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AGV path planning method based on improved DWA algorithm in dynamic environment

A dynamic environment and path planning technology, applied in manufacturing computing systems, electric/hybrid power, two-dimensional position/channel control, etc., can solve the problem of less research on efficient obstacle avoidance methods for dynamic obstacles, and achieve shortened AGV Exercise time, improve the effect of obstacle avoidance judgment ability

Pending Publication Date: 2022-08-05
HARBIN INST OF TECH AT WEIHAI
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In the existing technology, the current research on AGV path planning is mainly to use different planning methods to search the path, improve the speed of path search, reduce the inflection points on the path, and improve the smoothness of the path curve. However, for complex areas such as production workshops In a dynamic environment, there are few researches on efficient obstacle avoidance methods for dynamic obstacles

Method used

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  • AGV path planning method based on improved DWA algorithm in dynamic environment
  • AGV path planning method based on improved DWA algorithm in dynamic environment
  • AGV path planning method based on improved DWA algorithm in dynamic environment

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0085] Example 1: Build a grid map

[0086] The invention takes the semi-automatic production workshop of gyro products as the research object, extracts and simplifies the layout of the workshop, performs two-dimensional grid processing on the internal environment of the production workshop where the AGV is located, and constructs a grid map of the production workshop, such as figure 2 shown.

[0087] The production workshop in the figure is about 32m long and 20m wide. The AGV is placed in a plane rectangular coordinate system, with the X axis in the horizontal direction and the Y axis in the vertical direction. Referring to the actual situation of the objects in the production workshop, the number of black squares in the figure is the largest, indicating Walls, workstations and other static obstacles, while scattered in the space surrounded by static obstacles, a small number of dark squares represent people or other dynamic obstacles; there are two light-colored squares, w...

Embodiment 2

[0088] Example 2: Principle of A* Algorithm

[0089] Specifically, the A* algorithm is used for global path planning. The A* algorithm is mainly used for path planning in a two-dimensional plane, and is the most effective direct search method for finding the shortest path in a static map. The A* algorithm is a kind of heuristic algorithm, which uses the heuristic function to guide the search direction of the path. The heuristic search path planning algorithm is used, and the evaluation function is used to guide the search and expansion of nodes. Therefore, the evaluation function affects the size of the search space and the speed of the algorithm. The evaluation function expression of the traditional A* algorithm is shown in the following formula.

[0090] f(n)=g(n)+h(n)

[0091] Among them, n represents the current node, f(n) represents the evaluation function from the starting point to the target point via node n; g(n) represents the actual cost of reaching the node n from...

Embodiment 3

[0097] Embodiment 3: Principle of DWA Algorithm

[0098] The DWA algorithm converts position control into speed control, so it is necessary to analyze the motion model of the AGV. The kinematic model of the AGV of the present invention is as follows image 3 shown.

[0099] The AGV kinematics model of the present invention adopts a two-wheel differential motion model. It is assumed that within the sampling time, the AGV displacement is very small, and the movement trajectory is regarded as straight line processing. At this time, the mathematical expression of the motion model is:

[0100] x n+1 =x n +Δs*cosΔθ

[0101] y n+1 =y n +Δs*sinΔθ

[0102] θ n+1 =θ n +Δθ

[0103] where, (x n ,y n ,θ n ) is the position information of the AGV at the current moment, (x n+1 ,y n+1 ,θ n+1 ) is the position information of the AGV at the next moment.

[0104] The DWA algorithm samples the speed and angular speed according to the current speed and angular speed of the AGV, and...

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Abstract

The invention discloses an AGV path planning method based on an improved DWA algorithm in a dynamic environment, and belongs to the technical field of AGV path planning. According to the method, firstly, a local DWA algorithm is designed by obtaining local map information around the AGV, dynamic obstacles in a local map are classified, the speed of the dynamic obstacles is evaluated, the obstacle avoidance judgment capability of the AGV on the dynamic obstacles is improved, and the degree that the AGV deviates from the shortest path is reduced; secondly, aiming at the problem that the direction needs to be adjusted when the DWA algorithm moves to the path point, the adjustment time of the AGV at the path point is shortened by optimizing a DWA evaluation function, and the transportation efficiency of the AGV is improved by optimizing the inflection point of the global path of the AGV through the improved DWA algorithm; and finally, simulation verification is carried out, and a simulation result shows that the improved DWA algorithm provided by the invention can ensure real-time obstacle avoidance of the AGV in an environment with a complex dynamic condition, and meanwhile, the transportation time of the AGV is shortened, and the transportation efficiency is improved.

Description

technical field [0001] The invention belongs to the technical field of AGV path planning, and more particularly, relates to an AGV path planning method based on an improved DWA algorithm in a dynamic environment. Background technique [0002] The production process of multi-variety, small-batch, and customized complex products belongs to discrete production. For example, in a typical semi-automatic production workshop with gyro parts, tasks such as material transportation in the assembly process are still completed by manual transportation. With the development of intelligent manufacturing technology, these semi-automatic production enterprises have gradually begun to intelligently upgrade transportation tasks, and provide support for intelligent production by replacing manual transportation with AGVs. Since part of the assembly process of customized and complex products like gyro parts cannot be done without manual work, in the production workshop, the mobility of personnel...

Claims

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Application Information

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IPC IPC(8): G05D1/02
CPCG05D1/0214G05D1/0221Y02P90/60
Inventor 王琳童弋王瑞钟诗胜张永健
Owner HARBIN INST OF TECH AT WEIHAI
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