A Path Planning Method for Mobile Robots Based on Improved Ant Colony Algorithm

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

Active Publication Date: 2022-07-05
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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  • A Path Planning Method for Mobile Robots Based on Improved Ant Colony Algorithm
  • A Path Planning Method for Mobile Robots Based on Improved Ant Colony Algorithm
  • A Path Planning Method for Mobile Robots Based on Improved Ant Colony Algorithm

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[0046] like figure 1 A path planning method for mobile robots based on the improved ant colony algorithm is shown, adding the evaluation function of the A* algorithm to the heuristic function of the ant colony algorithm, which not only facilitates finding the global optimal path, but also speeds up the path finding. On the other hand, a corner constraint factor is added to the ant colony algorithm to avoid too many turning angles and too large cumulative turning angles of the found path. It is applied to the path planning problem of mobile robots to reduce the energy consumption lost by the robot, Faster runtime, selecting an optimal path for mobile robots that combines 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 ...

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Abstract

The invention discloses a path planning method for a mobile robot based on an improved ant colony algorithm, including resetting the initial pheromone concentration, improving the heuristic function and updating the pheromone concentration, wherein the resetting of the initial pheromone concentration is set for each grid Different pheromone concentrations; the A* algorithm evaluation function and the corner constraint factor are added to the heuristic function, the A* evaluation function is used to find the global optimal solution, and the corner constraint factor is used to constrain the angle; in the pheromone update of the ant colony algorithm Part, adding the distribution principle of the wolf group algorithm, using this principle to carry out the distribution of pheromone, and using the maximum-minimum principle in the MMAS algorithm to limit the pheromone concentration; the present invention can enhance the global search ability of the algorithm, shorten the path length, Planning a smoother running trajectory for mobile robots is not only limited to the fields of computers and artificial intelligence, but also applies to similar problems in the fields of transportation, logistics, and management.

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 and logistics, manufacturing plants, smart medical care and other fields. Path planning is an important branch of mobile robot research. Path planning means that the mobile robot finds 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, and visual graph method. With the increase of obstacles, the scale and complexity of the problem continue to increase, and traditional algorithms have certain limitations. Therefore, some bionic intelligent optimization algorithms emerge as the times require, such as a...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05D1/02
CPCG05D1/0223G05D1/0221G05D1/0276
Inventor 刘爽李开荣
Owner YANGZHOU UNIV
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