Want to understand intelligent robots? Interpretation of intelligent robot multi-sensor fusion technology

In this article, the editor will introduce the relevant content and situation of the robot “target=”_blank”> intelligent robot to help you improve your understanding of intelligent robots. Read the following content with the editor.

Intelligent robots can be said to be one of the most common artificial intelligence products we currently have. In previous research on service robots, a typical target application is that robots can do housework, which requires robots to use their arms to manipulate objects (grab and place). Although there are many studies in these areas, from the perspective of current technological progress, there are still many challenges in realizing robots to do housework in a few years.

The cost of current robots, especially humanoid robots, is prohibitive. For example, a robot arm may cost tens of thousands of dollars, while a robot hand costs more than 10,000 dollars. The cost of the entire robot is beyond the reach of ordinary families. The dexterity of the manipulator is still difficult to compare with the human hand.

So, what are the most important technologies in intelligent robots? Multi-sensor information fusion technology is one of them.

Multi-sensor information fusion technology is a very popular research topic in recent years. It combines control theory, signal processing, artificial intelligence, probability and statistics to provide technical solutions for robots to provide tasks in various complex, dynamic, uncertain and unknown environments. .

There are many types of sensors used in robots, and they are divided into two categories: internal measurement sensors and external measurement sensors according to different uses. Internal measurement sensors are used to detect the internal state of robot parts, including: specific position, angle sensor; arbitrary position, angle sensor; speed, angle sensor; acceleration sensor; tilt angle sensor; azimuth sensor, etc. External sensors include: vision, touch, force and angle sensors. Multi-sensor information fusion refers to the integration of sensory data from multiple sensors to generate more reliable, accurate or comprehensive information. The fused multi-sensor system can reflect the characteristics of the detected objects more completely and accurately, eliminate the uncertainty of information, and improve the reliability of information. The characteristics of fused multi-sensor information technology include: redundancy, complementarity, real-time, and low cost.

Multi-sensor information fusion technology is a very active research field. The main research directions of multi-sensor information fusion technology mainly include three, one is multi-level sensor fusion, the second is micro-sensors and smart sensors, and the third is adaptive multi-sensor fusion. . Below, let’s take a look at these aspects one by one.

1. Multi-level sensor fusion

Due to the weaknesses of individual sensors such as uncertainty, observation errors and incompleteness, single-layer data fusion limits the capability and robustness of the system. For advanced systems that require high robustness and flexibility, a multi-stage sensor fusion approach can be used. Low-level fusion methods can fuse multi-sensor data; mid-level fusion methods can fuse data and features to obtain fused features or decisions; advanced fusion methods can fuse features and decisions into final decisions.

2. Microsensors and Smart Sensors

The performance, price and reliability of the sensor are important indicators to measure the quality of the sensor. However, many sensors with good performance limit the application market due to their large size. The rapid development of microelectronics technology makes it possible to manufacture small and miniature sensors. Smart sensors integrate main processing, hardware and software.

3. Adaptive multi-sensor fusion

In the real world, it is difficult to obtain accurate environmental information, and there is no guarantee that the sensor will always work properly. Therefore, for various uncertain situations, a robust fusion algorithm is required. Currently, some adaptive multi-sensor fusion algorithms have been developed to deal with the uncertainty caused by imperfect sensors. For example, Hong proposes an extended joint method through innovative techniques, which can estimate the optimal Kalman gain for filtering a single measurement sequence. Pacini and Kosko also developed an adaptive target tracking fuzzy system that can be applied under slight ambient noise, which combines the Kalman filter algorithm in the process.

The above is the whole content of the multi-sensor fusion information technology of intelligent robots brought by the editor this time. Thank you very much for your patient reading. If you want to know more related content, or more exciting content, please pay attention to our website. .

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