The realization of perception and obstacle avoidance by drones is a complex and constantly evolving technical field, involving multiple aspects such as environmental perception, obstacle detection, path planning, and flight control. This article will elaborate in detail on how drones achieve perception and obstacle avoidance from various aspects including the principles of drone obstacle avoidance technology, development stages, sensor selection, obstacle detection, path planning, as well as practical application scenarios.
Principle of Drone Obstacle Avoidance Technology
The core of drone obstacle avoidance technology lies in environmental perception and intelligent decision-making. Drones collect information about the external environment through various sensors they carry, identify obstacles that may hinder their stable flight after processing and analysis, and make obstacle avoidance decisions based on this information. This process can be summarized into three stages: perceiving obstacles, bypassing obstacles, and searching for a path.
- Perception of obstacles
During the flight process, drones first need to perceive potential obstacles in the surrounding environment. This stage relies on sensors carried by the drone, such as ultrasonic radar, millimeter-wave radar, laser infrared sensors, binocular vision sensors, etc. These sensors can capture information about the surrounding environment, such as distance, shape, size, etc., helping drones identify obstacles.
Ultrasonic Radar: Measures distance using sound wave reflection principle and is suitable for detecting nearby obstacles but has limited accuracy and anti-interference capabilities.
Millimeter-Wave Radar: Can penetrate rain and fog in harsh weather conditions with long detection range and high accuracy but comes at a higher cost.
Laser Infrared Sensor: Measures distance by emitting laser beams and receiving reflected light with strong accuracy and anti-interference capabilities but is limited by size and power consumption making it less suitable for small drones.
Binocular Vision Sensor: Mimics human eye stereoscopic vision principles by capturing images through two cameras and calculating disparity to obtain depth information; suitable for complex environments with strong versatility.
1. Perception of obstacles and stop stage
In this stage, the drone will quickly identify and hover down after sensing obstacles through sensors, waiting for the next instruction. Although this obstacle avoidance method is simple, it may not meet the requirements of complex environments in practical applications.
2. Obtaining depth images of obstacles and autonomously avoiding stage
With the development of sensor technology and algorithms, drones can accurately perceive the specific contours and depth images of obstacles through sensors, thereby autonomously avoiding obstacles. The obstacle avoidance method in this stage is more flexible and efficient, able to adapt to more complex environments.
3. Establishing regional maps for reasonable path planning stage
At the highest level, drones can not only sense and bypass obstacles but also establish a three-dimensional map of the flight area and plan flight paths based on that map. This approach enables drones to achieve autonomous navigation and obstacle avoidance in complex environments, greatly enhancing the intelligence level of drones.
Selection of sensors for drone obstacle avoidance technology
The selection of sensors for drones is crucial for obstacle avoidance. Different sensors have different working principles and advantages and disadvantages, suitable for different application scenarios.
1. Ultrasonic Sensor
Ultrasonic sensors use the principle of sound wave reflection to measure distance, with advantages such as long detection range and low cost. However, they have relatively weak accuracy and anti-interference ability, and their detection effectiveness on mirror-like objects or objects that absorb sound waves is poor.
2. Infrared Sensor
Infrared sensors measure distance by emitting infrared light and receiving reflected light. Although infrared sensors have advantages such as long detection range and strong resistance to light interference, their detection effectiveness can be easily affected by factors like lighting conditions and atmospheric attenuation.
3. Laser Sensor
Laser sensors measure distance by emitting laser beams and receiving reflected light, with high accuracy and strong anti-interference ability. However, laser sensors are larger in size, consume more power, expensive, making them less suitable for small drones.
4. Millimeter Wave Radar
In agricultural drone operations where the environment is complex due to field conditions affecting ultrasonic or other optical-based sensor performance under weather conditions; millimeter wave radar can work all day long with strong penetration capabilities over a large operating range providing reliable detection while being resistant to electromagnetic interference. Currently widely used in automotive collision avoidance systems but has lower resolution compared to higher costs .
5. Binocular Vision Sensor
Binocular vision sensor simulates human eye stereoscopic vision principles by capturing images through two cameras calculating disparity to obtain depth information efficiently at a low cost while maintaining high-resolution depth maps within compact dimensions requiring minimal power consumption offering broad prospects in drone obstacle avoidance applications yet susceptible to factors like lighting changes or rotation leading to reduced detection accuracy.
Path planning for drone obstacle avoidance
The path planning for obstacle avoidance of drones is to safely fly the drone from the starting point to the destination while avoiding all obstacles along the way. There are various path planning algorithms, including graph-based algorithms, reinforcement learning-based algorithms, ant colony algorithms, etc.
1. Graph-based algorithm
Graph-based algorithms divide the flight space into a series of nodes and edges forming a graph structure, and avoid obstacles by searching for the shortest path in the graph. This algorithm has the advantage of quick solution finding but may face difficulties in constructing a graph structure in complex environments.
2. Reinforcement learning-based algorithm
Reinforcement learning-based algorithms optimize obstacle avoidance paths by allowing drones to continuously try and learn in simulated environments. This algorithm can adapt to complex and changing environments but requires a large amount of computational resources and time for training.
3. Ant colony algorithm
The ant colony algorithm simulates ants’ path selection behavior during foraging by guiding drones to choose optimal paths through simulating ants releasing pheromones. This algorithm has advantages such as distributed computing and self-organization but may be influenced by environmental noise factors in practical applications.
Practical Application Scenarios of Drone Obstacle Avoidance Technology
The obstacle avoidance technology of drones has broad prospects for application in various fields, including military, civilian, aerial photography, agriculture, and logistics.
1. Military Field
In warfare, drones need to cross enemy positions, mountainous areas, and other complex environments to carry out missions. Obstacle avoidance function can effectively prevent drones from colliding with obstacles, improving safety and mission success rate.
2. Civilian Field
In scenarios such as disaster relief and firefighting in urban areas or forests, drones need to navigate through buildings and forests for reconnaissance or rescue operations. The obstacle avoidance function can enhance the safety and work efficiency of drones, providing strong support for rescue operations.
3. Aerial Photography Field
In aerial photography, drones are required to capture large scenes such as cities and tourist attractions. The obstacle avoidance function can effectively prevent collisions between drones and obstacles like buildings, enhancing the quality and safety of photography.
4. Agricultural Field
Drones are widely used in agriculture for tasks such as pesticide spraying and seeding. The obstacle avoidance function can effectively prevent collisions between drones and obstacles like trees or power lines, improving operational efficiency and quality.
5. Logistics Field
Drones are utilized in logistics for delivering goods such as parcels or packages. The obstacle avoidance function can effectively prevent collisions between drones and obstacles like buildings or roads, enhancing delivery efficiency and safety.
