I believe that all the officers in front of the screen should have experienced the sweeping. Recall that the process of sweeping the floor can be broken down into two main actions: go to a certain position; clean it. Correspondingly, as a substitute for human sweeping work, the sweeping robot also contains two main systems: the autonomous navigation system and the cleaning system.
If the cleaning system is the cost of the sweeping robot in the name of "sweeping the ground", it is the heart of the sweeping robot; then the autonomous navigation system is the base of the "robot", and it is the brain of the sweeping robot. If there is no autonomous navigation system, then it can only be called a “sweeping machine†instead of a “sweeping robotâ€. Let's take a closer look at how the "bottom gas" of the sweeping robot is produced.
Three classic problems with robot navigation
Speaking of robotic autonomous navigation, it can be summarized as three questions raised by MIT professor John J. Leonard and former University of Sydney professor Hugh Durrant-Whyte:
(1) Where am I?
(2) Where I am going?
(3) How should I go there?
The first problem is the positioning problem of the robot, that is, how to determine the position of the robot in the current environment based on the information currently observed and previously known. The second and third questions are actually specifying a goal and then planning a certain path to achieve it. For a typical mobile robot, this goal is a point, point-to-point navigation. For the sweeping robot, the goal is not to reach a certain point, but to traverse a certain area to achieve cleaning of the room. Let us first look at the first question today, the positioning of the sweeping robot.
Positioning is the most basic link in the autonomous navigation of mobile robots including sweeping robots, and it is also a problem that must be solved to complete the task. When it comes to positioning, the first thing that people think of is the commonly used outdoor positioning such as GPS positioning and base station positioning.
In contrast, the positioning of the sweeping robot is indoor positioning, which requires high positioning accuracy (at least in the sub-meter level), good real-time performance, GPS, base station positioning and other methods can not be satisfied. The positioning of the sweeping robot can be generally divided into relative positioning and absolute positioning. Let us look at it separately.
Relative positioning method
The Dead-Reckoning Method is a classic relative positioning method and the most widely used positioning method for sweeping robots. It uses the various sensors equipped by the robot to obtain the motion dynamic information of the robot, and obtains the estimated position of the robot relative to the initial test state by the recursive accumulation formula. The most commonly used sensors for dead reckoning are: code discs, inertial sensors (such as gyroscopes, accelerometers).
The code wheel method generally records the number of revolutions of the wheel using an optical code disk mounted on the wheel, thereby obtaining the amount of change of the position and posture of the robot with respect to the previous sampling time, and the position of the robot can be estimated by the accumulation of these displacement amounts. The advantage of the code wheel method is that the method is simple and the price is low, but it is easily affected by factors such as calibration error, wheel slippage, bumps, etc., and the error is large. However, because the code wheel is cheap and easy to use, it can be used for position estimation within a short time distance of the robot.
The inertial sensor uses the gyroscope and accelerometer to obtain the angular acceleration and linear acceleration information of the robot, and obtains the position information of the robot through integration. In general, the positioning accuracy of the inertial sensor is higher than that of the code wheel, but its accuracy is also affected by gyroscope drift, calibration error, sensitivity and other issues. Whether using a code wheel or an inertial sensor, they all have one common disadvantage: there is a cumulative error, and as the travel time and distance increase, the error increases. Therefore, the relative positioning method is not suitable for precise positioning over long periods of time and long distances.
Absolute positioning method
Absolute positioning method means that the robot calculates its own position by calculating the mutual relationship between itself and the reference information by obtaining some known reference information such as some external positions. . Absolute positioning mainly uses beacon-based positioning, environment map model matching positioning, visual positioning and other methods.
Beacon-based positioning
Beacon positioning originally refers to the technology of positioning and navigating using radio waves emitted by radio base stations in navigation or aviation. For the indoor positioning of the robot, the robot receives or observes the beacon of the known position in the environment through various sensors, calculates the relative position of the robot and the beacon, and then substitutes the known beacon position coordinates to solve the robot's position. Absolute coordinates to achieve positioning. The beacon used for positioning needs to meet three conditions:
(1) The position of the beacon is fixed and the absolute coordinates of the beacon are known;
(2) Beacons have active and passive features and are easy to identify;
(3) The beacon position is convenient for observation from all directions.
Beacon positioning methods mainly include trilateration and triangulation. The trilateration is a method of determining the position of the mobile robot based on the measured distance between the robot and the beacon. The trilateration positioning system requires at least three transmitters (or receivers) of known positions, and the receiver (or transmitter) is mounted on the mobile robot. The idea of ​​triangulation and trilateration is roughly the same, and the positioning is performed by measuring the angle between the mobile robot and the beacon.
Beacon-based positioning systems rely on beacons of known features in a range of environments and require sensors to be mounted on the mobile robot to observe beacons. There are many kinds of sensors for beacon observation, such as ultrasonic sensors, laser radars, and visual sensors. It can be measured in real time, without progressive error, relatively high precision and good stability, providing fast, stable and accurate absolute position information, but it costs a lot to install and maintain beacons. There are already more mature beacon-based beacon-based sweeping robots on the market, such as Proscenic's analog GPS satellite three-point positioning technology, iRobot's Northstar navigation and positioning technology, but because of their relatively high price, they are used for relatively high-end. In the product.
Environmental map model matching and positioning
It is the robot that detects the surrounding environment through its own various sensors, uses the local environment information that is perceived to perform local map construction, and matches with the complete map stored in advance. Get your position in the global environment by matching relationships to determine your location. This method is only suitable for some environments with relatively simple structures due to strict conditions.
Vision-based positioning
Scientific research statistics show that about 75% of humans get information from the outside world from vision. The visual system is the closest way for robots to detect human beings. Benefiting from the development of pattern recognition and machine vision, vision-based robot positioning has become a research hotspot in recent years.
The vision-based positioning is mainly divided into monocular vision and binocular vision.
Monocular vision cannot directly obtain the 3D information of the target, and can only obtain the depth information of the feature points in the environment by moving. It is suitable for the case where the work task is relatively simple and the depth information is not high. If the geometric model of the target object is used, the target is When three or more feature points are taken up, information such as the position of the target can be acquired, but the positioning accuracy is not high.
Binocular stereoscopic 3D measurement is based on the principle of parallax, that is, any point on the image plane of the left camera can find the corresponding matching point on the right camera image surface, and then can determine the three-dimensional information of the point, thereby obtaining the corresponding The three-dimensional coordinates of the point. At present, the vision-based sweeping robot has also been launched. iRobot and Dyson launched the high-end sweeping robots RoomBa980 and 360Eye based on visual positioning in 2015 and 2014 respectively.
summary
Autonomous positioning is the basis of autonomous path planning for sweeping robots. After years of research, although the cost and production constraints, the dead reckoning algorithm is still the most widely used positioning method, but through algorithm optimization, hybrid positioning can reduce the impact of its error. Moreover, with the further maturity of positioning methods such as visual positioning and higher positioning accuracy, the cost will gradually decline, and will gradually be pushed from the high-end market to the mass market. At that time, the positioning accuracy and intelligent level of the sweeping robot will have General improvement.
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