Design, Performance-Based Optimization (PBO), and Actual Development of Unmanned High-Speed Aerial Vehicle

Abolghasemi, Abbas; Mir, Imran; Abbas, Syed Manzar

doi:10.2514/6.2023-1626

Cited by 2 publications

(1 citation statement)

References 38 publications

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“…Multi-Agent Robot Exploration is a field of study that deals with finding optimal solutions for a group of robots exploring an unknown environment. The main challenge in this field is to coordinate the actions of multiple robots so that they can work together effectively and efficiently [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. In order to solve this problem, researchers often use optimization algorithms, which are mathematical methods that find the best solution to a given problem based on specific objectives and constraints [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Variational Approach for Robotics: A Bio-Inspired Perspective

et al. 2023

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This study proposes an adaptable, bio-inspired optimization algorithm for Multi-Agent Space Exploration. The recommended approach combines a parameterized Aquila Optimizer, a bio-inspired technology, with deterministic Multi-Agent Exploration. Stochastic factors are integrated into the Aquila Optimizer to enhance the algorithm’s efficiency. The architecture, called the Multi-Agent Exploration–Parameterized Aquila Optimizer (MAE-PAO), starts by using deterministic MAE to assess the cost and utility values of nearby cells encircling the agents. A parameterized Aquila Optimizer is then used to further increase the exploration pace. The effectiveness of the proposed MAE-PAO methodology is verified through extended simulations in various environmental conditions. The algorithm viability is further evaluated by comparing the results with those of the contemporary CME-Aquila Optimizer (CME-AO) and the Whale Optimizer. The comparison adequately considers various performance parameters, such as the percentage of the map explored, the number of unsuccessful runs, and the time needed to explore the map. The comparisons are performed on numerous maps simulating different scenarios. A detailed statistical analysis is performed to check the efficacy of the algorithm. We conclude that the proposed algorithm’s average rate of exploration does not deviate much compared to contemporary algorithms. The same idea is checked for exploration time. Thus, we conclude that the results obtained for the proposed MAE-PAO algorithm provide significant advantages in terms of enhanced map exploration with lower execution times and nearly no failed runs.

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