Aquila Optimizer with parallel computing strategy for efficient environment exploration

This paper introduces Aquila Optimization Algorithm specifically configured for Multi-Robot space exploration that can be utilized for a wide range of operations. The proposed strategy incorporates a novel parallel communication protocol, to improve multi-robot space exploration process while minimizing the computation complexity. This ensures acquisition of a collision-free optimal motion in a barrier-filled environment via generating a finite explored map. The framework is a unique combination of both deterministic Coordinated Multi-robot Exploration (CME) and a swarm based methodology, known as Aquila Optimizer (AO). Combinely known as Coordinated Multi-robot Exploration Aquila Optimizer (CME-AO). The architecture starts by determining the cost and utility values of neighbouring cells around the robot using deterministic CME. Aquila Optimization technique is then incorporated to increase the overall solution accuracy. Numerous simulations under different environmental conditions were then performed to validate the effectiveness of the proposed CME-AO algorithm. A perspective analysis was then performed by comparing the performance of the CME-AO algorithm with latest contemporary algorithms namely conventional CME, CME Arithmetic Optimization Algorithm (CME-AOA) and Frequency Modified Hybrid-whale Optimization Algorithm (FMH-WOA). The comparison duly accommodates all pertinent aspects such as % area explored, number of failed runs, and time taken for map exploration for different environments. A statistical comparison with both CME and CME-AOA is then carried out by performing multiple simulations under different environmental configurations. The mean and standard deviation of the (%) area explored and total time taken are then calculated. Results indicate that the proposed algorithm presents distinct advantages of enhanced map exploration in a considerably lesser execution time with almost no fail runs.