  Recent advancements in robotics have paved the way for robots to replace
humans in perilous situations, such as searching for victims in blazing
buildings, earthquake-damaged structures, uncharted caves, traversing
minefields, or patrolling crime-ridden streets. These challenges can be
generalized as problems where agents need to explore unknown mazes. Although
various algorithms for single-agent maze exploration exist, extending them to
multi-agent systems poses complexities.
  We propose a solution: a cooperative multi-agent system of automated mobile
agents for exploring unknown mazes and locating stationary targets. Our
algorithm employs a potential field governing maze exploration, integrating
cooperative agent behaviors like collision avoidance, coverage coordination,
and path planning.
  This approach builds upon the Heat Equation Driven Area Coverage (HEDAC)
method by Ivi\'c, Crnkovi\'c, and Mezi\'c. Unlike previous continuous domain
applications, we adapt HEDAC for discrete domains, specifically mazes divided
into nodes. Our algorithm is versatile, easily modified for anti-collision
requirements, and adaptable to expanding mazes and numerical meshes over time.
  Comparative evaluations against alternative maze-solving methods illustrate
our algorithm's superiority. The results highlight significant enhancements,
showcasing its applicability across diverse mazes. Numerical simulations affirm
its robustness, adaptability, scalability, and simplicity, enabling centralized
parallel computation in autonomous systems of basic agents/robots.

Robotics;Multiagent Systems;Optimization and Control