  This research paper addresses the challenges of exploration and navigation in
unknown environments from an evolutionary swarm robotics perspective. Path
formation plays a crucial role in enabling cooperative swarm robots to
accomplish these tasks. The paper presents a method called the sub-goal-based
path formation, which establishes a path between two different locations by
exploiting visually connected sub-goals. Simulation experiments conducted in
the Argos simulator demonstrate the successful formation of paths in the
majority of trials.
  Furthermore, the paper tackles the problem of inter-collision (traffic) among
a large number of robots engaged in path formation, which negatively impacts
the performance of the sub-goal-based method. To mitigate this issue, a task
allocation strategy is proposed, leveraging local communication protocols and
light signal-based communication. The strategy evaluates the distance between
points and determines the required number of robots for the path formation
task, reducing unwanted exploration and traffic congestion. The performance of
the sub-goal-based path formation and task allocation strategy is evaluated by
comparing path length, time, and resource reduction against the A* algorithm.
The simulation experiments demonstrate promising results, showcasing the
scalability, robustness, and fault tolerance characteristics of the proposed
approach.
