Unmanned aerial vehicle real-time path planning system based on dynamic environment perception

Abstract

The invention provides an unmanned aerial vehicle real-time path planning system based on dynamic environment perception, and the system comprises the steps: firstly carrying out environment perception and data acquisition: employing a sensor to obtain the data of a surrounding environment in real time, carrying out the edge calculation and cloud data processing, and guaranteeing the real-time performance and accuracy; and then path planning and decision support are carried out: path planning is carried out by combining machine learning and an optimization method based on environment perception data, and an optimal flight path is rapidly and dynamically generated. And finally, performing real-time execution and control: after the path planning is completed, enabling the unmanned aerial vehicle to accurately fly according to a preset path through an autonomous navigation and control system, and performing real-time adjustment when needed. According to the invention, the advanced sensing technology and the real-time data processing capability are utilized, dynamic flight path planning and optimization are provided for the unmanned aerial vehicle in a changeable and unpredictable environment, and the safety and efficiency of task execution are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic navigation and path planning of unmanned aerial vehicles, and in particular to a real-time path planning system for unmanned aerial vehicles based on dynamic environment perception. Background Art

[0002] In recent years, drone technology has been widely used in many fields, from military reconnaissance, public security monitoring to commercial transportation, agricultural monitoring, etc., which have demonstrated its unique value and potential. Although drone technology has made significant progress, it still faces many challenges in path planning and navigation in complex environments, especially in dynamically changing environmental conditions. How to achieve efficient and safe flight management is one of the key issues.

[0003] The limitations of existing technologies are: 1. Traditional UAV path planning is mostly based on a preset environmental model, which usually assumes that the environment is static and unchanging. However, in practical applications, UAVs often need to operate in a constantly changing environment, such as sudden changes in weather conditions and changing ground activities. Static path planning cannot effectively deal with these dynamic factors, which may lead to mission failure or UAV loss.

[0004] 2. UAVs need to process a large amount of data in real time when performing tasks, including data collected from sensors and information about the external environment. Data processing and decision-making in existing systems often have delays, which is particularly disadvantageous in situations where rapid response is required in UAV path planning, such as emergency rescue or complex terrain flight.

[0005] 3. Existing path planning technologies often rely on GPS and other navigation systems, which may not be accurate enough or completely unavailable in certain environments, such as urban canyons, confined environments or basements. In addition, existing technologies often lack sufficient accuracy and reliability in handling obstacle identification and avoidance, increasing flight safety risks.

[0006] 4. Existing UAV path planning algorithms usually do not optimize energy consumption, which is a significant issue for battery-powered UAVs. Without effective management of energy consumption, the UAV may run out of power and fail to complete the mission or return safely.

[0007] Technological innovation needs include: 1. The new technology should be able to receive and process data from multiple sensors in real time, such as optical, infrared, radar, etc., as well as real-time weather information and geographic information system (GIS) data, and dynamically adjust the flight path to cope with environmental changes.

[0008] 2. By adopting advanced data processing architectures and algorithms, such as edge computing and machine learning technologies, rapid data processing and instant decision support can be achieved to reduce system response time and improve decision accuracy.

[0009] 3. Use deep learning and computer vision technology to improve the accuracy of obstacle detection and the autonomous obstacle avoidance capability of drones, ensuring flight safety in complex environments.

[0010] 4. Develop new algorithms to optimize path planning, reduce unnecessary flight distance and time, thereby reducing energy consumption and extending the operating time and life cycle of drones.

[0011] Providing efficient and safe path planning technology for drones in dynamic environments can not only expand their application scope, but also significantly improve mission success rate and operational safety. This will provide important support for the commercial and public safety applications of drone technology and have significant social and economic value. Summary of the invention

[0012] In order to solve the problems of the prior art, the present invention provides a real-time path planning system for UAVs based on dynamic environment perception, which utilizes advanced sensing technology and real-time data processing capabilities to provide dynamic flight path planning and optimization for UAVs in changeable and unpredictable environments, thereby ensuring the safety and efficiency of mission execution.

[0013] The present invention provides a real-time path planning system for an unmanned aerial vehicle based on dynamic environment perception, comprising the following steps: 1) Environmental perception and data acquisition: Use sensors to obtain data about the surrounding environment in real time, and perform edge computing and cloud data processing to ensure real-time and accuracy; 2) Path planning and decision support: Based on environmental perception data, combined with machine learning and optimization methods, path planning is performed to quickly and dynamically generate the best flight path. The algorithm considers the priority of flight missions, risk assessment, energy saving and time efficiency. During the path planning process, collision detection and obstacle avoidance strategies are implemented to ensure safety during flight. 3) Real-time execution and control: After path planning is completed, the drone uses autonomous navigation and control systems to accurately fly along the predetermined path and make real-time adjustments when necessary.

[0014] Further improvement, the surrounding environment data in step 1) includes ground obstacles, other flight obstacles, terrain height and meteorological conditions.

[0015] Further improvement, step 3) the autonomous navigation and control system includes an inertial navigation unit, a global positioning system and a visual navigation system.

[0016] The real-time path planning system for UAVs based on dynamic environment perception is an advanced technical solution that can instantly obtain and analyze key information in the flight environment, such as terrain, obstacles, weather, etc., by integrating advanced sensor technology and real-time data processing algorithms. Based on the real-time collected environmental data, the system automatically optimizes the flight path using intelligent path planning algorithms to avoid obstacles to the greatest extent, improve flight safety, and meet the needs of flight missions while ensuring flight efficiency.

[0017] The beneficial effects of the present invention are: It can achieve high-precision environmental perception, ensuring that the UAV can perceive environmental changes in a timely and accurate manner during flight, thereby effectively reducing flight risks.

[0018] The application of intelligent path planning algorithms enables the UAV to quickly respond to complex flight mission requirements, including safe flight in complex terrain and severe weather conditions.

[0019] The real-time and autonomy of the system enables the UAV to automatically adjust its flight path to adapt to changing environmental conditions, improving the efficiency and reliability of mission execution. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present invention are described clearly and completely below. 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.

[0021] The design and implementation of UAV path planning technology involves multiple key technologies and steps to ensure that the UAV can perform flight missions safely and efficiently in complex environments. The following are the specific contents of this technical solution: 1. Environmental perception and data acquisition Environmental perception is the basis of drone path planning. We use a variety of sensor technologies, including optical, infrared, radar, etc., to obtain real-time data about the surrounding environment. These sensors can not only identify ground obstacles and other flight obstacles, but also provide important information such as terrain height and weather conditions. Sensor data is processed in real time and accurately through efficient data acquisition and processing systems such as edge computing and cloud data processing.

[0022] Key technologies include: sensor integration and optimization, real-time data processing, and geographic information system (GIS) integration.

[0023] 2. Path planning and decision support Based on the data from environmental perception, we have developed advanced path planning algorithms. These algorithms combine machine learning and optimization methods to quickly and dynamically generate the best flight path. The algorithms consider multiple factors, such as the priority of the flight mission, risk assessment, and energy and time efficiency. During the path planning process, we also implement collision detection and obstacle avoidance strategies to ensure safety during flight.

[0024] Key technologies include: dynamic path planning algorithm, obstacle avoidance and collision detection, and multi-objective optimization.

[0025] 3. Real-time execution and control After the path planning is completed, the drone needs to be able to accurately perform the flight mission. Our system is equipped with advanced autonomous navigation and control systems, including inertial navigation unit (IMU), global positioning system (GPS) and visual navigation system. These systems work together to ensure that the drone can fly accurately along the predetermined path and make real-time adjustments when necessary.

[0026] Key technologies include: autonomous navigation and control, real-time feedback and adjustment, safety and emergency response.

[0027] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, the above is only a preferred embodiment of the present invention. Since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited to this. Any technician familiar with the technical field is within the technical scope disclosed by the present invention. For ordinary technicians in the technical field, changes or replacements that can be easily thought of should be covered within the protection scope of the present invention without departing from the principle of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A real-time path planning system for unmanned aerial vehicles based on dynamic environment perception, characterized in that The following steps are involved: 1) Environmental perception and data acquisition: Use sensors to obtain data about the surrounding environment in real time, and perform edge computing and cloud data processing to ensure real-time and accuracy; 2) Path planning and decision support: Based on environmental perception data, combined with machine learning and optimization methods, path planning is performed to quickly and dynamically generate the best flight path. The algorithm considers the priority of flight missions, risk assessment, energy saving and time efficiency. During the path planning process, collision detection and obstacle avoidance strategies are implemented to ensure safety during flight. 3) Real-time execution and control: After path planning is completed, the drone uses autonomous navigation and control systems to accurately fly along the predetermined path and make real-time adjustments when necessary.

2. The UAV real-time path planning system based on dynamic environment perception according to claim 1 is characterized in that: Step 1) The surrounding environment data includes ground obstacles, other flight obstacles, terrain height and meteorological conditions.

3. The UAV real-time path planning system based on dynamic environment perception according to claim 1 is characterized in that: Step 3) The autonomous navigation and control system includes an inertial navigation unit, a global positioning system and a visual navigation system.