Aquila Optimizer with Parallel Computation Application for Efficient Environment Exploration

Abstract

View Video Presentation: https://doi.org/10.2514/6.2023-1655.vid In the multiple fields covered by Artificial Intelligence (AI), robotic path planning is undoubtedly one of the issues that cover a wide range of research lines. This paper introduces recently developed Aquila Optimization algorithm specifically configured for Multi-Robot space exploration. The framework is a unique combination of both deterministic Coordinated Multi-robot Exploration (CME) and a swarm based Aquila Optimizer (AO), combinely known as Coordinated Multi-robot Exploration Aquila Optimizer (CME-AO). The proposed hybrid strategy also incorporates a novel parallel communication protocol, to improve multi-robot space exploration process while simultaneously minimizing both the computation complexity and time. This ensures acquisition of a optimal collision-free path in a barrier-filled environment via generating a finite map. 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. Algorithm validity and effectiveness was then validated utilizing different condition environment whose relative complexity was varied by varying parameters such as exploration space dimension and obstacle size, number and relative orientation. A perspective analysis is then performed to compare the performance of the proposed CME-AO algorithm with latest contemporary algorithms such as conventional CME and CME-WO (CME augmented Whale Optimizer). Results indicate efficacy of the proposed algorithm as it presents two distinct advantages a) enhanced map exploration in cluttered environment and b) significantly reduced computation complexity and execution time. This makes the suggested methodology particularly suitable for on-board utilization in an obstacle-cluttered environment, where other contemporary CME based techniques either fails (stuck locally) or takes longer exploration time.