The object-oriented dynamic task assignment for unmanned surface vessels

Abstract This paper investigates the task assignment and guidance issues of unmanned surface vessels (USVs) interception. When the USVs formation is invaded by some moving objects during its escort, it is necessary for the unmanned systems to assign defenders to prevent attackers approaching the vulnerable target in antagonistic scenarios. This action requires efficient guidance and task assignment strategies. With this in mind, this paper presents the Integral Proportional Navigation Guidance (IPNG) with Tabu Dynamic Consensus-Based Auction Algorithm (TDCBAA) in marine interception scenario. First, IPNG is introduced in the interception game considering the USV kinematic model, which can effectively reduce the individual interception time. Second, a new bidding function is designed for moving objects interception with the consideration of the attackers’ types, positions and interception time. Finally, a TDCBAA is designed to solve the task assignment subproblem, resulting in a shorter overall interception time and a higher interception success rate. Simulations demonstrate that the proposed algorithm can optimize the allocation of defenders in real-time and intercept the attackers more quickly compared with other classical algorithms, which is more suitable in situations where attackers are approaching from all directions.

[1]  Huizhong Yang,et al.  Robust point‐to‐point iterative learning control with trial‐varying initial conditions , 2020, IET Control Theory & Applications.

[2]  Ralph-Johan Back,et al.  Decentralization of process nets with centralized control , 1983, PODC '83.

[3]  Iman Mohammadzaman,et al.  PID guidance law design using short time stability approach , 2015 .

[4]  Stephen A. Murtaugh,et al.  Fundamentals of proportional navigation , 1966, IEEE Spectrum.

[5]  Takeshi Yamasaki,et al.  A Study on the Improvement of Modified Proportional Navigation Guidance , 2019 .

[6]  Xiaodi Li,et al.  Input-to-state stability of impulsive reaction–diffusion neural networks with infinite distributed delays , 2021, Nonlinear Dynamics.

[7]  Xiaogong Lin,et al.  FAILOS guidance law based adaptive fuzzy finite-time path following control for underactuated MSV , 2020 .

[8]  V. Stojanovic,et al.  Application of cuckoo search algorithm to constrained control problem of a parallel robot platform , 2016, The International Journal of Advanced Manufacturing Technology.

[9]  Binfeng Pan,et al.  A novel fractional order PID navigation guidance law by finite time stability approach. , 2019, ISA transactions.

[10]  Vladimir Stojanovic,et al.  Robust identification for fault detection in the presence of non-Gaussian noises: application to hydraulic servo drives , 2020 .

[11]  Han-Lim Choi,et al.  Consensus-Based Decentralized Auctions for Robust Task Allocation , 2009, IEEE Transactions on Robotics.

[12]  Arthur E. Bryson,et al.  Applied Optimal Control , 1969 .

[13]  M. Alighanbari,et al.  Decentralized Task Assignment for Unmanned Aerial Vehicles , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[14]  Ramdane Maamri,et al.  A Distributed Approach to the Multi-Robot Task Allocation Problem Using the Consensus-Based Bundle Algorithm and Ant Colony System , 2020, IEEE Access.

[15]  Han-Lim Choi,et al.  Consensus-Based Auction Approaches for Decentralized Task Assignment , 2008 .

[16]  Fumiaki Imado,et al.  Proportional navigation versus high-g barrel roll maneuvers from optimal control of view , 1996 .

[17]  Ana L. C. Bazzan,et al.  Solving task allocation problem in multi Unmanned Aerial Vehicles systems using Swarm intelligence , 2018, Eng. Appl. Artif. Intell..

[18]  Jianan Wang,et al.  A modified cooperative proportional navigation guidance law , 2019, J. Frankl. Inst..

[19]  Tor Arne Johansen,et al.  MPC-based Collision Avoidance Strategy for Existing Marine Vessel Guidance Systems , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[20]  Zhengqing Han,et al.  Modified Line-of-Sight Guidance Law With Adaptive Neural Network Control of Underactuated Marine Vehicles With State and Input Constraints , 2020, IEEE Transactions on Control Systems Technology.

[21]  George M Siouris,et al.  Missile Guidance and Control Systems , 2004 .

[22]  Loukas Petrou,et al.  Multi-objective optimization for dynamic task allocation in a multi-robot system , 2013, Eng. Appl. Artif. Intell..

[23]  Yixin Su,et al.  An improved integral light-of-sight guidance law for path following of unmanned surface vehicles , 2020, Ocean Engineering.

[24]  Vladimir Stojanovic,et al.  Asynchronous fault detection filtering for piecewise homogenous Markov jump linear systems via a dual hidden Markov model , 2021 .

[25]  Claude Le Pape.  A combination of centralized and distributed methods for multi-agent planning and scheduling , 1990, ICRA.

[26]  Dianguo Xu,et al.  A new guidance law for trajectory tracking of an underactuated unmanned surface vehicle with parameter perturbations , 2019, Ocean Engineering.

[27]  Dario Izzo,et al.  A survey on artificial intelligence trends in spacecraft guidance dynamics and control , 2018, Astrodynamics.

[28]  Yuanqiao Wen,et al.  The Review Unmanned Surface Vehicle Path Planning: Based on Multi-modality Constraint , 2020, ArXiv.

[29]  Vitaly Shalumov,et al.  Cooperative online Guide-Launch-Guide policy in a target-missile-defender engagement using deep reinforcement learning , 2020 .