Research on an AGV path planning method

Chen, Zhihui; Chen, Xiaoyan; Wang, Shiming

doi:10.5954/icarob.2022.os12-1

Cited by 2 publications

(4 citation statements)

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“…This article uses grid serial numbers to encode chromosomes; that is, each path of the AGV is composed of the grid serial numbers it passes through in the process of moving from S to G. The red path in Figure 1 can be expressed as {S, 10, 21, 31, 42, 43, 44, 55, 66, In Equation (1), m is the number of grids in each row or column, mod is the remainder operation, and fix is the rounding operation.…”

Section: Encoding Methodsmentioning

confidence: 99%

“…In scenarios such as intelligent manufacturing factories and logistics and transportation systems, automated guided vehicles (AGVs) have been widely used as the main tools for intelligent material transportation [1]. AGV path planning technology refers to AGVs that autonomously generate safe and feasible paths based on evaluation criteria such as travel time, path length, and turning frequency, combined with their own sensors' perception of the environment [2,3].…”

Section: Introductionmentioning

confidence: 99%

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Omnidirectional AGV Path Planning Based on Improved Genetic Algorithm

Niu,

Fu,

Dong

2024

WEVJ

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To address the issues with traditional genetic algorithm (GA) path planning, which often results in redundant path nodes and local optima, we propose an Improved Genetic Algorithm that incorporates an ant colony algorithm (ACO). Firstly, a new population initialization method is proposed. This method adopts a higher-quality random point generation strategy to generate random points centrally near the start and end of connecting lines. It combines the improved ACO algorithm to connect these random points quickly, thus greatly improving the quality of the initial population. Secondly, path smoothness constraints are proposed in the adaptive function. These constraints reduce the large-angle turns and non-essential turns, improving the smoothness of the generated path. The algorithm integrates the roulette and tournament methods in the selection stage to enhance the searching ability and prevent premature convergence. Additionally, the crossover stage introduces the edit distance and a two-layer crossover operation based on it to avoid ineffective crossover and improve convergence speed. In the mutation stage, we propose a new mutation method and introduce a three-stage mutation operation based on the idea of simulated annealing. This makes the mutation operation more effective and efficient. The three-stage mutation operation ensures that the mutated paths also have high weights, increases the diversity of the population, and avoids local optimality. Additionally, we added a deletion operation to eliminate redundant nodes in the paths and shorten them. The simulation software and experimental platform of ROS (Robot Operating System) demonstrate that the improved algorithm has better path search quality and faster convergence speed. This effectively prevents the algorithm from maturing prematurely and proves its effectiveness in solving the path planning problem of AGV (automated guided vehicle).

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Section: Encoding Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Omnidirectional AGV Path Planning Based on Improved Genetic Algorithm

Niu,

Fu,

Dong

2024

WEVJ

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“…Recently, it was founded that numerous studies in AGV path finding and obstacle avoidance using traditional A* algorithm combined with other algorithms for the purpose of increasing system capabilities. Chen et al [74] stated in his research by findings that the A* algorithm needs to be combined with other algorithms. Wu et al [75] proposed a hybrid dynamic path planning algorithm for forklift AGV by improving A* with Dynamic Window Algorithm (DWA).…”

Section: ) A-starmentioning

confidence: 99%

“…The method also handles unexpected obstacles in the path robustly Chen et al [72] Improved A* To propose two-stage congestionminimizing routing method Success to increase efficiency and bringing huge economic benefits to the warehouses Tang et al [73] Improved A* To avoid the problems of several nodes, long distance and large turning angle. Traditional A* algorithm limitation usually exist in the sawtooth and cross paths Success to reduces the number of nodes by 10% 40%, while the number of turns is reduced by 25%, the turning angle is reduced by 33.3%, and the total distance is reduced by 25.5% Chen et al [74] A* To find shortest path between two points in path planning Success to identified that A* is better and shorter than ACO Wu et al [75] Improved A* To plan FAGV the global optimal path and more suitable Success to improve A* algorithm in simulation the number of paths turns of the is reduced by 62.5%, the smoothness is higher, and the turning angle is smaller Chen et al [76] A* To find AGV shortest path planning in a static raster environment problem Success to demonstrates the effectiveness of the method and provide some reference for the shortest path planning of AGV Bai et al [77] Improved…”

Section: Zhao Et Al [25]mentioning

confidence: 99%

A Survey on Navigation Approaches for Automated Guided Vehicle Robots in Dynamic Surrounding

2023

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Automated Guided Vehicle (AGV) have received a lot of attention in recent years in terms of both hardware and software research. Nowadays, the AGV offer more adaptable and effective industrial and transportation system solutions. An AGV navigation techniques is essential to its operation. The decision to use AGV navigation is not simple, though to be suite to the application and sufficient. This paper survey the navigation approaches applied in AGV in the past five years of published academic research. In doing so, this work responds to three related questions: (1) is the AGV classical navigation techniques still relevant to the current application area; (2) is the heuristic navigation techniques itself able to optimize AGV movement in term of guide and strategy; (3) is the use of Artificial Intelligence (AI) in AGV navigation techniques able to increase system performance. As a result, numerous techniques for the AGV navigation have been developed globally. On the other hand, the most popular navigation approaches are provided below for more research.

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Research on an AGV path planning method

Cited by 2 publications

References 0 publications

Omnidirectional AGV Path Planning Based on Improved Genetic Algorithm

Omnidirectional AGV Path Planning Based on Improved Genetic Algorithm

A Survey on Navigation Approaches for Automated Guided Vehicle Robots in Dynamic Surrounding

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