AGV Path Planning based on Improved A-star Algorithm

Xu, Yanqiang; Zheng, Da Wei

doi:10.1109/imcec46724.2019.8983841

Cited by 56 publications

(32 citation statements)

References 4 publications

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“…A* algorithm is not only the most effective direct search method for solving the shortest path, but also a common heuristic algorithm for many other problems. Therefore, a good heuristic function based on environment selection is crucial [ 49 ].…”

Section: Three-dimensional Distance Vector-hop (3ddv-hop) and A* Amentioning

confidence: 99%

Three-Dimensional Localization Algorithm Based on Improved A* and DV-Hop Algorithms in Wireless Sensor Network

Huang

Han

Cui

et al. 2021

Sensors

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In the traditional wireless sensor networks (WSNs) localization algorithm based on the Internet of Things (IoT), the distance vector hop (DV-Hop) localization algorithm has the disadvantages of large deviation and low accuracy in three-dimensional (3D) space. Based on the 3DDV-Hop algorithm and combined with the idea of A* algorithm, this paper proposes a wireless sensor network node location algorithm (MA*-3DDV-Hop) that integrates the improved A* algorithm and the 3DDV-Hop algorithm. In MA*-3DDV-Hop, firstly, the hop-count value of nodes is optimized and the error of average distance per hop is corrected. Then, the multi-objective optimization non dominated sorting genetic algorithm (NSGA-II) is adopted to optimize the coordinates locally. After selection, crossover, mutation, the Pareto optimal solution is obtained, which overcomes the problems of premature convergence and poor convergence of existing algorithms. Moreover, it reduces the error of coordinate calculation and raises the localization accuracy of wireless sensor network nodes. For three different multi-peak random scenes, simulation results show that MA*-3DDV-Hop algorithm has better robustness and higher localization accuracy than the 3DDV-Hop, PSO-3DDV-Hop, GA-3DDV-Hop, and N2-3DDV-Hop.

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Section: Three-dimensional Distance Vector-hop (3ddv-hop) and A* Amentioning

confidence: 99%

Three-Dimensional Localization Algorithm Based on Improved A* and DV-Hop Algorithms in Wireless Sensor Network

Huang

Han

Cui

et al. 2021

Sensors

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“…Traditional path planning algorithms including artificial potential field method [1], A* algorithm [2] [3], Simulated Annealing algorithm (SA) [4], and so on. These traditional path planning methods are not suitable for complex environments with various constraints.…”

Section: Introductionmentioning

confidence: 99%

Path Planning of UAV Based on Improved Adaptive Grey Wolf Optimization Algorithm

Zhang

et al. 2021

IEEE Access

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Aiming at the three-dimensional path planning of unmanned aerial vehicle (UAV) in the complex environment of material delivery in earthquake-stricken areas, this paper proposes an improved adaptive grey wolf optimization algorithm (AGWO) based on the grey wolf optimization algorithm (GWO). There are two main contributions of the proposed method. Firstly, we propose an adaptive convergence factor adjustment strategy and an adaptive weight factor to update the individual's position. The effectiveness of the improved algorithm is verified by the convergence analysis and the test function simulation experiment. Secondly, the improved algorithm is applied to UAV path planning, the environmental map model is established by integrating digital elevation map and equivalent mountain threat model, and the performance evaluation function is established by fitting the calculated track length. The simulation results show that the improved AGWO is superior to the traditional intelligent algorithm in convergence precision, speed and stability performance, and it is effective for 3D trajectory optimization in complex environment.INDEX TERMS Adaptive grey wolf optimization algorithm (AGWO), path planning, unmanned aerial vehicle (UAV).

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“…A two-stage ant colony algorithm is also proposed to solve it [1] . Based on the characteristics of the A* algorithm, Zheng et al optimize a node search method and use an angle evaluation cost function to find the path with the least inflection point [2] . Jerald and Asokan implement a non-traditional optimization technique called the adaptive genetic algorithm to do simultaneous scheduling [3] .…”

Section: Introductionmentioning

confidence: 99%

Load-In-Load-Out AGV Route Planning in Automatic Container Terminal

Luan

et al. 2020

IEEE Access

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Efficient automatic guided vehicle (AGV) scheduling is the key to increase the throughput of automated container terminals. Traditional transport strategies cannot guarantee that AGVs are fully loaded during their traveling between the dock and the container yard, which leads to the insufficient utilization of AGVs. A load-in-load-out AGV route planning mode provides two-way loading between the dock and the container yard and thus improves the efficiency of container terminals. In this paper, a load-in-load-out AGV route planning model is designed with the help of a buffer zone where an AGV can carry at most two containers. A simulated annealing algorithm is used to solve it. Comparisons with the popular generic algorithm and particle swarm algorithm are made. Simulated results show that the proposed algorithm can effectively solve the problem and realize the bi-directional loading of AGVs, which is of great significance to improve the efficiency of the production of automated container terminals.

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AGV Path Planning based on Improved A-star Algorithm

Cited by 56 publications

References 4 publications

Three-Dimensional Localization Algorithm Based on Improved A* and DV-Hop Algorithms in Wireless Sensor Network

Three-Dimensional Localization Algorithm Based on Improved A* and DV-Hop Algorithms in Wireless Sensor Network

Path Planning of UAV Based on Improved Adaptive Grey Wolf Optimization Algorithm

Load-In-Load-Out AGV Route Planning in Automatic Container Terminal

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