Efficient multi-robot exploration using deterministic and metaheuristic methods

Autonomous exploration is a critical challenge in the use of robots across many applications. Nonetheless, current methodologies exhibit inadequate exploration rates, principally attributable to limited global coverage and infrequent decision-making. To mitigate these restrictions, we offer EMREL (Efficient Multi-Robot Exploration), a hybrid optimization method intended to enable rapid multi-robot exploration in intricate, unfamiliar environments. Our methodology implements the deterministic Coordinated Multi-Robot Exploration (CME) formulation, with cost and utility values organized into a Frontier Information Structure (FIS) shared across all robots. The deterministic cost and utility initially direct the prioritization of neighboring cells surrounding each robot. Subsequently, the stochastic optimizer (Crayfish Optimizer) enhances search space by improving overall solutions. This suggests that robots first evaluate their environment using a deterministic method before moving forward under the control of a stochastic algorithm. We evaluated the suggested hybrid technique on both ordinary and complicated maps and compared it with contemporary algorithms. The simulations show that the proposed method outperforms the contemporary algorithms in exploration rate, temporal efficiency, and number of failed simulation runs. These research results demonstrate the potential of EMREL as a versatile, superior, and efficient approach for robotic exploration, establishing a framework for potential developments in multi-robot system design.