With the continuous improvement of sensing technology, intelligent technology and computing technology, intelligent mobile robot will be able to play a human role in production and life. So what are the main aspects of mobile robot positioning technology? It is concluded that at present, mobile robots mainly have these five positioning technologies.
Ultrasonic navigation and positioning technology for mobile robot
The working principle of ultrasonic navigation and positioning is also similar to that of laser and infrared. Usually, ultrasonic wave is emitted from the transmitting probe of ultrasonic sensor, and the ultrasonic wave returns to the receiving device when encountering obstacles in the medium.
By receiving the ultrasonic reflection signal transmitted by itself, and calculating the propagation distance s according to the time difference and propagation speed of ultrasonic transmission and echo reception, the distance from the obstacle to the robot can be obtained, that is, there is a formula: S = TV / 2, in which T - the time difference between ultrasonic transmission and reception; V - wave velocity of ultrasonic wave propagating in medium.
Of course, many mobile robots use separate transmitting and receiving devices in navigation and positioning technology. Multiple receiving devices are arranged in the environmental map, and transmitting probes are installed on the mobile robot.
In the navigation and positioning of mobile robots, it is difficult to fully obtain the surrounding environment information due to the defects of ultrasonic sensors, such as specular reflection and limited beam angle. Therefore, the ultrasonic sensor system composed of multiple sensors is usually used to establish the corresponding environment model, The information collected by the sensor is transmitted to the control system of the mobile robot through serial communication. Then the control system adopts a certain algorithm to process the corresponding data according to the collected signal and the established mathematical model, and the position environment information of the robot can be obtained.
Because of the advantages of low cost, fast information acquisition rate and high range resolution, ultrasonic sensor has been widely used in the navigation and positioning of mobile robot for a long time. Moreover, it does not need complex image technology when collecting environmental information, so it has fast ranging speed and good real-time performance.
Visual navigation and positioning technology of mobile robot
In the visual navigation and positioning system, the navigation mode of installing vehicle camera in robot based on local vision is widely used at home and abroad. In this navigation mode, the control equipment and sensing devices are loaded on the robot body, and the high-level decisions such as image recognition and path planning are completed by the on-board control computer.
Visual navigation and positioning system mainly includes: camera (or CCD image sensor), video signal digitization equipment, fast signal processor based on DSP, computer and its peripherals, etc. At present, many robot systems use CCD image sensors. The basic element is a row of silicon imaging elements. Photosensitive elements and charge transfer devices are configured on a substrate. Through the sequential transfer of charges, the video signals of multiple pixels are taken out time-sharing and sequentially. For example, the resolution of the image collected by area CCD sensor can be from 32 × 32 to 1024 × 1024 pixels, etc.
The working principle of the visual navigation and positioning system is simply to optically process the environment around the robot. First, the camera is used to collect the image information, compress the collected information, and then feed it back to a learning subsystem composed of neural network and statistical methods, Then the learning subsystem connects the collected image information with the actual position of the robot to complete the autonomous navigation and positioning function of the robot.
global positioning system
Nowadays, in the application of intelligent robot navigation and positioning technology, the pseudo range differential dynamic positioning method is generally adopted. The reference receiver and dynamic receiver are used to observe four GPS satellites together, and the three-dimensional position coordinates of the robot at a certain time and moment can be obtained according to a certain algorithm. Differential dynamic positioning eliminates the satellite clock error. For users 1000km away from the reference station, it can eliminate the satellite clock error and tropospheric error, so it can significantly improve the dynamic positioning accuracy.
However, in mobile navigation, the positioning accuracy of mobile GPS receiver is affected by satellite signal conditions and road environment, as well as clock error, propagation error, receiver noise and many other factors. Therefore, the positioning accuracy and reliability of GPS navigation alone are low. Therefore, magnetic compass and Optical code disk and GPS data for navigation. In addition, GPS navigation system is not suitable for indoor or underwater robot navigation and robot systems with high position accuracy.
Optical reflection navigation and positioning technology for mobile robot
The typical optical reflection navigation and positioning method mainly uses laser or infrared sensor to measure the distance. Both laser and infrared use light reflection technology for navigation and positioning.
Laser global positioning system is generally composed of laser rotating mechanism, mirror, photoelectric receiving device and data acquisition and transmission device.
During operation, the laser is emitted outward through the rotating mirror mechanism. When the cooperative road sign composed of backward reflector is scanned, the reflected light is processed by the photoelectric receiver as the detection signal, start the data acquisition program, read the code disk data of the rotating mechanism (the measured angle value of the target), and then transmit it to the upper computer for data processing through communication, According to the known position and detected information of the road sign, the current position and direction of the sensor in the road sign coordinate system can be calculated, so as to achieve the purpose of further navigation and positioning.
Laser ranging has the advantages of narrow beam, good parallelism, small scattering and high ranging direction resolution, but it is also greatly disturbed by environmental factors. Therefore, how to denoise the collected signal when using laser ranging is also a big problem. In addition, there are blind areas in laser ranging, so it is difficult to realize navigation and positioning by laser alone, In industrial applications, it is generally used in industrial field detection within a specific range, such as detecting pipeline cracks.
Infrared sensing technology is often used in multi joint robot obstacle avoidance system to form a large area of robot "sensitive skin", which covers the surface of robot arm and can detect various objects encountered in the operation of robot arm.
A typical infrared sensor includes a solid-state light-emitting diode that can emit infrared light and a solid-state photodiode used as a receiver. The modulated signal is transmitted by the infrared light emitting tube, and the infrared photosensitive tube receives the infrared modulated signal reflected by the target. The elimination of ambient infrared light interference is guaranteed by signal modulation and special infrared filter. Let the output signal VO represent the voltage output of the reflected light intensity, then VO is a function of the distance between the probe and the workpiece: VO = f (x, P), where p - the reflection coefficient of the workpiece. P is related to the surface color and roughness of the target. X - distance between probe and workpiece.
When the workpiece is a similar target with the same p value, X and VO correspond one by one. X can be obtained by interpolating the experimental data of proximity measurement of various targets. In this way, the position of the robot from the target object can be measured by infrared sensor, and then the mobile robot can be navigated and positioned by other information processing methods.
Although infrared sensor positioning also has the advantages of high sensitivity, simple structure and low cost, because of their high angle resolution and low distance resolution, they are often used as proximity sensors in mobile robots to detect approaching or sudden motion obstacles, which is convenient for robot people to stop obstacles in an emergency.
Slam Technology
Most of the industry-leading service robot enterprises adopt slam technology. What is slam technology? In short, slam technology refers to the whole process of robot positioning, mapping and path planning in an unknown environment.
Slam (simultaneous localization and mapping), since it was proposed in 1988, is mainly used to study the intelligence of robot movement. For completely unknown indoor environment, equipped with core sensors such as lidar, slam technology can help the robot build an indoor environment map and help the robot walk independently.
SLAM problem can be described as: the robot starts to move from an unknown position in an unknown environment, locates itself according to position estimation and sensor data, and constructs an incremental map at the same time.
The implementation approaches of slam technology mainly include vSLAM, WiFi slam and lidar slam.
1. VSLAM (visual SLAM)
It refers to navigation and exploration with depth cameras such as camera and Kinect in indoor environment. Its working principle is simply to carry out optical processing on the surrounding environment of the robot. Firstly, the camera is used to collect the image information, compress the collected information, and then feed it back to a learning subsystem composed of neural network and statistical methods, and then the learning subsystem connects the collected image information with the actual position of the robot, Complete the autonomous navigation and positioning function of the robot.
However, the indoor vSLAM is still in the research stage and is far from practical application. On the one hand, the amount of calculation is too large, which requires high performance of the robot system; On the other hand, the maps generated by vSLAM (mostly point clouds) can not be used for robot path planning, which needs further exploration and research.
2．Wifi－SLAM
It refers to using a variety of sensing devices in smart phones for positioning, including WiFi, GPS, gyroscope, accelerometer and magnetometer, and drawing accurate indoor map from the obtained data through machine learning, pattern recognition and other algorithms. The provider of this technology was acquired by apple in 2013. It is unknown whether Apple has applied WiFi slam technology to the iPhone, so that all iPhone users are equivalent to carrying a small drawing robot. There is no doubt that more accurate positioning is not only conducive to the map, but also makes all location dependent applications (LBS) more accurate.
3．Lidar SLAM
It refers to the use of lidar as a sensor to obtain map data, so that the robot can realize synchronous positioning and map construction. As far as the technology itself is concerned, it has been quite mature after years of verification, but the bottleneck of lidar's high cost needs to be solved urgently.
Google driverless cars use this technology. The lidar installed on the roof comes from velodyne company of the United States and sells for more than $70000. This lidar can emit 64 laser beams to the surrounding when rotating at high speed. When the laser touches the surrounding objects and returns, it can calculate the distance between the vehicle body and the surrounding objects. The computer system then draws a fine 3D topographic map according to these data, and then combines it with the high-resolution map to generate different data models for the on-board computer system. Lidar accounts for half of the cost of the whole vehicle, which may also be one of the reasons why Google's unmanned vehicles are unable to be mass produced.
Lidar has the characteristics of strong directivity, which can effectively ensure the accuracy of navigation and adapt to the indoor environment. However, lidar slam has not performed well in the field of robot indoor navigation, because the price of lidar is too expensive.