Robot is a multidisciplinary technology involving mechanical design, computer, sensor, automatic control, human-computer interaction, bionics and many other disciplines. Therefore, there are many problems to be studied in the field of robotics, and sensing, positioning and control are three important issues of robot technology . The following mainly focuses on the environment perception, autonomous positioning, motion control and other aspects of intelligent robots , and briefly describes some of the techniques used.
Environmental awareness
At present, in a structured indoor environment, mobile robots based on machine vision and with other sensors are relatively mature in terms of autonomous environment perception, scene recognition and navigation technology. In outdoor practical applications, due to the diversity of environment, randomness, complexity, and the influence of weather and illumination changes, the task of environmental awareness is much more complicated and the real-time requirements are higher. This has been a research hotspot at home and abroad. Multi-sensor information fusion, environmental modeling, etc. are the technical tasks faced by robot sensing systems.
The environment-aware approach based on a single sensor has its weaknesses that are difficult to overcome. The information of various sensors is organically combined, and by processing redundant and complementary information from different sensors, a detection system covering almost all space and time can be constructed, and the capability of the sensing system can be improved. Therefore, taking advantage of the richness of machine vision information, combined with the ability to acquire distance information by radar sensors, ultrasonic radar sensors or infrared sensors, to realize the perception of the surrounding environment of robots has become a hot topic for scholars in various countries.
The use of a variety of sensors to form an environment-aware system brings problems such as synchronization, matching and communication of multi-source information. It is necessary to study methods and techniques for multi-sensor cross-modal cross-scale information registration and fusion. However, in practical applications, the more sensors and types used, the better. For the specific application of robots in different environments, it is necessary to consider the validity of each sensor data and the real-time calculation.
The so-called environmental modeling refers to the conversion and analysis of relevant features into the feature space that the robot can understand based on the known environmental information. The methods of constructing environmental models are divided into geometric modeling methods and topological modeling methods. The geometric modeling method usually quantizes the mobile robot working environment into a series of grid cells, records the environment information in units of rasters, and searches for paths through tree search or distance conversion. The topology modeling method divides the workspace into sub-features. Space, based on the connectivity of each other to establish a topological network, find the topological path from the starting point to the target point on the network, and then convert to the actual geometric path.
Autonomous positioning
Positioning is one of the three basic problems that mobile robots have to solve. Although GPS has been able to provide high-precision global positioning, its application has certain limitations. For example, indoor GPS signals are weak; in complex urban environments, positioning accuracy is degraded and position is lost due to occlusion of GPS signals, multipath effects, etc.; in military applications, GPS signals are often interfered by enemy forces. . Therefore, GPS-independent positioning technology has broad application prospects in the field of robotics.
At present, the most commonly used autonomous positioning technology is based on the inertial unit's track estimation technology, which uses motion estimation (inertial or odometer) to recursively calculate the position of the robot. However, due to the problem of error accumulation, the dead reckoning algorithm is only suitable for the pose estimation of short-term short-distance motion. For large-scale positioning, the sensor is used to observe the environment and match with the environment map to achieve precise positioning of the robot. . The robot pose can be regarded as the system state, and the pose of the robot is estimated by Bayesian filtering. The most commonly used methods are Kalman filter localization algorithm, Markov localization algorithm and Monte Carlo localization algorithm.
Since the odometer and inertial navigation system errors are cumulative, they must be corrected by other positioning methods over a period of time, so they are not suitable for long-distance precise navigation positioning. In recent years, a method of constructing an environmental model while determining its own position has often been used to solve the problem of robot positioning. This method called SLAM (Simultaneous Localization And Mapping) is the best embodiment of the intelligent level of mobile robots. The ability to have simultaneous mapping and positioning is considered by many to be a key prerequisite for robots to achieve autonomy.
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