Global path planning for multiple AUVs using GWO

• Autorzy: Panda Madhusmita , Das Bikramaditya , Pati Bibhuti

Identyfikatory

DOI

10.24425/acs.2020.132586

• Języki publikacji: EN

Abstrakty

EN

In global path planning (GPP), an autonomous underwater vehicle (AUV) tracks a predefined path. The main objective of GPP is to generate a collision free sub-optimal path with minimum path cost. The path is defined as a set of segments, passing through selected nodes known as waypoints. For smooth planar motion, the path cost is a function of the path length, the threat cost and the cost of diving. Path length is the total distance travelled from start to end point, threat cost is the penalty of collision with the obstacle and cost of diving is the energy expanse for diving deeper in ocean. This paper addresses the GPP problem for multiple AUVs in formation. Here, Grey Wolf Optimization (GWO) algorithm is used to find the suboptimal path for multiple AUVs in formation. The results obtained are compared to the results of applying Genetic Algorithm (GA) to the same problem. GA concept is simple to understand, easy to implement and supports multi-objective optimization. It is robust to local minima and have wide applications in various fields of science, engineering and commerce. Hence, GA is used for this comparative study. The performance analysis is based on computational time, length of the path generated and the total path cost. The resultant path obtained using GWO is found to be better than GA in terms of path cost and processing time. Thus, GWO is used as the GPP algorithm for three AUVs in formation. The formation follows leader-follower topography. A sliding mode controller (SMC) is developed to minimize the tracking error based on local information while maintaining formation, as mild communication exists. The stability of the sliding surface is verified by Lyapunov stability analysis. With proper path planning, the path cost can be minimized as AUVs can reach their target in less time with less energy expanses. Thus, lower path cost leads to less expensive underwater missions.

Słowa kluczowe

EN

Autonomous Underwater Vehicle; AUV; genetic algorithm; GA; Global Path Planning; GPP; Grey Wolf Optimization; GWO; Sliding Mode Control; SMC; waypoints

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2020
• Tom: Vol. 30, No. 1
• Strony: 77--100
• Opis fizyczny: Bibliogr. 21 poz., rys., tab., wykr., wzory

Twórcy

autor

Panda Madhusmita

• Department of Electronics and Telecommunication, VSSUT, Burla, Odisha, India

autor

Das Bikramaditya

• Department of Electronics and Telecommunication, VSSUT, Burla, Odisha, India

autor

Pati Bibhuti

• Department of Electrical Engineering, VSSUT, Burla, Odisha, India

Bibliografia

• [1] B. Das, B. Subudhi, and B. B. Pati: Co-operative control of a team of autonomous underwater vehicles in an obstacle-rich environment, Journal of Marine Engineering & Technology, 15(1) (2016), 135–151.
• [2] X. Kang, H. Xu, and X. Feng: Fuzzy logic based behavior fusion for multi-AUV formation keeping in uncertain ocean environment, OCEANS 2009, (2009), 1–7.
• [3] B. Das, B. Subudhi, and B. B. Pati: Adaptive sliding mode formation control of multiple underwater robots, Archives of control Sciences, 24(4) (2014), 515–543.
• [4] B. Das, B. Subudhi, and B. B. Pati: Employing nonlinear observer for formation control of AUVs under communication constraints, International Journal of Intelligent Unmanned Systems, 3(2/3) (2015), 122–155.
• [5] K. Shojaei: Neural network formation control of underactuated autonomous underwater vehicles with saturating actuators, Neurocomputing, 194 (2016), 372–384.
• [6] B. Das, B. Subudhi, and B. B. Pati: Cooperative formation control of autonomous underwater vehicles: An overview, International Journal of Automation and computing, 13(1) (2016), 199–225.
• [7] H. Cao, N. E. Brener, and S. Sitharama Iyengar: 3D large grid route planner for the autonomous underwater vehicles, International Journal of Intelligent Computing and Cybernetics, 2(1) (2009), 455–476.
• [8] M. P. Aghababa, M. H. Amrollahi, and M. Borjkhani: Application of GA, PSO, and ACO algorithms to path planning of autonomous underwater vehicles, Journal of Marine Science and Application, 11(1) (2012), 78–386.
• [9] M. P. Aghababa: 3D path planning for underwater vehicles using five evolutionary optimization algorithms avoiding static and energetic obstacles, Applied Ocean Research, 38 (2012), 48–62.
• [10] M. Ataei and A. Yousefi-Koma: Three-dimensional optimal path planning for waypoint guidance of an autonomous underwater vehicle, Robotics and Autonomous Systems, 67 (2015), 23–32.
• [11] C. Muro, R. Escobedo, L. Spector, and R. P. Coppinger: Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations, Behavioural processes, 88(1) (2011), 192–197.
• [12] S. Mirjalili, S. M. Mirjalili, and A. Lewis: Greywolf optimizer, Advances in Engineering Software, 69 (2014), 46–61.
• [13] M. Panda and B. Das: Grey Wolf Optimizer and Its Applications: A Survey, Proc. of the Third International Conference on Microelectronics, Computing and Communication Systems, (2019), 179–194.
• [14] M. Radmanesh and M. Kumar: Grey wolf optimization based sense and avoid algorithm for UAV path planning in uncertain environment using a Bayesian framework, 2016 International Conference on Unmanned Aircraft Systems (ICUAS), (2016), 68–76.
• [15] P. Yao and H. L. Wang: Three-dimensional path planning for UAV based on improved interfered fluid dynamical system and grey wolf optimizer, Control and Decision, 31(4) (2016), 701–708.
• [16] S. Zhang, Y. Zhou, Z. Li, and W. Pan: Grey wolf optimizer for unmanned combat aerial vehicle path planning, Advances in Engineering Software, 99 (2016), 121–136.
• [17] M. Panda, B. Das, and B. B. Pati: Grey wolf optimization for global path planning of autonomous underwater vehicle, Proc. of the Third International Conference on Advanced Informatics for Computing Research, (2019), 1–6.
• [18] M. Panda, B. Das, B. Subudhi, and B. B. Pati: A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles, International Journal of Automation and Computing, (2020), 1–32.
• [19] B. Das, B. Subudhi, and B. B. Pati: Co-operative control coordination of a team of underwater vehicles with communication constraints, Transactions of the Institute of Measurement and Control, 38(4) (2016), 463–481.
• [20] T. I. Fossen: Guidance and Control of Ocean Vehicles, British Library, 6–54, 1994.
• [21] Y. Cao and W. Ren: Distributed Coordinated Tracking With Reduced Interaction via a Variable Structure Approach, IEEE Trans. Automat. Contr., 57(1) (2012), 33–48.

Uwagi

EN

1. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).