Operation type flying robot path planning method

Abstract

The invention discloses an operation type flying robot path planning method, and the method comprises the following steps: 1, determining a task and an environment, and determining operation tasks of a flying robot, such as aerial shooting, environment monitoring, cargo transportation, and operation environment analysis; 2, equipment and software are prepared, a high-performance computer is prepared and used for operating a path planning algorithm, installing and using suitable robot simulation software and map construction and visualization tools; according to complexity and task requirements of an operation environment, proper path planning algorithms are selected, common algorithms include a Dijkstra algorithm, an A * algorithm, an RRT algorithm, an MPC algorithm and a genetic algorithm, flight is simulated through combination of multiple algorithms, the path planning method of the operation type flying robot can be systematically implemented, and the path planning efficiency of the operation type flying robot is improved. And it is ensured that the flying robot can safely and efficiently complete tasks in a complex environment.

Description

Technical Field

[0001] The present invention relates to the technical field of flying robot path planning methods, and more specifically to a working flying robot path planning method. Background Art

[0002] The core feature of an operational flying robot, also known as an aerial work platform or unmanned aerial vehicle operating system, is that it combines the maneuverability of an aircraft with the operational capabilities of a robotic arm or mounted equipment. They usually adopt multi-rotor, fixed-wing combined with multi-rotor hybrid power, or even vertical take-off and landing fixed-wing designs to meet the needs of different operating environments. The fuselage is equipped with advanced detection equipment such as high-definition cameras, radars, infrared sensors, and a high-precision GPS positioning system to ensure stable flight and precise operation in complex environments.

[0003] When an operational flying robot performs flight operations in a complex environment, it is easy to have unexpected configurations. Reasonable path planning is an important means to reduce the occurrence of accidents. Therefore, a new technical solution is needed to solve this problem. Summary of the invention

[0004] The purpose of the present invention is to provide a path planning method for an operational flying robot, which solves the problem that the operational flying robot is prone to accidental configuration when performing flight operations in a complex environment. Reasonable path planning is an important means to reduce the occurrence of accidents.

[0005] To achieve the above object, the present invention provides the following technical solution: a path planning method for an operational flying robot, the path planning method for an operational flying robot comprising the following steps:

[0006] Step 1: Determine the mission and environment, clarify the mission of the flying robot, such as aerial photography, environmental monitoring, cargo transportation, and analyze the operating environment;

[0007] Step 2: Equipment and software preparation: prepare a high-performance computer to run the path planning algorithm, install and use appropriate robot simulation software, and map building and visualization tools;

[0008] Step 3: Environmental modeling: Use sensor data to build a digital model of the operating environment and mark key information such as obstacles, no-fly zones, and target points in the model;

[0009] Step 4: Path planning algorithm selection: select a suitable path planning algorithm based on the complexity of the working environment and task requirements;

[0010] Step 5: Algorithm implementation and integration: Write the corresponding algorithm code according to the selected algorithm and integrate the algorithm into the robot simulation software to ensure that the environment information and robot status data can be received in real time;

[0011] Step 6: Run and debug. Run the algorithm in a simulation environment to observe the path planning effect of the flying robot. Adjust the algorithm parameters and strategies according to the simulation results to optimize the efficiency and safety of path planning. Repeat the test until the flying robot can stably and accurately complete the task according to the planned path.

[0012] Step 7: Field testing: Conduct flight tests in actual operating environments to verify the practicality and reliability of the path planning algorithm. Further adjust the algorithm based on the field test results to ensure that the flying robot can operate stably under various conditions.

[0013] Step 8: Data analysis and optimization: collect and analyze the path planning data of the flying robot. Based on the data analysis results, further optimize the algorithm to improve the efficiency and accuracy of path planning.

[0014] As a preferred implementation of the present invention, the path planning algorithm in step 4 adopts one or more combinations of Dijkstra algorithm, A* algorithm, RRT algorithm, MPC algorithm and genetic algorithm.

[0015] As a preferred implementation of the present invention, the algorithm implementation and integration includes setting algorithm parameters, and setting the initial parameters of the algorithm, such as heuristic function, map size, and obstacle position, according to the specific conditions of the operating environment.

[0016] As a preferred embodiment of the present invention, the operating environment in step 1 includes terrain, buildings, natural obstacles, and possible flight restriction areas.

[0017] As a preferred implementation manner of the present invention, the path planning data of the flying robot in step 8 includes path length, running time, and resource consumption.

[0018] As a preferred embodiment of the present invention, the collection of the flying robot's own data in step 8 includes speed, acceleration, and turning radius.

[0019] As a preferred implementation manner of the present invention, the sensor data includes sensor information including GPS, laser radar, and camera data.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] The present invention selects a suitable path planning algorithm according to the complexity of the operating environment and the task requirements. Common algorithms include Dijkstra algorithm, A* algorithm, RRT algorithm, MPC algorithm and genetic algorithm. Among them, Dijkstra algorithm is suitable for shortest path calculation in a static environment, A* algorithm is suitable for path planning with obstacles and complex environments, RRT algorithm is a random search algorithm, suitable for non-convex dynamic environments, and can quickly generate paths, MPC algorithm is a control algorithm based on model prediction, and is suitable for path optimization in a dynamic environment, and genetic algorithm simulates the genetic mechanism of nature to find the optimal path. By simulating flight through a combination of multiple algorithms, the path planning method of the operating flying robot can be systematically implemented to ensure that the flying robot can complete the task safely and efficiently in a complex environment. DETAILED DESCRIPTION

[0022] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] The present invention provides a technical solution: a path planning method for an operational flying robot, the path planning method for an operational flying robot comprising the following steps:

[0024] Step 1: Determine the mission and environment, clarify the mission of the flying robot, such as aerial photography, environmental monitoring, cargo transportation, and analyze the operating environment;

[0025] Step 2: Equipment and software preparation: prepare a high-performance computer to run the path planning algorithm, install and use appropriate robot simulation software, and map building and visualization tools;

[0026] Step 3: Environmental modeling: Use sensor data to build a digital model of the operating environment and mark key information such as obstacles, no-fly zones, and target points in the model;

[0027] Step 4: Path planning algorithm selection: select a suitable path planning algorithm based on the complexity of the working environment and task requirements;

[0028] Step 5: Algorithm implementation and integration: Write the corresponding algorithm code according to the selected algorithm and integrate the algorithm into the robot simulation software to ensure that the environment information and robot status data can be received in real time;

[0029] Step 6: Run and debug. Run the algorithm in a simulation environment to observe the path planning effect of the flying robot. Adjust the algorithm parameters and strategies according to the simulation results to optimize the efficiency and safety of path planning. Repeat the test until the flying robot can stably and accurately complete the task according to the planned path.

[0030] Step 7: Field testing: Conduct flight tests in actual operating environments to verify the practicality and reliability of the path planning algorithm. Further adjust the algorithm based on the field test results to ensure that the flying robot can operate stably under various conditions.

[0031] Step 8: Data analysis and optimization: collect and analyze the path planning data of the flying robot. Based on the data analysis results, further optimize the algorithm to improve the efficiency and accuracy of path planning.

[0032] As a further improvement, the path planning algorithm in step 4 adopts one or more combinations of Dijkstra algorithm, A* algorithm, RRT algorithm, MPC algorithm and genetic algorithm.

[0033] Further improved, the algorithm implementation and integration includes setting algorithm parameters, and setting initial parameters of the algorithm, such as heuristic function, map size, and obstacle position, according to the specific conditions of the operating environment.

[0034] As a further improvement, the operating environment in step 1 includes terrain, buildings, natural obstacles, and possible flight restricted areas, and detailed environmental data improves the accuracy of path planning.

[0035] Further improved, the path planning data of the flying robot in step 8 includes path length, running time, and resource consumption. The data collection of the path length and the running time and resource consumption of the flying robot can ensure the stability of the flying robot's operation.

[0036] As a further improvement, the collection of the flying robot's own data in step 8 includes speed, acceleration, and turning radius. The collection of the flying robot's own data improves the accuracy of path planning.

[0037] Further improved, the sensor data includes sensor information including GPS, lidar, and camera data.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The present invention selects a suitable path planning algorithm according to the complexity of the operating environment and the task requirements. Common algorithms include Dijkstra algorithm, A* algorithm, RRT algorithm, MPC algorithm and genetic algorithm. Among them, Dijkstra algorithm is suitable for shortest path calculation in a static environment, A* algorithm is suitable for path planning with obstacles and complex environments, RRT algorithm is a random search algorithm, suitable for non-convex dynamic environments, and can quickly generate paths, MPC algorithm is a control algorithm based on model prediction, and is suitable for path optimization in a dynamic environment, and genetic algorithm simulates the genetic mechanism of nature to find the optimal path. By simulating flight through a combination of multiple algorithms, the path planning method of the operating flying robot can be systematically implemented to ensure that the flying robot can complete the task safely and efficiently in a complex environment.

[0040] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any mark in the claims should not be regarded as limiting the claims involved.

[0041] Finally, a few points should be explained: First, in the description of this application, it should be noted that, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, and can be mechanical or electrical connections, or internal connectivity between two components, or direct connections. "Up", "down", "left", "right", etc. are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may change.

[0042] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for path planning of an operational flying robot, characterized in that: The path planning method of the operational flying robot includes the following steps: Step 1: Determine the mission and environment, clarify the mission of the flying robot, such as aerial photography, environmental monitoring, cargo transportation, and analyze the operating environment; Step 2: Equipment and software preparation: prepare a high-performance computer to run the path planning algorithm, install and use appropriate robot simulation software, and map building and visualization tools; Step 3: Environmental modeling: Use sensor data to build a digital model of the operating environment and mark key information such as obstacles, no-fly zones, and target points in the model; Step 4: Path planning algorithm selection: select a suitable path planning algorithm based on the complexity of the working environment and task requirements; Step 5: Algorithm implementation and integration: Write the corresponding algorithm code according to the selected algorithm and integrate the algorithm into the robot simulation software to ensure that the environment information and robot status data can be received in real time; Step 6: Run and debug. Run the algorithm in a simulation environment to observe the path planning effect of the flying robot. Adjust the algorithm parameters and strategies according to the simulation results to optimize the efficiency and safety of path planning. Repeat the test until the flying robot can stably and accurately complete the task according to the planned path. Step 7: Field testing: Conduct flight tests in actual operating environments to verify the practicality and reliability of the path planning algorithm. Further adjust the algorithm based on the field test results to ensure that the flying robot can operate stably under various conditions. Step 8: Data analysis and optimization: collect and analyze the path planning data of the flying robot. Based on the data analysis results, further optimize the algorithm to improve the efficiency and accuracy of path planning.

2. A method for path planning of an operational flying robot according to claim 1, characterized in that: The path planning algorithm in step 4 adopts one or more combinations of Dijkstra algorithm, A* algorithm, RRT algorithm, MPC algorithm and genetic algorithm.

3. A method for path planning of an operational flying robot according to claim 1, characterized in that: The algorithm implementation and integration includes setting algorithm parameters, and setting the initial parameters of the algorithm, such as heuristic function, map size, and obstacle position, according to the specific conditions of the operating environment.

4. A method for path planning of an operational flying robot according to claim 1, characterized in that: The operating environment in step 1 includes terrain, buildings, natural obstacles, and possible flight restricted areas.

5. The path planning method for an operational flying robot according to claim 1, characterized in that: The path planning data of the flying robot in step 8 includes path length, running time, and resource consumption.

6. A method for path planning of an operational flying robot according to claim 1, characterized in that: The collection of the flying robot's own data in step 8 includes speed, acceleration, and turning radius.

7. A method for path planning of an operational flying robot according to claim 1, characterized in that: The sensor data includes sensor information including GPS, lidar, and camera data.