A key indicator of mobile robot intelligence is autonomous navigation, and a fundamental requirement for achieving autonomous navigation is obstacle avoidance. Let's learn about obstacle avoidance in mobile robots. Obstacle avoidance refers to the process by which a mobile robot, based on collected obstacle state information, uses sensors to detect static and dynamic objects that impede its passage during navigation and effectively avoids these obstacles using specific methods to ultimately reach its target point.
Environmental perception is essential for obstacle avoidance and navigation. Obstacle avoidance in unknown or partially unknown environments requires sensors to acquire information about the surrounding environment, including the size, shape, and location of obstacles. Therefore, sensor technology plays a crucial role in obstacle avoidance for mobile robots. The sensors used for obstacle avoidance mainly include ultrasonic sensors, visual sensors, infrared sensors, and laser sensors.
Commonly used sensors for obstacle avoidance in mobile robots
1. Laser sensor
Laser rangefinders use lasers to measure parameters such as the distance or displacement of an object. A common method involves a pulsed laser emitting a very short-duration laser pulse, which travels a short distance to the target and then returns, where the echo is received by a photodetector. The distance to the target is calculated based on the interval between the main wave signal and the echo signal, i.e., the round-trip time of the laser pulse from the laser to the target. Because of the high speed of light, the round-trip time is extremely short when measuring small distances. Therefore, this method is not suitable for measuring distances requiring very high precision (sub-millimeter level). For very high precision requirements, triangulation or phase methods are commonly used.
2. Vision sensor
The advantages of visual sensors are their wide detection range and rich information acquisition. In practical applications, multiple visual sensors are often used or combined with other sensors. Through certain algorithms, they can obtain a wealth of information such as the shape, distance, and speed of an object. Alternatively, a sequence of images from a single camera can be used to calculate the distance and speed of a target. The SSD algorithm can also be used to calculate the relative displacement between the robot and the target based on a single camera's motion image. However, in image processing, edge sharpening, feature extraction, and other image processing methods are computationally intensive, have poor real-time performance, and place high demands on the processor. Furthermore, visual ranging methods cannot detect the presence of transparent obstacles such as glass, and are greatly affected by the intensity of light and smoke in the field of view.
3. Infrared sensor
Most infrared sensor ranging is based on the principle of triangulation. An infrared emitter emits an infrared beam at a certain angle. When it encounters an object, the beam is reflected back, as shown in the figure. The reflected infrared light is detected by a CCD detector, which generates an offset value L. Using trigonometric relationships, given the emission angle α, offset L, central moment X, and the focal length f of the filter, the distance D from the sensor to the object can be calculated geometrically. The advantages of infrared sensors are that they are unaffected by visible light, can measure day and night, have high angular sensitivity, simple structure, and are relatively inexpensive, and can quickly detect the presence of objects. However, the measurement is greatly affected by the environment; the object's color, orientation, and surrounding light can all cause measurement errors, resulting in less precise measurements.
4. Ultrasonic sensor
The principle behind ultrasonic sensors for distance detection is to measure the time difference between the emission of an ultrasonic wave and its subsequent detection, and then calculate the distance to the object based on the speed of sound. Since the speed of ultrasound in air is related to temperature and humidity, more accurate measurements require taking into account changes in temperature and humidity, as well as other factors. Ultrasonic sensors generally have a short effective range, typically between 5-10 meters, but they have a minimum detection blind zone, usually several tens of millimeters. Due to their low cost, simple implementation, and mature technology, ultrasonic sensors are commonly used in mobile robots.
