Power optimization control method of automatic driving tractor based on unmanned aerial vehicle

Abstract

The invention discloses a power optimization control method of an automatic driving tractor based on an unmanned aerial vehicle, and belongs to the technical field of intelligent unmanned driving control. The method aims at solving the problems that an existing intelligent tractor depends on a GPS positioning system, so that the use area is limited, and the energy efficiency is not fully considered. Field data are collected through aerial photography of the unmanned aerial vehicle, a GI map is constructed, obstacles are recognized, and the data are sent to a tractor control system to plan an optimal path and motion control parameters. According to the deviation between the current position and the target position, the target values of the driving system, the auxiliary power system, the braking system and the steering system are optimized, optimal application of energy is achieved, and the system operation efficiency is improved. According to the method, the cost of a GPS positioning module and a related data center can be saved, the difference of communication signals in different areas is ignored, and the energy utilization is optimized while the working efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent unmanned driving control technology, and in particular to a power optimization control method for an autonomous driving tractor based on a drone. Background Art

[0002] Building fully autonomous intelligent control systems for tractors has become a key research direction. Existing communication technologies, intelligent driving technologies, and smart algorithms ensure its feasibility. Building intelligent control systems based on existing drones and tractors allows them to independently complete various operations, improving agricultural production efficiency and making it more convenient.

[0003] The prior art publication number is CN110825091A, which discloses an intelligent tractor based on a drone.

[0004] The route selection method and device can automatically select the optimal route and enable the tractor to travel along the optimal route without the need for manual input of data setting points.

[0005] Although this device has many beneficial effects, the following problems still exist: the tractor's intelligent route selection method and device still rely on the GPS positioning system to locate the intelligent tractor, which limits the area of ​​use; and during use, it does not model the location information and incorporate it into process control, which is not friendly to the use of energy efficiency. Summary of the Invention

[0006] (1) Technical problems solved

[0007] In view of the shortcomings of the existing technology, the present invention provides a power optimization control method for an automatic driving tractor based on a drone, which solves the problems raised by the above background technology.

[0008] (2) Technical solution

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: A power optimization control method for an autonomous driving tractor based on a drone, comprising the following steps:

[0010] S1, drone aerial photography to collect field data;

[0011] S2: The drone builds a GIS map, identifies obstacles, and encapsulates data such as the tractor's location and sends it to the tractor's control system via wireless communication.

[0012] S3, after receiving the data from the UAV, the tractor control system plans the optimal path and the motion control parameters;

[0013] S4, the tractor control system calculates the target torque of the drive system based on the current position data and motion control parameters, and sends it to the drive system in real time;

[0014] S5. The tractor control system calculates the target power of the auxiliary power system and sends it to the auxiliary power system in real time;

[0015] The S6 tractor control system calculates the target torque of the braking system and sends it to the braking system in real time;

[0016] S7. The tractor control system calculates the target angle of the steering system and sends it to the steering system in real time.

[0017] Preferably, the UAV is composed of multiple subsystems including a control system, a drive system, an auxiliary power system, a power battery and a vision system.

[0018] Preferably, the self-driving tractor is composed of multiple subsystems including a control system, a drive system, an auxiliary power system, a braking system, a steering system and a power battery, wherein the control system includes a path planning module and a motion control module.

[0019] Preferably, the motion control parameters include the current position, target position n, final target position, current torque Ta and speed ndrv of the drive motor, power battery state of charge Soc, power battery charging and discharging power, current power of the auxiliary power system, actual braking torque, actual steering angle, and current vehicle speed.

[0020] Preferably, the subsystem target values ​​include a drive system target torque, an auxiliary power system target power, a brake system target torque, and a steering system target angle.

[0021] Preferably, the target value of the subsystem in the automatic driving tractor is optimized based on the deviation between the current position of the tractor and the target position; when the deviation is greater than the set value, the drive system strengthens the target torque based on the deviation value, the drive power demand is strengthened, the auxiliary power system target power is adjusted significantly based on the difference between the drive power demand and the power discharge power of the power battery, the braking system strengthens the management of the target torque only when an obstacle is detected, and weakens it at other times, and the steering system weakens the steering angle adjustment range based on the deviation value; when the deviation is less than the set value, the drive system weakens the target torque based on the deviation value, the drive power demand is weakened, the auxiliary power system target power is adjusted significantly based on the difference between the drive power demand and the power discharge power of the power battery, the braking system strengthens the management of the target torque, and the steering system strengthens the steering angle adjustment range based on the deviation value.

[0022] (3) Beneficial effects

[0023] The present invention provides a power optimization control method for an autonomous driving tractor based on a drone, which has the following beneficial effects:

[0024] 1. This control method controls the tractor's movement through a path planning module. This module uses advanced algorithms, combined with GIS maps and actual field conditions, to plan the optimal operating path for the tractor. This path not only takes into account the shortest distance but also integrates factors such as energy consumption, operating efficiency, and obstacle avoidance, effectively improving the tractor's operating efficiency and energy utilization.

[0025] 2. This control method uses real-time monitoring by drones, eliminating the need for GPS positioning modules and related data center costs. It also ignores differences in communication signals in different regions. By optimizing path planning and building a path-based vehicle motion control model, it optimizes energy utilization during motion and improves work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a system hardware architecture diagram of the present invention;

[0027] Figure 2 is a flow chart of the present invention;

[0028] Figure 3 is a schematic diagram of the path planning of the present invention;

[0029] Figure 4 It is a schematic diagram of motion control parameters of the present invention. DETAILED DESCRIPTION

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0031] Example

[0032] like Figure 1-4 As shown, an embodiment of the present invention provides a power optimization control method for an autonomous driving tractor based on a drone, comprising the following steps:

[0033] S1, drone aerial photography to collect field data;

[0034] S2: The drone builds a GIS map, identifies obstacles, and encapsulates data such as the tractor's location and sends it to the tractor's control system via wireless communication.

[0035] S3, after receiving the data from the UAV, the tractor control system plans the optimal path and the motion control parameters;

[0036] S4, the tractor control system calculates the target torque of the drive system based on the current position data and motion control parameters, and sends it to the drive system in real time;

[0037] S5. The tractor control system calculates the target power of the auxiliary power system and sends it to the auxiliary power system in real time;

[0038] The S6 tractor control system calculates the target torque of the braking system and sends it to the braking system in real time;

[0039] S7. The tractor control system calculates the target angle of the steering system and sends it to the steering system in real time.

[0040] The system hardware architecture of the present invention includes a drone and an autonomous driving tractor.

[0041] The drone subsystem consists of a control system, a drive system, an auxiliary power system, a power battery, and a vision system. The control system processes flight data and commands, regulates flight attitude, and ensures the drone maintains stable flight along the preset route while collecting field data. The drive system, consisting of a motor and propeller, adjusts motor speed to enable the drone to fly flexibly. The auxiliary power system provides additional thrust or lift for complex tasks, such as helping the drone to hover stably during windy field operations, ensuring the continuity and accuracy of data collection. The power battery provides power to all systems of the drone, using high-energy-density, highly stable lithium batteries and equipped with an intelligent battery management system that monitors battery status in real time to ensure safe use. The vision system is a key component for drones collecting field data. It includes multiple high-definition cameras and lidar, which are used to identify information such as crop growth status, field topography, and obstacles, and to construct a high-precision field terrain model, providing basic information for subsequent path planning.

[0042] The autonomous tractor's subsystem includes a control system, drive system, auxiliary power system, braking system, steering system, and power battery. Unlike conventional electric tractors, the control system incorporates a hardware module for communicating with drones and a new path planning model in its software. This model's parameters are integrated into motion control parameters to optimize energy utilization and improve motion control efficiency. The control system integrates a high-performance processor and advanced control algorithms to rapidly process drone data, generate optimal motion control parameters, and coordinate the operation of various subsystems to ensure stable and efficient tractor operation. It also features intelligent learning and adaptive capabilities to optimize control strategies and enhance operational efficiency and quality. The drive system, consisting of an electric motor, a speed reducer, and drive wheels, provides power to the tractor and meets traction requirements. A torque control module adjusts torque output in real time. The auxiliary power system provides power support for auxiliary equipment, offering flexible power adjustment for optimal energy utilization. The braking system utilizes a combination of electric and mechanical braking to ensure safe operation and features an intelligent brake distribution module that automatically adjusts brake force distribution. The steering system utilizes electric power steering technology, enabling steerable operations based on command. High-precision sensors monitor steering status and provide feedback in real time to ensure consistent trajectory with the planned path. The power battery provides electrical energy for various systems of the tractor. It adopts a modular design and is equipped with an efficient thermal management system to ensure stable operation. The control system optimizes the energy management strategy according to the battery charge state and operating requirements to extend the operating time.

[0043] The path planning module, one of the core intelligent components of this system, plans the optimal operating path for the tractor based on field data collected by drones. It analyzes the GIS map transmitted by the drone, identifies obstacles, and defines cells based on the tractor's boundary dimensions, dividing the field into smaller areas. Using advanced algorithms, it combines terrain, crop distribution, and task requirements to calculate the optimal path, taking into account factors such as path distance, energy consumption, operating efficiency, and obstacle avoidance. It also constructs a set of target locations and decomposes the optimal path into subpaths, facilitating real-time adjustments and optimization.

[0044] The motion control parameter module calculates a series of motion control parameters, including position, torque, speed, state of charge, power, etc., based on the optimal path generated by the path planning module and the real-time status information of the tractor, providing precise control basis for each subsystem and realizing collaborative operation.

[0045] The drive system control module uses advanced motor control algorithms to precisely control the drive motor based on the target torque and speed information provided by the motion control parameter module, adjusting the drive current and voltage in real time to ensure that the motor outputs the required torque and speed. It also has overcurrent, overvoltage, and overheating protection functions.

[0046] The auxiliary power system control module accurately controls the auxiliary power system based on the target power information provided, dynamically adjusts the power output to meet different operational requirements, and also has intelligent fault diagnosis capabilities.

[0047] The braking system control module uses electronic braking control technology based on target torque information to accurately control the braking torque, monitor driving speed, distance and braking intention in real time to ensure smooth deceleration or parking, and has a braking effect self-test function. The steering system control module uses high-precision angle sensors and electric power steering technology based on target angle information to accurately control the steering angle, adjust the steering wheel angle in real time to ensure that the driving trajectory is consistent with the planned path, and has a steering torque feedback function to improve operational smoothness and comfort.

[0048] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A power optimization control method for an autonomous driving tractor based on a drone, characterized in that: The following steps are involved: S1, drone aerial photography to collect field data; S2: The drone builds a GIS map, identifies obstacles, and encapsulates data such as the tractor's location and sends it to the tractor's control system via wireless communication. S3, after receiving the data from the UAV, the tractor control system plans the optimal path and the motion control parameters; S4, the tractor control system calculates the target torque of the drive system based on the current position data and motion control parameters, and sends it to the drive system in real time; S5. The tractor control system calculates the target power of the auxiliary power system and sends it to the auxiliary power system in real time; The S6 tractor control system calculates the target torque of the braking system and sends it to the braking system in real time; S7. The tractor control system calculates the target angle of the steering system and sends it to the steering system in real time.

2. The power optimization control method for an autonomous driving tractor based on a drone according to claim 1, characterized in that: The UAV is composed of multiple subsystems including a control system, a drive system, an auxiliary power system, a power battery and a vision system.

3. The power optimization control method for an autonomous driving tractor based on a drone according to claim 1, characterized in that: The self-driving tractor consists of multiple subsystems including a control system, a drive system, an auxiliary power system, a braking system, a steering system and a power battery, wherein the control system includes a path planning module and a motion control module.

4. The power optimization control method for an autonomous driving tractor based on a drone according to claim 1, characterized in that: In S3, the path planning scheme is to define cells based on the boundary size of the tractor in the GIS map constructed by the drone, plan the optimal path, and define the optimal path into sub-path sets by constructing a target position set Sn = {target position s1, target position s2...final position st}, ultimately achieving optimized process control.

5. The power optimization control method for an autonomous driving tractor based on a drone according to claim 3, characterized in that: The tractor control system calculates the target value of each subsystem based on the motion control parameters and controls the operating state of the subsystem, thereby achieving optimal use of energy and improving system operating efficiency.

6. The power optimization control method for an autonomous driving tractor based on a drone according to claim 5, characterized in that: The motion control parameters include the current position, target position n, final target position, current torque Ta and speed ndrv of the drive motor, power battery state of charge Soc, power battery charging and discharging power, current power of the auxiliary power system, actual braking torque, actual steering angle, and current vehicle speed.

7. The power optimization control method for an autonomous driving tractor based on a drone according to claim 5, characterized in that: The subsystem target values ​​include the drive system target torque, the auxiliary power system target power, the brake system target torque, and the steering system target angle.

8. The power optimization control method for an autonomous driving tractor based on a drone according to claim 3, characterized in that: The target value of the subsystem in the automatic driving tractor is optimized based on the deviation between the current position of the tractor and the target position; when the deviation is greater than the set value, the drive system strengthens the target torque based on the deviation value, the drive power demand is strengthened, and the target power of the auxiliary power system is significantly adjusted based on the difference between the drive power demand and the power discharge power of the power battery. The braking system strengthens the management of the target torque only when an obstacle is detected, and weakens it at other times. The steering system weakens the steering angle adjustment range based on the deviation value; when the deviation is less than the set value, the drive system weakens the target torque based on the deviation value, the drive power demand is weakened, the target power of the auxiliary power system is significantly adjusted based on the difference between the drive power demand and the power discharge power of the power battery, the braking system strengthens the management of the target torque, and the steering system strengthens the steering angle adjustment range based on the deviation value.

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