Most Common Positioning technology used for autonomous mobile robots
Autonomous mobile robots require a variety of sensors to properly explore their environments. The robot or autonomous system can detect obstructions in its path as it moves towards its target and understand the surroundings thanks to these sensors.
We can divide the devices into 2 types:
- Internal sensing device- enables the mobile robot to see and understand the internal aspects. The robot’s position, acceleration, velocity, and torque can all be detected by these autonomous sensors.
- External sensors device- pick up on conditions outside the robot or autonomous system. These might include RGB cameras, proximity warning systems, infrared and ultrasonic detection systems, and others.
Naturally, having a good sensor for an autonomous mobile robot is crucial since they are basically the eyes and ears of the robot, allowing them to move around accurately and safely. Following are a few examples of the most common positioning technologies:
- Global Navigation Satellite Systems (GNSS): Global positioning data is provided by GNSS systems like GPS (Global Positioning System), GLONASS (Global Navigation Satellite System), and Galileo using satellite signals. These systems are frequently utilized in outdoor settings because they can deliver precise positional information.
- Inertial Measurement Units (IMUs): IMUs use accelerometers and gyroscopes to measure a robot’s angular and linear motion. An IMU may infer the robot’s position and orientation by integrating the motion data across time. IMUs, however, experience drift over time and need to be periodically calibrated or fused with other positioning systems.
- Visual Odometry: Using cameras to track visual characteristics in the surroundings, visual odometry approaches estimate the robot’s motion by examining changes in these features. Visual odometry can estimate a relative position by integrating the estimated motion across time. However, surroundings with low-texture or dynamic sceneries are more prone to errors.
- LIDAR (Light Detection and Ranging): Laser beams are emitted by LIDAR sensors, which then time how long it takes for the lasers to return after striking nearby objects. By scanning the area, LIDAR can produce a thorough 3D map of the robot’s surroundings and determine the robot’s location using the map and the distances measured. Robotics frequently uses LIDAR for precise and dependable positioning.
- Simultaneous Localization and Mapping (SLAM): A map of the robot’s surroundings is simultaneously created using SLAM algorithms, which incorporate sensor data from cameras and LIDAR inputs. In situations when the environment is unknown or always changing, SLAM is especially helpful.
Different sensors can work effectively together through sensor fusion algorithms to accomplish challenging goals. The success of the autonomous mobile robot is largely dependent on the sensors and sensor software algorithms to carry on their specific tasks.
Photo by Canva
References
https://deepblue.lib.umich.edu/bitstream/handle/2027.42/34938/2_ftp.pdf
https://www.euspa.europa.eu/european-space/eu-space-programme/what-gnss
https://towardsdatascience.com/what-is-imu-9565e55b44c
https://springerplus.springeropen.com/articles/10.1186/s40064-016-3573-7
https://www.neonscience.org/resources/learning-hub/tutorials/lidar-basics
https://www.flyability.com/simultaneous-localization-and-mapping
https://www.sciencedirect.com/topics/engineering/sensor-fusion
