When a mobile robot detects static or dynamic obstacles along its planned route, it updates its path in real time according to a specific algorithm, bypassing the obstacles and finally reaching the target point. This dynamic navigation planning, or obstacle avoidance, requires first and foremost environmental awareness. During robot movement, sensors are needed to acquire real-time information about surrounding obstacles, including but not limited to their size and shape. Various types of sensors are used for obstacle avoidance, each with different principles and characteristics. Currently, commonly used sensors include visual sensors, laser sensors, infrared sensors, and ultrasonic sensors.
Infrared sensor
Infrared ranging generally uses the principle of triangulation. An infrared emitter emits an infrared beam at a certain angle. When the beam encounters an object, it is reflected back. By detecting the reflected light and using the geometric triangulation relationships of the structure, the distance to the object can be calculated.
However, due to the limitations of infrared detectors, when the object is close enough, the sensor will lose its detection position, and when the object is too far away, the measurement accuracy will significantly deteriorate. Therefore, common infrared sensors have relatively short measurement distances, less than ultrasonic sensors, and there are also minimum distance limitations for long-distance measurements. In addition, infrared sensors cannot detect the distance to transparent or nearly blackbody objects.
ultrasonic sensor
The basic principle of an ultrasonic sensor is to measure the time of flight of an ultrasonic wave, using the formula d = vt/2 to measure distance, where d is the distance, v is the speed of sound, and t is the time of flight. 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 generate ultrasonic pulses at frequencies of tens of kHz using piezoelectric or electrostatic transmitters. The system detects reverse sound waves exceeding a certain threshold and calculates the distance using the measured time-of-flight. Ultrasonic sensors generally have a short effective range, typically a few meters, but with a minimum detection blind zone of tens of millimeters. Due to their low cost, simple implementation, and mature technology, ultrasonic sensors are commonly used in mobile robots. However, ultrasonic measurement cycles are relatively long; sound wave transmission to an object approximately 3 meters away takes about 20 ms. Furthermore, multiple ultrasonic sensors may interfere with each other.
laser sensor
Common lidar systems are time-of-flight based, measuring distance by determining the flight time of a laser beam. A lidar system consists of a transmitter and a receiver; the transmitter illuminates the target with a laser, and the receiver receives the reflected light wave. Mechanical lidar systems include a mechanism with a mirror; the rotation of the mirror allows the laser beam to cover a plane, thus enabling the measurement of distance information on that plane.
LiDAR can measure distances of tens or even hundreds of meters, with high angular resolution, typically reaching a few tenths of a degree, and high ranging accuracy. However, the confidence level of the measured distance is inversely proportional to the square of the received signal amplitude. Therefore, distance measurements of black bodies or distant objects are not as accurate as those of bright, nearby objects. Furthermore, LiDAR is ineffective for transparent materials such as glass. Due to its complex structure and high component costs, LiDAR is also very expensive.
Some low-end lidar systems use triangulation for ranging. However, this limits their range, typically to a few meters, and their accuracy is relatively low. But they are still quite effective for SLAM in low-speed indoor environments or for obstacle avoidance in outdoor environments.
Visual sensors
There are many commonly used computer vision solutions, such as binocular vision, Time-of-Flight (TOF) based depth cameras, and structured light based depth cameras. Depth cameras can simultaneously acquire RGB images and depth maps. However, whether based on TOF or structured light, their performance is not ideal in bright outdoor lighting conditions because they both require active light emission.
Like structured light-based depth cameras, the emitted light generates relatively random yet fixed speckle patterns. These specks, when they hit an object, are captured at different positions by the camera due to varying distances. The offset of the captured specks from a calibrated standard pattern at different positions is then calculated. Using parameters such as camera position and sensor size, the distance between the object and the camera can be determined. Active light sources are greatly affected by conditions such as sunlight, making passive vision solutions like binocular vision more suitable. Therefore, most vision solutions adopted are based on binocular vision.
Binocular vision ranging is essentially triangulation. When two cameras see the same point P, the resulting image will have different pixel positions. Triangulation allows us to measure the distance to this point. In practical applications, we read continuous video frame streams from the cameras. We can use these frames to estimate the motion of objects in the scene, build motion models for them, and estimate and predict their direction and speed of motion. This is very useful for planning walking and obstacle avoidance.
The above are some of the most common types of sensors, each with its own advantages and disadvantages. In actual applications, a combination of different sensors is usually used to maximize the robot's ability to correctly perceive obstacle information under various application and environmental conditions.
