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Previously, Xiao Bian discussed several obstacle avoidance principles and their respective advantages and disadvantages in the "Triple Realm of Unmanned Aircraft Obstacle Avoidance." At the end of that article, it was also mentioned that current consumer-grade drone manufacturers are still in the first realm — simply trying to perceive obstacles. (Forget it, please read the "Triple Realm of Unmanned Aircraft Obstacle Avoidance"). So what is the realm of industry-level UAV obstacle avoidance? What are the obstacle avoidance methods suitable for field plant protection drones? And what are their limitations?
Currently, the types of sensors commonly used in obstacle avoidance applications include radar ranging, binocular vision, laser ranging, and ultrasonic ranging. The plant protection drone is affected by the complex farmland environment, which significantly increases the difficulty of obstacle avoidance, and the application scenarios are also very different.
Choosing an obstacle avoidance system in principle
Common radar ranging, laser ranging, and ultrasonic ranging use distance sensors. These systems work by emitting electromagnetic waves or lasers, sound waves, and measuring the time difference of the echo reflected from obstacles ahead. The strength of the echo reflected from the object's material is also very different.
The principle of visual obstacle avoidance is to calculate the distance of each point based on the difference in images, similar to how human eyes perceive depth. However, this method involves more complicated algorithms and requires significant processing power and excellent algorithm design.
Infrared perception generally works on the principle of triangulation. An infrared emitter sends out an infrared beam at a certain angle, and when it hits an object, the light reflects back. The CCD then detects the reflected light and calculates the object's distance using the geometric relationship of the structure.
However, for plant protection drones, other obstacle avoidance methods are not as effective as radar wave ranging. Visual obstacle avoidance, for example, can be seriously affected by flying dust and liquid medicine in the farmland environment, which can block the visual system and cause accidents. Therefore, visual obstacle avoidance is not very suitable for farmland environments, while radar wave technology can handle these challenges effectively, making it a better choice at present.
Accuracy requirements for obstacle avoidance in adapting to farmland
Plant protection drones usually fly only 1-5 meters above the ground in farmland, meaning they face many obstacles. For instance, small trees growing in the middle of fields, poles, and wires. Cable-stayed cables, which are typically only 2-3 cm in diameter, are difficult to spot with the naked eye and pose a special danger to plant protection drones. Therefore, the obstacle avoidance system must have very high performance to meet the requirements of plant protection drones. Lower precision systems are far from sufficient for such tasks.
Exploring obstacle avoidance methods
Now let’s talk about obstacle avoidance methods. After detecting obstacles, there are three common approaches: on-site suspension, planned route avoidance, and autonomous obstacle avoidance.
Suspension is the most basic way of handling, relatively simple and safe, and is necessary when encountering major obstacles. Planned route avoidance requires exploring obstacles before operation and setting up obstacle avoidance routes in the flight path. This method is economical and reliable but requires people to participate in daily surveys. Autonomous obstacle avoidance requires the machine to use algorithms to evade obstacles and regenerate routes on its own. However, after re-generation, new problems may arise, such as how far to go and how to avoid new obstacles.
Redundant obstacle avoidance actions affect efficiency
When a plant protection drone automatically avoids obstacles during flight, redundant actions can become a burden on battery life. If two obstacles are encountered, the remaining power may not be enough to return. Therefore, there are still many issues to solve in autonomous obstacle avoidance functions, and there is no practical application yet.
In the future, the plant protection drone’s obstacle avoidance system should be able to detect obstacles in all directions without blind spots, identify various obstacles efficiently and accurately, and ensure the safety of our plant protection drones. At the same time, the obstacle avoidance system should be highly integrated, modular, with high protection levels and easy to maintain, to better adapt to farmland conditions.
As plant protection drones become increasingly popular, these machines need to be smarter, have more powerful performance, and undergo extensive testing to select the best obstacle avoidance systems for plant protection drones.