Infrared sensor Most infrared sensor ranging is based on the principle of triangulation. The infrared transmitter emits an infrared beam at a certain angle. When it encounters an object, the beam will be reflected back, as shown in the figure. After the reflected infrared light is detected by the CCD detector, an offset value l will be obtained. Using the trigonometric relationship, after knowing the emission angle α, offset distance l, central moment x, and the focal length f of the filter, the sensor The distance d to the object can be calculated through geometric relationships. The advantages of infrared sensors are that they are not affected by visible light, can be measured day or night, have high angular sensitivity, simple structure, and are relatively cheap. They can quickly sense the presence of objects. However, the measurement is greatly affected by the environment, such as the color, direction, and surroundings of the object. All light can cause measurement errors and the measurement is not accurate enough.

(Industrial Control) Classification of Robot Obstacle Avoidance Technology Currently, mobile robot obstacle avoidance can be divided into two types: known obstacle information, partially unknown obstacle information, or completely unknown obstacle information according to the degree of mastery of environmental information. Traditional navigation and obstacle avoidance methods such as visual graph method, grid method, free space method and other algorithms can handle the obstacle avoidance problem when the obstacle information is known. However, when the obstacle information is unknown or the obstacle is movable, the traditional navigation obstacle avoidance method can handle the obstacle avoidance problem well. Navigation methods generally cannot solve the obstacle avoidance problem well or cannot avoid obstacles at all. In real life, in most cases, the environment in which robots operate is dynamic, variable, and unknown. In order to solve the above problems, people have introduced some algorithms in the fields of computers and artificial intelligence. At the same time, thanks to the improvement of processor computing power and the development of sensor technology, it has become easier to perform some complex algorithm calculations on mobile robot platforms. This has resulted in a series of intelligent obstacle avoidance methods. The more popular ones are: genetic Algorithms, neural network algorithms, fuzzy algorithms, etc. are introduced below. Robot obstacle avoidance algorithm based on genetic algorithm: Genetic algorithm (genetic algorithm, referred to as ga) is a search algorithm used to solve optimization problems in computational mathematics, and is a type of evolutionary algorithm. Evolutionary algorithms are developed based on phenomena such as inheritance, mutation, natural selection, and hybridization in evolutionary biology. Genetic algorithms use several operators abstracted from natural evolution to perform genetic operations on parameter-encoded strings, including reproduction or select operators (reproduction or select), crossover operators (crossover), and mutation operators (mutation). (Industrial control video) Ultrasonic sensor The detection distance principle of the ultrasonic sensor is to measure the time difference between when the ultrasonic wave is emitted and when the ultrasonic wave is detected again, and at the same time, the distance of the object is calculated based on the speed of sound. Since the speed of ultrasonic waves in the air is related to temperature and humidity, changes in temperature, humidity and other factors need to be taken into account in more accurate measurements. Ultrasonic sensors generally have a short range of action, and the common effective detection distance is between 5-10m, but there will be a minimum detection blind zone, usually tens of millimeters. Due to the low cost, simple implementation method and mature technology of ultrasonic sensors, ultrasonic sensors are commonly used sensors in mobile robots. The main advantages of the genetic algorithm are: it uses a group method to conduct a multi-clue parallel search on the objective function space without falling into a local minimum point; it only requires the value of the feasible solution to the objective function and does not require other information, which affects the continuity of the objective function. , there is no requirement for differentiability, and it is easy to use; the solution is selected and generated in a probabilistic way, so it has strong adaptability and robustness.

Reference material: Infrared Sensor-Industrial Control Network