According to incomplete statistics, the global market size of unmanned operating vehicles has exceeded billions of dollars, and there is still a lot of room for growth in the future.
Problems encountered in automatic robot control systems and solutions for inertial sensors
Hardware limitations: The processing power and storage capacity of AGV machines are limited, which limits the real-time performance and accuracy of SLAM technology. Good hardware often requires higher costs and higher power consumption.
Inertial sensors have high frequency, fast and real-time output of information such as acceleration, velocity, position, angular velocity, attitude, etc. At the same time, inertial sensors have small size, low power consumption, and low price.
Environmental complexity: AGV machines typically operate in complex environments, such as crowded public places and complex terrain in the wild. High and low temperatures, light reflections, and even vibrations can interfere with the normal operation of SLAM technology in these complex environments.
High precision inertial sensors have higher stability and reliability, and can obtain accurate data in real-time in complex environments such as motion, vibration, and temperature changes.
Limitations of algorithms: Existing SLAM algorithms cannot meet the requirements of all scenarios, such as requiring higher accuracy, real-time performance, or robustness in certain scenarios, and may also encounter issues such as algorithm tuning and parameter settings.
Difficulty in coping with changes in motion posture: During the process of robot operation and material transportation, the robot's posture will frequently change. Without an IMU module, the robot will find it difficult to cope with various posture changes, and the motion control effect will be affected.
Inertial sensors come with gyroscopes and accelerometers, which are very sensitive to motion states such as velocity and attitude.
Integration of Inertial Navigation and SLAM Technology
Inertial navigation systems calculate the position and direction of vehicles by measuring their acceleration and angular velocity, while SLAM technology achieves autonomous vehicle navigation by simultaneously recognizing the environment, positioning, and mapping. Integrating the two technologies can effectively improve the accuracy and robustness of autonomous navigation.
Firstly, use the Inertial Navigation System (INS) to measure the acceleration and angular velocity of the vehicle, and obtain the acceleration and angular velocity of the vehicle. According to Euler's formula, integrating angular velocity can obtain the orientation of the vehicle, and integrating acceleration can obtain the position of the vehicle.
2. Utilize SLAM technology for map construction, which involves collecting environmental information through multiple sensors (such as LiDAR, cameras, etc.), identifying landmarks, and using mapping algorithms to construct a map of the vehicle's environment and determine its location.
3. Integrate the measurement results of the Inertial Navigation System (INS) into SLAM technology, and use methods such as Kalman filtering to integrate the information of the two technologies to obtain more accurate and reliable vehicle positioning and environmental information.
4. Utilize the fused data to optimize path planning and control. For example, using a path planner in conjunction with fused data can avoid vehicle swaying during drastic environmental changes and improve trajectory tracking performance.
Overall, integrating inertial navigation system and SLAM technology can solve the problems of both, improve the accuracy and robustness of vehicle autonomous navigation, and enhance the reliability and efficiency of AGV applications.
Forsense's advantages in the field of robotics
As a positioning technology and product provider with MEMS inertial components as the core. Forsense has a rich and comprehensive IMU product line. And it has formed a series of advantages in years of development.
High cost-effectiveness: Low precision IMU integration has significant deviation, which affects positioning accuracy and long-term stability. On the market, high-precision IMUs are expensive. Forsense significantly improves sensor accuracy and stability through large-scale array algorithms. At the same time, it also ensures that the cost of the product is well controlled.
Comprehensive and Rich Product Line: Over the years, Forsense has developed various specifications and forms of inertial sensor products to meet the diverse needs of the market, which can meet the diverse needs of robot control systems for inertial sensors.
Strong reliability: All products undergo batch refinement temperature compensation and independent turntable calibration before leaving the factory. Using a rigorous and scientific experimental verification system, design and comprehensively test and verify the factors that may affect the integration performance or reliability of IMUs during the production process, ensuring that each IMU can operate reliably throughout its lifecycle and reducing system failure rates.
Excellent Algorithm: With years of experience in MEMS IMU fusion and independently developed diverse fusion algorithms, we help customers save product introduction time, reduce technical difficulty, and lower technical investment costs.
Comprehensive and in-depth technical support: We focus on inertial sensing technology, providing professional technical support to customers, reducing development difficulties and technical investment, and enhancing the competitiveness of terminal products.