Navigation in mobile robotics is the technology that enables robots to autonomously move from one point to another, both indoors and outdoors. This process involves knowing their current position, building a map of the environment, planning the route to follow, and controlling their movements to reach the desired destination while performing the task they were designed for. This article offers a general overview of autonomous navigation and mobile robotics, as well as some key technological insights into mapping and localization, all based on Robotnik's more than 20 years of experience as a manufacturer of autonomous mobile robots.

One of the main differences between autonomous mobile robots (AMRs) and traditional AGVs is that the former do not require physical references such as magnetic tapes or beacons for guidance. Therefore, AMR navigation relies on advanced technological systems that ensure intelligent and safe movement.

autonomous navigation is the ability of a mobile robot to independently move from a starting point to a predetermined destination without direct human intervention along the way. This process involves several key stages:

- Mapping: The robot creates or uses a map to understand the physical space and plan routes.
Localization: After modeling the environment, the robot must know its exact position within it at all times.

- Traditionally, mapping and localization were performed sequentially, but nowadays, some manufacturers like Robotnik incorporate Simultaneous Localization and Mapping (SLAM) technology, which allows the robot to map the environment while simultaneously estimating its position.

- Path planning: Based on the map and its current position, the robot calculates the best trajectory in each case to reach its destination. Route planning becomes smarter as technologies like Machine - Learning or other types of Artificial Intelligence are integrated, enabling more up-to-date decision-making based on continuous learning patterns.
- Movement and obstacle detection: The robot executes the calculated route by controlling speed, direction, and maneuvers to reach the desired point, while detecting and avoiding possible static or dynamic obstacles.

These tasks require a sophisticated combination of hardware and software, including high-precision sensors, advanced algorithms, and robust control systems.

🟥KEY TECHNOLOGIES IN AUTONOMOUS NAVIGATION🟥

SLAM (Simultaneous Localization and Mapping) and LiDAR (Light Detection and Ranging) are fundamental technologies in the development of autonomous navigation systems. SLAM allows a robot or autonomous vehicle to build a map of the environment while determining its own location within it — all in real time.

A LiDAR sensor emits light pulses to generate a point cloud of the environment, creating detailed (2D or 3D) representations of the surroundings. Combined, these technologies enable highly accurate and reliable spatial perception. Below is a closer look at each:

🔺SLAM (SIMULTANEOUS LOCALIZATION AND MAPPING)
SLAM technology enables localization and mapping simultaneously. In other words, the robot not only maps its environment but also uses that map to determine its exact location in real time. This method is especially useful in unknown or changing environments where no pre-existing maps are available.

In robotics, 2D SLAM is commonly used, which builds maps using 2D LiDAR sensors to represent the environment in a two-dimensional space (x, y). However, Robotnik robots now include a 3D SLAM module based on ROS that allows robots equipped with 3D LiDARs to map and localize in three dimensions. Unlike 2D SLAM, which only captures data at the sensor's height, 3D SLAM provides a complete representation of the environment, including structures at different heights. This significantly improves localization accuracy, robustness to environmental changes, and trajectory planning efficiency — thanks to the greater amount of data and the 3D sensors' ability to avoid occlusions.

🔺HOW DOES SLAM WORK?
SLAM acts like the robot's eyes. It detects and collects data and information from the environment using LiDAR sensors, cameras, or IMUs (Inertial Measurement Units). This data is processed to:

- Detect features in the environment (walls, doors, furniture, obstacles, etc.)
- Create a real-time digital map based on that data
- Compare new readings with the generated map to estimate the robot's position

🔺TYPES OF SLAM
In robotics, SLAM technology can be mainly classified into two types: visual SLAM and LiDAR-based SLAM.

- Visual SLAM (vSLAM): Visual SLAM uses cameras instead of lasers to capture visual features and build maps from them. It can be monocular (single camera), stereo (two cameras), or RGB-D (depth camera). This technique is often more cost-effective, although it may have limitations in environments with variable lighting.
- LiDAR SLAM: Uses LiDAR sensors to generate detailed maps through laser pulses. LiDAR SLAM is highly accurate and less affected by lighting conditions, making it effective in low-light or completely dark environments.