A terminal guidance algorithm based on ant colony optimization

Nobahari, Hadi; Nasrollahi, Saeed

doi:10.1016/j.compeleceng.2019.05.012

Cited by 10 publications

(10 citation statements)

References 21 publications

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“…Intelligent algorithm simulates biological evolution and insect foraging behaviors in nature, mainly including genetic algorithm(GA) [34] , ant colony algorithm (ACO) [35] , particle swarm algorithm [36] , etc. GA has the characteristics of potential parallelism and is suitable for solving and optimizing complex problems.…”

Section: Figure1 Schematic Diagram Of Mobile Robot Motion Planningmentioning

confidence: 99%

Motion Planning for Mobile Robots—Focusing on Deep Reinforcement Learning: A Systematic Review

Sun

Zhang

et al. 2021

IEEE Access

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Mobile robots contributed significantly to the intelligent development of human society, and the motion-planning policy is critical for mobile robots. This paper reviews the methods based on motionplanning policy, especially the ones involving Deep Reinforcement Learning (DRL) in the unstructured environment. The conventional methods of DRL are categorized to value-based, policy-based and actorcritic-based algorithms, and the corresponding theories and applications are surveyed. Furthermore, the recently-emerged methods of DRL are also surveyed, especially the ones involving the imitation learning, meta-learning and multi-robot systems. According to the surveys, the potential research directions of motion-planning algorithms serving for mobile robots are enlightened.

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Section: Figure1 Schematic Diagram Of Mobile Robot Motion Planningmentioning

confidence: 99%

Motion Planning for Mobile Robots—Focusing on Deep Reinforcement Learning: A Systematic Review

Sun

Zhang

et al. 2021

IEEE Access

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“…On the one hand, the gap between the energy supply of the whole society and the energy consumption of the whole society is getting bigger and bigger. At the same time, in the process of energy use, waste problems are everywhere, making the energy saving potential more and more On the other hand, the use of energy produces a large amount of waste water and waste gas, which makes the problem of environmental pollution increasingly serious, and the environmental pollution caused by energy consumption has threatened human life [6]. Energy shortage and environmental pollution are two important issues that affect the improvement and progress of Chinese society and the entire human society.…”

Section: Research Background and Significancementioning

confidence: 99%

Energy Efficiency Calculation Based on Ant Colony Algorithm

2020

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The energy issue has always been the focus of attention of the international community. On the one hand, it is due to the limited and scarcity of energy, and on the other hand, energy is of great significance to the sustainable improvement of human society. In this context, as an important economic improvement area in China, the exploration of energy efficiency is of great significance for accelerating the realization of sustainable economic improvement in the future. The purpose of this paper is to calculate the energy efficiency based on the ant colony algorithm. Using the ant colony algorithm and the stochastic frontier model, the results based on the common frontier model, the results of the subregional frontier model and the empirical results of the input of classified energy elements are respectively carried out in the experiments.

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“…In addition, classical guidance theory and advanced guidance theory require specific formulas to calculate guidance commands, while intelligent algorithms do not require specific guidance command calculation formulas. Heuristic intelligent algorithms such as genetic algorithm [12], ant colony [13][14][15] algorithm, and particle swarm optimization (PSO) [16][17][18][19] algorithm have been utilized to calculate the guidance commands, which further improve the performance of both traditional and modern guidance laws.…”

Section: Introductionmentioning

confidence: 99%

Novel Particle Swarm Optimization Guidance for Hypersonic Target Interception with Impact Angle Constraint

Chen

Bao

2022

International Journal of Aerospace Engineering

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Considering that the terminal impact angle constraint can improve the interception performance of hypersonic target, a novel particle swarm optimization guidance (NPSOG) algorithm is proposed to satisfy the impact angle constraint. Two-dimensional dynamics engagement mode for hypersonic target interception is formulated. The performance index is positively correlated with the line-of-sight (LOS), LOS rate, and the relative distance between missile and target. The weight coefficients among the three are adaptively adjusted by the fuzzy logic controller. The particle swarm optimization (PSO) algorithm is utilized to generate the guidance commands. Numerical examples are given to verify the performance of the proposed guidance law in various engagement scenarios, and the performance of the algorithm is validated comparing with several heuristic guidance methods and nonheuristic guidance methods.

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A terminal guidance algorithm based on ant colony optimization

Cited by 10 publications

References 21 publications

Motion Planning for Mobile Robots—Focusing on Deep Reinforcement Learning: A Systematic Review

Motion Planning for Mobile Robots—Focusing on Deep Reinforcement Learning: A Systematic Review

Energy Efficiency Calculation Based on Ant Colony Algorithm

Novel Particle Swarm Optimization Guidance for Hypersonic Target Interception with Impact Angle Constraint

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