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Aquila Optimizer with parallel computing strategy for efficient environment exploration

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Abstract

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.

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References

  • Abela J (1991) A parallel genetic algorithm for solving the school timetabling problem. In: Division of Information Technology, CSIRO, Citeseer

  • Abualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609

    MathSciNet  MATH  Google Scholar 

  • Abualigah L, Yousri D, Abd Elaziz M, Ewees AA, Al-Qaness MA, Gandomi AH (2021) Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput Ind Eng 157:107250

    Google Scholar 

  • Abualigah L, Abd Elaziz M, Sumari P, Geem ZW, Gandomi AH (2022) Reptile search algorithm (rsa): a nature-inspired meta-heuristic optimizer. Expert Syst Appl 191:116158

    Google Scholar 

  • Ali AA, Rashid AT, Frasca M, Fortuna L (2016) An algorithm for multi-robot collision-free navigation based on shortest distance. Robot Auton Syst 75:119–128

    Google Scholar 

  • Almadhoun R, Taha T, Seneviratne L, Zweiri Y (2019) A survey on multi-robot coverage path planning for model reconstruction and mapping. SN Appl Sci 1(8):1–24

    Google Scholar 

  • Amador-Angulo L, Castillo O, Peraza C, Ochoa P (2021) An efficient chicken search optimization algorithm for the optimal design of fuzzy controllers. Axioms 10(1):30

    Google Scholar 

  • Amador-Angulo L, Castillo O (2021) Optimization of fuzzy trajectory tracking in autonomous mobile robots based on bio-inspired algorithms. In: Recent advances of hybrid intelligent systems based on soft computing, Springer, pp 249–271

  • Batinović A, Oršulić J, Petrović T, Bogdan S (2020) Decentralized strategy for cooperative multi-robot exploration and mapping. IFAC-PapersOnLine 53(2):9682–9687

    Google Scholar 

  • Burgard W, Moors M, Stachniss C, Schneider FE (2005) Coordinated multi-robot exploration. IEEE Trans Rob 21(3):376–386

    Google Scholar 

  • Chai Q, Chu SC, Pan JS, Hu P, Zheng W (2020) A parallel woa with two communication strategies applied in dv-hop localization method. EURASIP J Wirel Commun Netw 2020(1):1–10

    Google Scholar 

  • Chu SC, Roddick JF, Pan JS (2005) A parallel particle swarm optimization algorithm with communication strategies. J Inf Sci Eng 21(4):9

    Google Scholar 

  • Das PK (2020) Hybridization of kidney-inspired and sine-cosine algorithm for multi-robot path planning. Arab J Sci Eng 45(4):2883–2900

    Google Scholar 

  • Das P, Behera H, Panigrahi B (2016) Intelligent-based multi-robot path planning inspired by improved classical q-learning and improved particle swarm optimization with perturbed velocity. Eng Sci Technol Int J 19(1):651–669

    Google Scholar 

  • Das PK, Behera HS, Panigrahi BK (2016) A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning. Swarm Evol Comput 28:14–28

    Google Scholar 

  • Din AFU, Akhtar S, Maqsood A, Habib M, Mir I (2022a) Modified model free dynamic programming: an augmented approach for unmanned aerial vehicle. Appl Intell 1:1–21

    Google Scholar 

  • Din AFU, Mir I, Gul F, Mir S, Alhady SSN, Nasar A, Rustom M, Alkhazaleh HA, Abualigah L (2022b) Robust flight control system design of a fixed wing UAV using optimal dynamic programming. Soft Comput 1:1–21

  • Din AFU, Mir I, Gul F, Nasar A, Rustom M, Abualigah L (2022c) Reinforced learning-based robust control design for unmanned aerial vehicle. Arab J Sci Eng 1:1–16

    Google Scholar 

  • Duleba I, Sasiadek JZ (2003) Nonholonomic motion planning based on newton algorithm with energy optimization. IEEE Trans Control Syst Technol 11(3):355–363

    Google Scholar 

  • Fatima SK, Abbas M, Mir I, Gul F, Mir S, Saeed N, Alotaibi AA, Althobaiti T, Abualigah L (2022) Data driven model estimation for aerial vehicles: a perspective analysis. Processes 10(7):1236

    Google Scholar 

  • Gan W, Lin JCW, Fournier-Viger P, Chao HC, Yu PS (2019) A survey of parallel sequential pattern mining. ACM Trans Knowl Discovery Data (TKDD) 13(3):1–34

    Google Scholar 

  • Gerke M, Hoyer H (1997) Planning of optimal paths for autonomous agents moving in inhomogeneous environments. In: 1997 8th International Conference on Advanced Robotics. Proceedings. ICAR’97, IEEE, pp 347–352

  • Gul F, Rahiman W, Nazli Alhady SS (2019) A comprehensive study for robot navigation techniques. Cogent Eng 6(1):1632046

    Google Scholar 

  • Gul F, Mir I, Abualigah L, Sumari P (2021) Multi-robot space exploration: An augmented arithmetic approach. IEEE Access 9:107738–107750

    Google Scholar 

  • Gul F, Mir I, Abualigah L, Sumari P, Forestiero A (2021) A consolidated review of path planning and optimization techniques: technical perspectives and future directions. Electronics 10(18):2250

    Google Scholar 

  • Gul F, Mir I, Rahiman W, Islam TU (2021) Novel implementation of multi-robot space exploration utilizing coordinated multi-robot exploration and frequency modified whale optimization algorithm. IEEE Access 9:22774–22787

    Google Scholar 

  • Gul F, Mir I, Abualigah L, Mir S, Altalhi M (2022) Cooperative multi-function approach: a new strategy for autonomous ground robotics. Futur Gener Comput Syst 134:361–373

    Google Scholar 

  • Gul F, Mir I, Alarabiat D, Alabool HM, Abualigah L, Mir S (2022) Implementation of bio-inspired hybrid algorithm with mutation operator for robotic path planning. J Parallel Distrib Comput 169:171–184

    Google Scholar 

  • Gul F, Mir S, Mir I (2022d) Coordinated multi-robot exploration: Hybrid stochastic optimization approach. In: AIAA SCITECH 2022 Forum, p 1414

  • Gul F, Mir S, Mir I (2022e) Multi robot space exploration: A modified frequency whale optimization approach. In: AIAA SCITECH 2022 Forum, p 1416

  • Gul F, Mir A, Mir I, Mir S, Islaam TU, Abualigah L, Forestiero A (2022) A centralized strategy for multi-agent exploration. IEEE Access 10:126871–126884

  • Gul F, Rahiman W (2019) An integrated approach for path planning for mobile robot using bi-rrt. In: IOP conference series: materials science and engineering, IOP Publishing, vol 697, p 012022

  • Gul F, Rahiman W (2022) Mathematical modeling of self balancing robot and hardware implementation. In: Proceedings of the 11th International Conference on Robotics, Vision, Signal Processing and Power Applications, Springer, pp 20–26

  • Gul F, Rahiman W, Alhady SSN, Ali A, Mir I, Jalil A (2021) Meta-heuristic approach for solving multi-objective path planning for autonomous guided robot using PSO–GWO optimization algorithm with evolutionary programming. J Ambient Intell Humanized Comput 12:7873–7890

  • Ibraheem GAR, Azar AT, Ibraheem IK, Humaidi AJ (2020) A novel design of a neural network-based fractional PID controller for mobile robots using hybridized fruit fly and particle swarm optimization. Complexity 2020:1–18

  • Kala R (2012) Multi-robot path planning using co-evolutionary genetic programming. Expert Syst Appl 39(3):3817–3831

    Google Scholar 

  • Kudriashov A, Buratowski T, Garus J, Giergiel M (2021) 3d environment exploration with slam for autonomous mobile robot control. Sensors 3:16

    Google Scholar 

  • Liu X, Gong D (2011) A comparative study of a-star algorithms for search and rescue in perfect maze. In: 2011 International Conference on Electric Information and Control Engineering, IEEE, pp 24–27

  • Madridano Á, Al-Kaff A, Martín D, de la Escalera A (2021) Trajectory planning for multi-robot systems: methods and applications. Expert Syst Appl 173:114660

    Google Scholar 

  • Manchester Z, Peck M (2011) Stochastic space exploration with microscale spacecraft. In: AIAA Guidance, Navigation, and Control Conference, p 6648

  • Mathew N, Smith SL, Waslander SL (2015) Optimal path planning in cooperative heterogeneous multi-robot delivery systems. In: Algorithmic Foundations of Robotics XI, Springer, pp 407–423

  • Mir I, Eisa SA, Maqsood A (2018) Review of dynamic soaring: technical aspects, nonlinear modeling perspectives and future directions. Nonlinear Dyn 94(4):3117–3144

    Google Scholar 

  • Mir I, Maqsood A, Akhtar S (2018) Biologically inspired dynamic soaring maneuvers for an unmanned air vehicle capable of sweep morphing. Int J Aeronaut and Space Sci 19(4):1006–1016

    Google Scholar 

  • Mir I, Maqsood A, Eisa SA, Taha H, Akhtar S (2018) Optimal morphing-augmented dynamic soaring maneuvers for unmanned air vehicle capable of span and sweep morphologies. Aerosp Sci Technol 79:17–36

    Google Scholar 

  • Mir I, Taha H, Eisa SA, Maqsood A (2018) A controllability perspective of dynamic soaring. Nonlinear Dyn 94(4):2347–2362

    MATH  Google Scholar 

  • Mir I, Akhtar S, Eisa S, Maqsood A (2019) Guidance and control of standoff air-to-surface carrier vehicle. Aeronaut J 123(1261):283–309

    Google Scholar 

  • Mir I, Eisa SA, Taha H, Maqsood A, Akhtar S, Islam TU (2021) A stability perspective of bioinspired unmanned aerial vehicles performing optimal dynamic soaring. Bioinspir Biomim 16(6):066010

    Google Scholar 

  • Mir I, Gul F, Mir S, Khan MA, Saeed N, Abualigah L, Abuhaija B, Gandomi AH (2022) A survey of trajectory planning techniques for autonomous systems. Electronics 11(18):2801

    Google Scholar 

  • Mir I, Gul F, Mir S, Saeed N, Althobaiti T, Abbas SM, Abualigah L et al (2022) Deep reinforcement learning for integrated non-linear control of autonomous uavs. Processes 10(7):1307

    Google Scholar 

  • Mir I, Eisa S, Maqsood A, Gul F (2022a) Contraction analysis of dynamic soaring. In: AIAA SCITECH 2022 Forum, p 0881

  • Mir I, Eisa S, Taha HE, Gul F (2022b) On the stability of dynamic soaring: floquet-based investigation. In: AIAA SCITECH 2022 Forum, p 0882

  • Mir I, Gul F, Mir S, Khan MA, Saeed N, Abualigah L, Abuhaija B, Gandomi AH (2022d) A survey of trajectory planning techniques for autonomous systems. Electronics 11(18), https://doi.org/10.3390/electronics11182801, https://www.mdpi.com/2079-9292/11/18/2801

  • Mir I, Maqsood A, Akhtar S (2017) Optimization of dynamic soaring maneuvers to enhance endurance of a versatile UAV. In: IOP conference series: materials science and engineering, vol 211, no. 1. IOP Publishing, p 012010

  • Mir I, Maqsood A, Akhtar S (2017a) Dynamic modeling and stability analysis of a generic UAV in glide phase. In: MATEC Web of Conferences, EDP Sciences, vol 114, p 01007

  • Mir I, Maqsood A, Akhtar S (2017b) Optimization of dynamic soaring maneuvers for a morphing capable UAV. In: AIAA Information Systems-AIAA Infotech@ Aerospace, p 0678

  • Mir I, Maqsood A, Taha HE, Eisa SA (2019b) Soaring energetics for a nature inspired unmanned aerial vehicle. In: AIAA Scitech 2019 Forum, p 1622

  • Moll M, Kavraki LE (2004) Path planning for minimal energy curves of constant length. In: IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA’04. 2004, IEEE, vol 3, pp 2826–2831

  • Nasrabadi MS, Sharafi Y, Tayari M (2016) A parallel grey wolf optimizer combined with opposition based learning. In: 2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC), IEEE, pp 18–23

  • Oleiwi BK, Roth H, Kazem BI (2014) A hybrid approach based on aco and ga for multi objective mobile robot path planning. App Mech Mater Trans Tech Publ 527:203–212

    Google Scholar 

  • Pumma S, Si M, Feng Wc, Balaji P (2017) Parallel i/o optimizations for scalable deep learning. In: 2017 IEEE 23rd International Conference on Parallel and Distributed Systems (ICPADS), IEEE, pp 720–729

  • Senarathne P, Wang D (2015) Incremental algorithms for safe and reachable frontier detection for robot exploration. Robot Auton Syst 72:189–206

    Google Scholar 

  • Shin KG, Zheng Q (1992) Minimum-time collision-free trajectory planning for dual-robot systems. IEEE Trans Robot Autom 8(5):641–644

    Google Scholar 

  • Szczepanski R, Tarczewski T, Grzesiak LM (2019) Adaptive state feedback speed controller for pmsm based on artificial bee colony algorithm. Appl Soft Comput 83:105644

    Google Scholar 

  • Tsai PW, Pan JS, Chen SM, Liao BY, Hao SP (2008) Parallel cat swarm optimization. In: 2008 international conference on machine learning and cybernetics, IEEE, vol 6, pp 3328–3333

  • Wagner G, Choset H (2015) Subdimensional expansion for multirobot path planning. Artif Intell 219:1–24

    MathSciNet  MATH  Google Scholar 

  • Xiao J, Michalewicz Z, Zhang L, Trojanowski K (1997) Adaptive evolutionary planner/navigator for mobile robots. IEEE Trans Evol Comput 1(1):18–28

    Google Scholar 

  • Yamauchi B (1997) A frontier-based approach for autonomous exploration. In: Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA’97.’Towards New Computational Principles for Robotics and Automation’, IEEE, pp 146–151

  • Yang Q, Chu SC, Pan JS, Chen CM (2020) Sine cosine algorithm with multigroup and multistrategy for solving CVRP. Math Prob Eng 2020:1–10

    Google Scholar 

  • Yu J, LaValle SM (2016) Optimal multirobot path planning on graphs: complete algorithms and effective heuristics. IEEE Trans Rob 32(5):1163–1177

    Google Scholar 

  • Zhang YJ, Yan YX, Zhao J, Gao ZM (2022) Aoaao: the hybrid algorithm of arithmetic optimization algorithm with aquila optimizer. IEEE Access 10:10907–10933

    Google Scholar 

  • Zhao J, Gao ZM, Chen HF (2022) The simplified aquila optimization algorithm. IEEE Access 10:22487–22515

    Google Scholar 

  • Zhu M, Chu SC, Yang Q, Li W, Pan JS (2021) Compact sine cosine algorithm with multigroup and multistrategy for dispatching system of public transit vehicles. J Adv Transp 2021:1–16

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Authors and Affiliations

  1. Department of Electrical Engineering, Air University, Aerospace and Aviation, Campus Kamra, Islamabad, Pakistan

    Faiza Gul

  2. School of Avionics and Electrical Engineering, College of Aeronautical Engineering, NUST, Risalpur, Pakistan

    Imran Mir

  3. Department of Avionics Engineering, Air University, Aerospace and Aviation, Campus Kamra, Islamabad, Pakistan

    Imran Mir

  4. Electrical Department, Fast-National University of Computer and Emerging Sciences, Peshawar, Pakistan

    Suleman Mir

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  1. Faiza Gul
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  2. Imran Mir
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Correspondence to Imran Mir.

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Gul, F., Mir, I. & Mir, S. Aquila Optimizer with parallel computing strategy for efficient environment exploration. J Ambient Intell Human Comput 14, 4175–4190 (2023). https://doi.org/10.1007/s12652-023-04515-x

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