platform to realize corresponding linear and rotary motion;
and step S4: the six-axis simulation platform collects motion information and feeds the motion information back to the motion controller, and the motion information is transmitted to a loss function in the gradient descent parameter optimization module through the motion controller;
step S5: the loss function receives path information such as the position, the speed and the attitude of the semi-physical simulation motion of the unmanned aerial vehicle, and self-adaptive factor parameters are optimized through the gradient descent method; updating the optimized parameters by the self-adaptive factors to realize a global closed loop;
step S6: and operating the optimized unmanned aerial vehicle path planning module to obtain a final optimized path, and outputting the final optimized path.
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