Robot navigation or localization refers to the ability of a robot to establish its position and orientation in a given space, an essential step to eventually be able to navigate and traverse said environment on its faculties.
Autonomous robotic navigation mainly relies on sensors to be able to identify their distance concerning other objects. Combining this depth input with pre-loaded paths and maps it becomes possible for robots to navigate complex spaces to satisfaction and without crashing into unexpected obstacles.
One of the most common sensor systems used in the market is LiDAR (Light Detection and Ranging) which allows robots to identify the depth of their surroundings by calculating the time of flight of light beams that bounce of nearby objects. A traditional LiDAR sensor is capable of both emitting these beams and calculating the distance based on the time of flight in real-time, which makes it a reliable and popular pick for robots that are expected to navigate in real-time.
A Vector Filled Histogram is a real-time obstacle avoidance method that relies on a two-dimensional cartesian histogram as a grid model. In this system, the grid works as a simple representation of the robot’s environment, and based on the sensor input the information for each area in the grid gets constantly updated to help the robot navigate its environment safely.
AGV or Automated Guided Vehicle is one of the most traditional and successful systems used in the world of robot navigation. In an AVG system robots are aided in their navigation through physical path guidance. These methods of path guidance include embedded magnets or wires for direct guidance or painted lines that provide a direct path for the robots to follow. While traditionally limited recent changes in digital technology have allowed making more dynamic AVG systems with preprogrammed alternate pathways.
Vision-based navigation refers to robot positioning systems where cameras operate as the main sensor from which environmental input is obtained. Traditionally camera vision was limited due to its lack of depth perception and the complexity of navigating with this detailed yet superficial information. However high-end vision cameras and advances in stereoscopic vision mean that they are far more reliable nowadays and are very easy to install and adapt on most systems.
In practice, there’s no limit to the possible applications of robot navigation in our modern world. The complex algorithm in place to allow robot navigation to be a reality has allowed more automated machinery to be integrated into delivery logistics, exploration missions, automated mapping solutions, and even manufacturing. Robots provide a practical and efficient way to handle countless small tasks in most markets, and proper navigation is an essential step to ensure they are up to the task.
Simultaneous localization and mapping or SLAM refers to systems that can simultaneously identify their location in regards to their environment and then map said environment for further navigation. SLAM is one of the most important aspects of navigation as it allows autonomous systems to navigate new environments even if it’s their first time in it, and as such it’s a complex system that relies on multiple sensor inputs and complex calculations.
An autonomous mobile robot is defined as a robot that is capable of not only traversing a given area but to planning its route around it even if it’s congested and even if the layout of the area changes in between travels. In short, an autonomous mobile robot will be able to navigate a space every time even if its conditions have changed from its expected state, and can theoretically traverse any environment without external assistance.
Outdoor navigation refers to the ability of a robot to navigate towards a given goal in an exterior setting. Outdoor navigation brings with it many new challenges since it is traditionally a less controlled environment. A robot can potentially run into humans, animals, bumps, and other variables that it can’t predict ahead of time. Outdoor navigation tends to rely on GPS systems and additional sensors to avoid unexpected obstacles in real-time.
Indoor navigation refers to a robot’s ability to navigate itself in a fixed space inside another construction or building. Compared to outdoor navigation indoor systems have the advantage of a more controlled environment and the possibility of installing further aids to help the robot on its task. Indoor navigation can be supported with floorplans, pre-made maps, and external aids like painted paths and sensor systems on the building itself.
Robot navigation is an essential step in the process to make robotic systems truly autonomous and independent in day-to-day usage. While robots are capable of performing countless tasks their lack of true sensorial input has always been a challenge to perform more complex tasks like autonomous navigation and generally speaking identifying their surroundings.
To that end, robot navigation is a field that is being constantly developed and improved to make robots more autonomous with each passing day. Whether they are designed for indoor or outdoor navigation, whether it’s delivery robots or flying robots or whether they rely on LiDAR or AGV; autonomous robot navigation is an essential process to ensure the smooth integration of robotic solutions in more industries.
While it’s complicated to simplify the intricacies of robot navigation to just a given “method” the easiest way to answer this question is to consider the possible sensors and techniques a robot can employ for its navigation. AVG systems traditionally rely on magnetic tape and other physical aids to support their navigation. While more advanced machinery like the ones employed in SLAM uses various sensorial inputs like camera and LiDAR to truly understand its environment instead of just following fixed paths.
Mapping for a robot consists of two main steps, acquiring input data and then compiling it in an understandable format. To obtain said data robots can rely on multiple sensors including but not limited to lasers, cameras, and proximity scanners. The information obtained by these sensors is then portrayed in a 2D or 3D environment based on its axis coordinates to create a map. It is important to remember however that based on the complexity of each sensor some readings might not be completely accurate, and manual revision of the output maps is recommended.