Autonomous Mapping

This work presents the design of an autonomous robot capable of exploring an unknown environment, continuously building a navigable map, and moving toward newly discovered targets using Breadth-First Search (BFS). The system integrates laser perception, dynamic map construction, reactive navigation, and automatic collision avoidance.

The goal is to achieve fully independent behavior where the robot identifies traversable areas, evaluates safety, generates local goals, and travels to them while actively avoiding nearby obstacles.

General Goal

The robot operates without predefined waypoints or prior maps.
Its mission is to:

Explore the environment while mapping obstacles in real time.
Identify safe regions distant from the robot and navigate toward them.
Avoid collisions and redirect its route when obstacles are detected.
Update the map and goal positions throughout the process.

This enables free exploration and progressive expansion of the mapped environment with no supervision.

Map Generation

The map is represented as an intensity grid:

Value	Meaning
`0`	obstacle detected
`>0`	free space, brighter = more frequently observed

The laser sensor reconstructs the environment through a full angular sweep:

Each ray advances in discrete steps, increasing intensity on free cells.
If an obstacle is detected, that cell is penalized by lowering intensity.
The map is updated only when the robot has moved sufficiently from its last recorded position, avoiding unnecessary recalculation of already explored areas.
The map is published to WebGUI as a user map viewable in real time.

The result is a continuous map where frequently observed areas appear brighter, and collision zones appear darker. The intensity updates use a logistic model to prevent saturation and allow gradual reinforcement of free space.

Intelligent Goal Selection

The navigation engine runs a radial BFS from the robot’s current position.
It searches for a cell that meets:

✔ safe minimum distance from the robot
✔ free region surrounding the target area
✔ unobstructed path from origin to goal

BFS returns two coordinates:

a mid target for smoother trajectory
a final target that optimally expands the exploration frontier

The robot moves toward the mid target first, then proceeds to the final target, gradually increasing the mapped region.

Motion Control

Movement relies on reactive orientation:

The relative angle between robot and target is computed.
If the angular error is high → rotation only.
If alignment is achieved → forward velocity is applied.

When half the route is completed, the goal automatically switches to the final target.
If the target area becomes unsafe, it is discarded and BFS selects a new one.

Active Obstacle Avoidance

A front-safety module ensures collision prevention:

If the laser detects a close obstacle within a narrow angular window, navigation is suspended and avoidance mode starts:

Stage	Action
phase 1 ~ rotation	in-place turn to open space
phase 2 ~ short forward motion	lateral escape from obstacle
phase 3	return to navigation mode

This sequence improves stability and provides fast reactions in confined areas. The duration of each phase is parametrizable to adapt to different environments.

Visualization

The interface displays:

the full map updated live
robot and targets highlighted as markers
exploration trail and processed goal points

This makes it easy to monitor exploration and understand how BFS drives spatial expansion.

Video

SLAM INTENSITY

SLAM PROBABILISTIC INTENSITY

Conclusion

This robot establishes a robust foundation for autonomous exploration and surveillance in unknown environments.
Its achieved capabilities include:

incremental laser-based mapping with continuous reconstruction
autonomous BFS-based target generation
reactive motion driven by angular error
immediate avoidance of close obstacles

The system is independent of prior maps or predefined routes, making it light, scalable, and suitable for real-world exploration and search applications.