An improved differential evolution based artificial fish swarm algorithm and its application to AGV path planning problems

Li, Guangqiang; Liu, Qi; Yang, Yong; Zhao, Fengqiang; Zhou, Yiran; Guo, Chen

doi:10.23919/chicc.2017.8027746

Cited by 13 publications

(2 citation statements)

References 7 publications

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“…The evolutionary algorithm, rooted in Darwin's evolutionary theory, encompasses selection, recombination, and mutation. Notable algorithms in UAV path planning include GA [62], differential evolution (DE) [63], and non-dominated sorting genetic algorithm-II (NSGA-II) [64]. GA, renowned for its versatility and efficacy in large-scale and nonlinear problems, is frequently employed across path-planning studies.…”

Section: Evolutionary Algorithmmentioning

confidence: 99%

Research on Unmanned Aerial Vehicle Path Planning

Luo,

Tian,

Wang

2024

Drones

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As the technology of unmanned aerial vehicles (UAVs) advances, these vehicles are increasingly being used in various industries. However, the navigation of UAVs often faces restrictions and obstacles, necessitating the implementation of path-planning algorithms to ensure safe and efficient flight. This paper presents innovative path-planning algorithms designed explicitly for UAVs and categorizes them based on algorithmic and functional levels. Moreover, it comprehensively discusses the advantages, disadvantages, application challenges, and notable outcomes of each path-planning algorithm, aiming to examine their performance thoroughly. Additionally, this paper provides insights into future research directions for UAVs, intending to assist researchers in future explorations.

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Section: Evolutionary Algorithmmentioning

confidence: 99%

Research on Unmanned Aerial Vehicle Path Planning

Luo,

Tian,

Wang

2024

Drones

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“…Meta-heuristic algorithms are a class of optimization algorithms inspired by natural phenomena such as genetic algorithms [15,16], differential evolution (DE) [17,18], swarm intelligence [19,20] (ant [21] and bee [22] colonies, wolf optimization [23]), and simulated annealing (SA) [24] that have been applied to various optimization problems including path planning for AGVs. Meta-heuristic algorithms can handle high-dimensional and nonlinear optimization problems with multiple objectives and constraints.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Path Planning Capabilities of Automated Guided Vehicles in Dynamic Environments: Multi-Objective PSO and Dynamic-Window Approach

Dao,

Ngo,

Pan

et al. 2024

Biomimetics

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Automated guided vehicles (AGVs) are vital for optimizing the transport of material in modern industry. AGVs have been widely used in production, logistics, transportation, and commerce, enhancing productivity, lowering labor costs, improving energy efficiency, and ensuring safety. However, path planning for AGVs in complex and dynamic environments remains challenging due to the computation of obstacle avoidance and efficient transport. This study proposes a novel approach that combines multi-objective particle swarm optimization (MOPSO) and the dynamic-window approach (DWA) to enhance AGV path planning. Optimal AGV trajectories considering energy consumption, travel time, and collision avoidance were used to model the multi-objective functions for dealing with the outcome-feasible optimal solution. Empirical findings and results demonstrate the approach’s effectiveness and efficiency, highlighting its potential for improving AGV navigation in real-world scenarios.

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