This paper presents a heterogeneous viscoelastic cyber-physical swarm exploration algorithm for navigating robot groups in unknown, obstacle-filled environments. The framework integrates physical agents aware of a target coordinate with a surrounding layer of cyber agents that process environmental measurements and prevent collisions through viscoelastic interactions. Built on a structured, stable, and robust formulation, the method guarantees asymptotic stability of the collective motion and robustness to bounded perturbations. A hybrid control strategy enables agents to switch between normal operation and stagnation-recovery modes, helping the swarm avoid deadlock, maintain cohesion, and pass through narrow spaces without prior map knowledge. Monte Carlo simulations show that the algorithm consistently achieves alignment, stable cohesion, and collision-free exploration under sensing noise, varied swarm compositions, and different target coordinates in an unknown maze. The results demonstrate that the cyber-physical viscoelastic architecture provides adaptability and resilience, offering a scalable and analytically validated approach to autonomous exploration.