Abstract
This paper highlights a new approach to generate an optimal collision-free trajectory path for each robot in a cluttered and unknown workspace using enhanced particle swarm optimization (IPSO) with sine and cosine algorithms (SCAs). In the current work, PSO has enhanced with the notion of democratic rule in human society and greedy strategy for selecting the optimal position in the successive iteration using sine and cosine algorithms. The projected algorithm mainly emphasizes to produce a deadlock-free successive location of every robot from their current location, preserve a good equilibrium between diversification and intensification, and minimize the path distance for each robot. Results achieved from IPSO–SCA have equated with those developed by IPSO and DE in the same workspace to authenticate the efficiency and robustness of the suggested approach. The outcomes of the simulation and real platform result reveal that IPSO–SCA is superior to IPSO and DE in the form of producing an optimal collision-free path, arrival time, and energy utilization during travel.
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References
Kamil, F.; et al.: A review on motion planning and obstacle avoidance approaches in dynamic environments. Adv. Robot. Autom. 4(2), 134–142 (2015)
Qu, Y.; et al.: Analyzing crowd dynamic characteristics of boarding and alighting process in urban metro stations. Phys. A 526, 121075 (2019)
Saicharan, B.; Tiwari, R.; Roberts, N.: Multi Objective optimization based Path Planning in robotics using nature inspired algorithms: a survey. In: 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES). IEEE (2016)
Das, P.K.; Behera, H.S.; Das, S.; Tripathy, H.K.; Panigrahi, B.K.; Pradhan, S.K.: A hybrid improved PSO-DV algorithm for multi-robot path planning in a clutter environment. Neurocomputing 207, 735–753 (2016)
Galceran, Enric; Carreras, Marc: A survey on coverage path planning for robotics. Robotics and Autonomous systems 61(12), 1258–1276 (2013)
Zhang, Yong; Gong, Dun-Wei; Zhang, Jian-Hua: Robot path planning in uncertain environment using multi-objective particle swarm optimization. Neurocomputing 103, 172–185 (2013)
Kumar, P.B.; Sahu, C.; Parhi, D.R.: A hybridized regression-adaptive ant colony optimization approach for navigation of humanoids in a cluttered environment. Appl. Soft Comput. 68, 565–585 (2018)
Singh, N.H.; Thongam, K.: Neural network-based approaches for mobile robot navigation in static and moving obstacles environments. Intell. Serv. Robot. 12(1), 55–67 (2019)
Tu, E.; et al.: Exploiting AIS data for intelligent maritime navigation: a comprehensive survey from data to methodology. IEEE Trans. Intell. Transp. Syst. 19(5), 1559–1582 (2017)
Das, P.K.; Behera, H.S.; Jena, P.K.; Panigrahi, B.K.: Multi-robot path planning in a dynamic environment using improved gravitational search algorithm. J. Electr. Syst. Inf. Technol. 3(2), 295–313 (2016)
Das, P.K.; Behera, H.S.; Panigrahi, B.K.: A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning. Swarm Evol. Comput. 28, 14–28 (2016)
Song, Baoye; Wang, Zidong; Zou, Lei: On global smooth path planning for mobile robots using a novel multimodal delayed PSO algorithm. Cogn. Comput. 9(1), 5–17 (2017)
Purcaru, C.; et al.: Hybrid PSO-GSA robot path planning algorithm in static environments with danger zones. In: 2013 17th International Conference on System Theory, Control and Computing (ICSTCC). IEEE (2013)
Shahnazar, A.; et al.: A new developed approach for the prediction of ground vibration using a hybrid PSO-optimized ANFIS-based model. Environ. Earth Sci. 76(15), 527 (2017)
Tanweer, M.R.; Suresh, S.; Sundararajan, N.: Self regulating particle swarm optimization algorithm. Inf. Sci. 294, 182–202 (2015)
Shukla, S.; Shukla, N.K.; Sachan, V.K.: Multi Robot Path Planning Parameter Analysis Based on Particle Swarm Optimization (PSO) in an Intricate Unknown Environments. In: 2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), vol. 1. IEEE (2019)
Nazarahari, Milad; Khanmirza, Esmaeel; Doostie, Samira: Multi-objective multi-robot path planning in continuous environment using an enhanced genetic algorithm. Expert Syst. Appl. 115, 106–120 (2019)
Das, P.K.; Behera, H.S.; Panigrahi, B.K.: A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning. Swarm Evol. Comput. 1(28), 14–28 (2016)
Wang, X.; Shi, Y.; Ding, D.; Gu, X.: Double global optimum genetic algorithm–particle swarm optimization-based welding robot path planning. Eng. Optim. 48(2), 299–316 (2016)
Das, P.K.; Behera, H.S.; Panigrahi, B.K.: Intelligent-based multi-robot path planning inspired by improved classical Q-learning and improved particle swarm optimization with perturbed velocity. Int. J. Eng. Sci. Technol. 19(1), 651–669 (2016)
Prajapati, A.; Chhabra, J.K.: A particle swarm optimization-based heuristic for software module clustering problem. Arab. J. Sci. Eng. 43(12), 7083–7094 (2018)
Burman, R.; Chakrabarti, S.; Das, S.: Democracy-inspired particle swarm optimizer with the concept of peer groups. Soft. Comput. 21(12), 3267–3286 (2017)
Moslah, O.; Hachaïchi, Y.; Lahbib, Y.: Democratic inspired particle swarm optimization for multi-robot exploration task (2016)
Das, P.K.; Jena, P.K.: Multi-robot path planning using improved particle swarm optimization algorithm through novel evolutionary operators. Appl. Soft Comput. 2020, 106312 (2020)
Zhan, Z.; Zhang, J.; Li, Y.; Chung, H.S.: Adaptive particle swarm optimization. IEEE Trans. Syst. Man Cybern. Part B 39(6), 1362–1381 (2009)
Ratnaweera, A.; Halgamuge, S.K.: Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficient. IEEE Trans. Evol. Comput. 83, 240–255 (2004)
Kundu, R.; Mukherjee, R.; Das, S.: Modified particle swarm optimization with switching update strategy. In: Proceedings of the International Conference on Swarm, Evolutionary, and Memetic Computing. Springer, Berlin, Heidelberg, pp. 644–652 (2012)
Del Valle, Y.; et al.: Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans. Evol. Comput. 12(2), 171–195 (2008)
Mahmoodabadi, M.J.; Mottaghi, Z.S.; Bagheri, A.: HEPSO: high exploration particle swarm optimization. Inf. Sci. 273, 101–111 (2014)
Liang, J.J.; et al.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10(3), 281–295 (2006)
Shi, Yuhui; Eberhart, Russell C.: Parameter selection in particle swarm optimization. International Conference on Evolutionary Programming. Springer Berlin Heidelberg, Berlin (1998)
Mirjalili, S.: SCA: a sine cosine algorithm for solving optimization problems. Knowl. Based Syst. 15(96), 120–133 (2016)
Issa, M.; Hassanien, A.E.; Oliva, D.; Helmi, A.; Ziedan, I.; Alzohairy, A.: ASCA-PSO: adaptive sine cosine optimization algorithm integrated with particle swarm for pairwise local sequence alignment. Expert Syst. Appl. 99, 56–70 (2018)
Zhang, Jinhua; Zhuang, Jian; Haifeng, Du: Self-organizing genetic algorithm based tuning of PID controllers. Inf. Sci. 179(7), 1007–1018 (2009)
Fakhouri, H.N.; Hudaib, A.; Sleit, A.: Hybrid particle swarm optimization with sine cosine algorithm and Nelder-Mead simplex for solving engineering design problems. Arab. J. Sci. Eng. 2020, 1–19 (2020)
Alitappeh, R.J.; Jeddisaravi, K.; Guimarães, F.G.: Multi-objective multi-robot deployment in a dynamic environment. Soft. Comput. 21(21), 6481–6497 (2017)
Yu, J.; Rus, D.: An effective algorithmic framework for near optimal multi-robot path planning. In: Robotics Research, pp. 495–511. Springer, Cham (2018)
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Paikray, H.K., Das, P.K. & Panda, S. Optimal Multi-robot Path Planning Using Particle Swarm Optimization Algorithm Improved by Sine and Cosine Algorithms. Arab J Sci Eng 46, 3357–3381 (2021). https://doi.org/10.1007/s13369-020-05046-9
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DOI: https://doi.org/10.1007/s13369-020-05046-9