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Mobile robot path planning method based on improved ant colony algorithm

A mobile robot, ant colony algorithm technology, applied in the direction of instruments, non-electric variable control, two-dimensional position/channel control, etc., can solve the problems of long running time, difficult to find the global optimal path, large cumulative turning angle, etc. Achieve the effect of improving operating efficiency, improving global search capabilities, and reducing the number of turns

Active Publication Date: 2021-04-13
YANGZHOU UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to overcome the defects of the prior art, to provide a mobile robot path planning method based on the improved ant colony algorithm, to solve the problem that the basic ant colony algorithm is easy to fall into the local optimum, the running time is too long, it is difficult to find the global optimal path, and the accumulated Problem with large turning angles

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  • Mobile robot path planning method based on improved ant colony algorithm
  • Mobile robot path planning method based on improved ant colony algorithm
  • Mobile robot path planning method based on improved ant colony algorithm

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Embodiment Construction

[0046] like figure 1 A mobile robot path planning method based on the improved ant colony algorithm is shown. The evaluation function of the A* algorithm is added to the heuristic function of the ant colony algorithm, which not only facilitates finding the global optimal path, but also speeds up the pathfinding speed. On the other hand, the corner constraint factor is added to the ant colony algorithm to avoid too many corners in the found path and the cumulative turning angle is too large, and it is applied to the path planning of the mobile robot to reduce the energy loss of the robot. Run time is accelerated to select an optimal path for a mobile robot combining path length and number of turns. Specific steps are as follows:

[0047] Step 1) Use the grid method to model the working environment, and set the starting point and target point of the movement for the mobile robot;

[0048] Create a grid map, the specific model is:

[0049]

[0050]Among them: x is the horiz...

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Abstract

The invention discloses a mobile robot path planning method based on an improved ant colony algorithm. The method comprises the steps: resetting an initial pheromone concentration, improving a heuristic function, and updating the pheromone concentration, wherein the resetting initial pheromone concentration is the setting of different pheromone concentrations for each grid; adding an A* algorithm valuation function and a corner constraint factor into the heuristic function, using the A* valuation function for searching for a global optimal solution, and meanwhile using the corner constraint factor for angle constraint; and in the pheromone updating part of the ant colony algorithm, adding a distribution principle of a wolf pack algorithm, using the principle to distribute pheromones, and meanwhile, using a maximum-minimum principle in an MMAS algorithm to limit the pheromone concentration. According to the method, the global search capability of the algorithm can be enhanced, a path length is shortened, a smoother moving track is planned for a mobile robot, the method is not limited to the fields of computers and artificial intelligence, and the method is also suitable for similar problems in the fields of traffic, logistics, management and the like.

Description

technical field [0001] The invention relates to the field of artificial intelligence, in particular to a path planning method for a mobile robot based on an improved ant colony algorithm. Background technique [0002] With the rapid development of artificial intelligence technology, robots are widely used in warehousing logistics, manufacturing plants, smart medical care and other fields. Path planning is an important branch of mobile robot research. Path planning refers to the mobile robot looking for a collision-free path from the starting point that avoids all obstacles, so as to reach the target point smoothly. Traditional path planning methods include artificial potential field method, Dijstra algorithm, visual graph method and so on. With the increase of obstacles, the scale and complexity of problems continue to increase, and traditional algorithms have certain limitations. Therefore, some bionic intelligent optimization algorithms have emerged as the times require,...

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Application Information

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IPC IPC(8): G05D1/02
CPCG05D1/0223G05D1/0221G05D1/0276
Inventor 刘爽李开荣
Owner YANGZHOU UNIV
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