Chaotic artificial bee colony approach to Uninhabited Combat Air Vehicle (UCAV) path planning

Xu, Chunfang; Duan, Haibin; Liu, Fang

doi:10.1016/j.ast.2010.04.008

Cited by 226 publications

(98 citation statements)

References 9 publications

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“…Chaos is the highly unstable motion of deterministic systems infinite phase space which often exists in nonlinear systems [9].Chaos theory is epitomized by the so-called 'butterfly effect' detailed by Lorenz [10]. Until now, chaotic behavior has already been observed in the laboratory in a variety of systems including electrical circuits, lasers, oscillating chemical reactions, fluid dynamics, as well as computer models of chaotic processes.…”

Section: Principles Of Cedamentioning

confidence: 99%

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Quadrotor Flight Control Parameters Optimization Based on Chaotic Estimation of Distribution Algorithm

Chu

Duan

2013

Advances in Neural Networks – ISNN 2013

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Abstract.Quadrotor is a type of rotor craft that consists of four rotors and two pairs of counter-rotating, fixed-pitch blades located at the four corners of the body. The flight control parameters optimization is one of the key issues for quadrotor. Estimation of distribution algorithm is a new kind of evolutionary algorithm developed rapidly recently. However, low convergence speed and local optimum of the EDA are the main disadvantages that limit its further application. To overcome the disadvantages of EDA, a chaotic estimation of distribution algorithm is proposed in this paper. It is a combination of chaos theory and principles of estimation of distribution algorithm. Series of experimental comparison results are presented to show the feasibility, effectiveness and robustness of our proposed method. The results show that the proposed chaotic EDA can effectively improve both the global searching ability and the speed of convergence.

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Section: Principles Of Cedamentioning

confidence: 99%

“…Step5: Conduct the chaotic search around the best solution based on formula (9). Among the engendered series of solutions, select the best one and use it to replace the former best solution.…”

Section: Ceda Approach For Flight Control Parameters Optimizationmentioning

confidence: 99%

Quadrotor Flight Control Parameters Optimization Based on Chaotic Estimation of Distribution Algorithm

Chu

Duan

2013

Advances in Neural Networks – ISNN 2013

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“…In this model, S is defined as the starting point, and T is the target point ( Figure 1) [18,19]. There are some threat resources in UCAV battlefield, for example, radars, missiles, and artillery, the effects of which all are shown in the form of a circle.…”

Section: Threat Resource Model In Ucav Path Planningmentioning

confidence: 99%

“…In this work, the threat cost of the route between two discrete points was calculated. It is approximately equal to the sum of the threat cost of five points, as shown in Figure 2 [18,19]. 1 1 (x , y ) If the i th edge is within the effect range, the calculation formula of the threat cost is as follows: …”

Section: Evaluation Functionmentioning

confidence: 99%

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Quantum Wind Driven Optimization for Unmanned Combat Air Vehicle Path Planning

et al. 2015

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Unmanned combat air vehicle (UCAV) path planning aims to calculate the optimal or suboptimal flight path considering the different threats and constraints under the complex battlefield environment. This flight path can help the UCAV avoid enemy threats and improve the efficiency of the investigation. This paper presents a new quantum wind driven optimization (QWDO) for the path planning of UCAV. QWDO algorithm uses quantum rotation gate strategy on population evolution and the quantum non-gate strategy to realize the individual variation of population. These operations improve the diversity of population and avoid premature convergence. This paper tests this optimization in two instances. The experimental results show that the proposed algorithm is feasible in these two cases. Compared to quantum bat algorithm (QBA), quantum particle swarm optimization (QPSO), wind driven optimization (WDO), bat algorithm (BA), particle swarm optimization (PSO), and differential evolution (DE), the QWDO algorithm exhibited better performance. The simulation results demonstrate that the QWDO algorithm is an effective and feasible method for solving UCAV path planning.Keywords: wind driven optimization (WDO); unmanned combat air vehicle (UCAV); path planning; quantum wind driven optimization (QWDO) OPEN ACCESSAppl. Sci. 2015Sci. , 5 1458

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Post‐disaster assessment with unmanned aerial vehicles: A survey on practical implementations and research approaches

Recchiuto

Sgorbissa

2017

Journal of Field Robotics

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In this paper, we provide a review of the principal aspects related to search & rescue (SAR) with unmanned aerial vehicles (UAVs), with particular interest in the phase of post-disaster assessment (PDA). Some areas of interest related to this topic have been chosen for the analysis: the aerial platforms used in the field, multirobot software architectures, onboard sensors and simultaneous localization and mapping approaches, terrain coverage algorithms, autonomous navigation techniques, and human-swarm interfaces. All these aspects have been analyzed with respect to the state-of-the-art, and also in relation to the project PRISMA, which focuses on the development and deployment of robots and autonomous systems that can operate in emergency scenarios, with a specific reference to monitoring and real-time intervention

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Chaotic artificial bee colony approach to Uninhabited Combat Air Vehicle (UCAV) path planning

Cited by 226 publications

References 9 publications

Quadrotor Flight Control Parameters Optimization Based on Chaotic Estimation of Distribution Algorithm

Quadrotor Flight Control Parameters Optimization Based on Chaotic Estimation of Distribution Algorithm

Quantum Wind Driven Optimization for Unmanned Combat Air Vehicle Path Planning

Post‐disaster assessment with unmanned aerial vehicles: A survey on practical implementations and research approaches

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