The process behind autonomous robots navigating streets
Most likely, we have all seen wheeled robots on the internet moving around, probably carrying things from point A to point B autonomously. This process is clearly not done magically, and a lot goes into the technology to make that possible.
For any autonomous mobile robot, the ability to navigate in its environment is imperative to avoid dangerous situations such as collisions and unsafe conditions. This aspect is paramount to every type of autonomous robot in all sectors, whether it is robots in agriculture, delivery, inspection etc.
Navigation can be defined based on the combination of three fundamental capabilities:
- Path planning
- Map-building and map interpretation
Some robot navigation systems combine localization and mapping to generate 3D reconstructions of their surroundings.
To carry out successful navigation, the robot goes through several stages, including perception, localization, cognition, and motion control. During the perception phase, the robot interprets its sensors to extract relevant data. Using data from external sensors, the robot determines its current location in the area during the localization phase. The robot plans the necessary steps to get to the destination during the cognition phase. By adjusting its motor outputs, the robot can attain its target trajectory during the motion control phase.
For simplicity purposes, I will make use of two categories to explain the localization problem better, location tracking and global positioning/localization, based on the information of the initial position:
- In position tracking, the goal is to follow the robot at every point in time as it navigates through the environment. The robot’s beginning position is known.During the localization, the algorithm uses the robot’s past position during navigation to update its present location. This is made possible by continuously tracking the robot’s path. Data from sensors and odometry are used in position tracking. However, the robot might not be localised if there is a lot of uncertainty. Thus, it is necessary for position monitoring that the robot’s location uncertainty be minimal.
- For global localization, the robot does not have information about its initial position. In other words, the robot can locate itself globally within the environment.