An Obstacle Avoidance Path Planning Algorithm Based on the Combination of Differential Evolution and Fuzzy Control
A technology of fuzzy control and path planning, applied in the directions of comprehensive factory control, road network navigator, navigation, etc., can solve the problem that intelligent vehicles cannot make obstacle avoidance, and achieve the effect of solving the local minimum problem
Active Publication Date: 2022-06-07
TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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[0004] The present invention aims to provide an obstacle avoidance path planning algorithm based on the combination of differential evolution and fuzzy control, aiming at the problem that the existing fuzzy control will cause the intelligent vehicle to fall into a local minimum problem so that it cannot make timely and accurate obstacle avoidance actions. The algorithm of the present invention constructs an obstacle avoidance path planning method for intelligent vehicles by using fuzzy control and differential evolution algorithm, and optimizes the fuzzy control rule table by using differential evolution algorithm, which can effectively solve the local minimum problem and obtain the global optimal solution
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[0043] In order to make the objects, features and advantages of the present invention more clearly understood, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
[0044] An obstacle avoidance path planning algorithm based on the combination of differential evolution and fuzzy control is implemented according to the following steps:
[0045] Step 1, design the fuzzy controller, its structural block diagram is as follows figure 1 shown, the specific steps are:
[0046] 1) Determine the structure of the fuzzy controller, and determine the basic system model according to the input and output of the studied system;
[0047] 2) Define the fuzzy distribution of input and output;
[0048] 3) Establish fuzzy control rules;
[0049] 4) Approximate reasoning;
[0050] Step 2, using the differential evolution algorithm to optimize the fuzzy control rule table;
[0051] 1) Determine the representation of th...
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An obstacle avoidance path planning algorithm based on the combination of differential evolution and fuzzy control of the present invention belongs to the field of intelligent control, and solves the problem that the existing fuzzy control will also cause the intelligent vehicle to fall into a local minimum, making it impossible to make timely and accurate obstacle avoidance Action problem, the specific steps are: 1) Design the fuzzy controller, 2) Use the differential evolution algorithm to optimize the fuzzy control rule table, 3) Apply the optimized fuzzy control rule table to the fuzzy controller and then implement it in the entire fuzzy control system, The algorithm of the present invention constructs an obstacle avoidance path planning method for intelligent vehicles by using fuzzy control and differential evolution algorithm, and optimizes the fuzzy control rule table by using differential evolution algorithm, which can effectively solve the local minimum problem and obtain the global optimal solution.
Description
technical field [0001] The invention belongs to the field of intelligent control, in particular to an obstacle avoidance path planning algorithm based on the combination of differential evolution and fuzzy control. Background technique [0002] Path planning is one of the current research hotspots of intelligent vehicle technology. It requires the intelligent vehicle to autonomously search for an optimal path to avoid obstacles in the process of moving from the initial state to the target state. Among them, the obstacle avoidance strategy is the top priority. At present, researchers mostly use artificial potential field method, Sanger method, fuzzy control method and other methods to study the obstacle avoidance algorithm of intelligent vehicles. Among them, the artificial potential field method is widely used because of its simple algorithm and easy real-time control, but it is easy to fall into a local minimum and cannot make the smart car reach the target position; while ...
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IPC IPC(8): G01C21/34
CPCG01C21/3446Y02P90/02
Inventor 张春美刘承鹏郭红戈申静如
Owner TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY