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Indoor AGV (Automated Guided Vehicles) path planning method based on improved A* algorithm

A path planning and algorithm technology, applied in the field of indoor navigation, can solve problems such as touching obstacles and planning long paths, and achieve the effects of reducing the number of nodes, improving search efficiency, and good path planning effects

Active Publication Date: 2018-05-04
SOUTHEAST UNIV
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Problems solved by technology

The traditional A* algorithm regards the planning target as a particle, and the planned path is likely to touch obstacles, and only uses Manhattan distance or Euclidean distance as the cost function value, making the planned path longer

Method used

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  • Indoor AGV (Automated Guided Vehicles) path planning method based on improved A* algorithm
  • Indoor AGV (Automated Guided Vehicles) path planning method based on improved A* algorithm
  • Indoor AGV (Automated Guided Vehicles) path planning method based on improved A* algorithm

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[0061] In order to verify the effectiveness of the algorithm of the present invention, the simulation map adopts a grid model of 50×40, each grid size is 0.4m×0.4m, the starting point is set to (1,1), and the end point is set to (48,38). The path planned by the traditional A* algorithm is as follows: image 3 As shown, since the traditional A* algorithm does not consider the AGV size, the map is expanded twice in the improved A* algorithm, and the cost function is optimized. Figure 5 is the path planning graph after expanding the map, Figure 6 It is the path diagram of the actual AGV in the obstacle environment.

[0062] Table 1 shows the parameter comparison between the traditional A* algorithm and the improved A* algorithm. The traditional A* algorithm uses the Manhattan distance without considering the size of the AGV. The planned path length is short, but there are many dangerous points (points closer to obstacles), and the search solution space is also large, and the ...

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Abstract

The invention discloses an indoor AGV (Automated Guided Vehicles) path planning method based on an improved A* algorithm. The method comprises the following steps: firstly, constructing an environmental map by adopting a grid method, and expanding the grid map according to the size of an AGV; then, optimizing a search strategy of a traditional A* algorithm, introducing a multi-value map model, cost function evaluation and path inflexion smoothing, and adding safety factors as path evaluation indexes; and finally, adding a starting point and a target point of the AGV to the grid map, adopting an improved distance evaluation function, taking the node with the smallest cost function as a node of the next step, gradually planning a running route of the AGV, and finally displaying the running route in an electronic map. Simulated analysis verifies that the AGV path planning based on the improved A* algorithm is higher in search route safety factor, smoother in path and higher in search speed.

Description

technical field [0001] The invention relates to a path planning technology based on the improved Manhattan distance, and belongs to the technical field of indoor navigation. Background technique [0002] Automated guided vehicles, referred to as AGV (Automated Guided Vehicles) is one of the key equipment in the modern industrial automation logistics system. An important guarantee for intelligent obstacle avoidance. [0003] Path planning is to find a collision-free optimal path from the start point to the end point in its workspace in a given obstacle environment, according to certain optimization criteria (such as the shortest path, the shortest time, etc.). The traditional A* algorithm regards the planning target as a particle, and the planned path is likely to touch obstacles, and only uses Manhattan distance or Euclidean distance as the cost function value, making the planned path longer. When using the improved A* algorithm for AGV path planning, it can effectively so...

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Application Information

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IPC IPC(8): G01C21/20G01C21/34
CPCG01C21/206G01C21/3446
Inventor 程向红张震
Owner SOUTHEAST UNIV
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