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This research paper addresses the challenges of exploration and navigation inunknown environments from an evolutionary swarm robotics perspective. Pathformation plays a crucial role in enabling cooperative swarm robots toaccomplish these tasks. The paper presents a method called the sub-goal-basedpath formation, which establishes a path between two different locations byexploiting visually connected sub-goals. Simulation experiments conducted inthe Argos simulator demonstrate the successful formation of paths in themajority of trials.  Furthermore, the paper tackles the problem of inter-collision (traffic) amonga large number of robots engaged in path formation, which negatively impactsthe performance of the sub-goal-based method. To mitigate this issue, a taskallocation strategy is proposed, leveraging local communication protocols andlight signal-based communication. The strategy evaluates the distance betweenpoints and determines the required number of robots for the path formationtask, reducing unwanted exploration and traffic congestion. The performance ofthe sub-goal-based path formation and task allocation strategy is evaluated bycomparing path length, time, and resource reduction against the A* algorithm.The simulation experiments demonstrate promising results, showcasing thescalability, robustness, and fault tolerance characteristics of the proposedapproach.

Evolutionary Swarm Robotics: Dynamic Subgoal-Based Path Formation and Task Allocation for Exploration and Navigation in Unknown Environments