A Dynamic Obstacle Avoidance Method Based on Sensor Fusion and Improved q-Learning Algorithm

A technology of dynamic obstacle avoidance and learning algorithm, applied in instruments, surveying and mapping and navigation, navigation calculation tools, etc., can solve the problem of small amount of calculation and real-time performance, and achieve the effect of improving the efficiency of obstacle avoidance

Active Publication Date: 2022-03-22
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

Each has its own advantages and disadvantages. For example, the artificial potential field method has a small amount of calculation and good real-time performance, but it is prone to local minimum points

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  • A Dynamic Obstacle Avoidance Method Based on Sensor Fusion and Improved q-Learning Algorithm
  • A Dynamic Obstacle Avoidance Method Based on Sensor Fusion and Improved q-Learning Algorithm
  • A Dynamic Obstacle Avoidance Method Based on Sensor Fusion and Improved q-Learning Algorithm

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Embodiment Construction

[0046] The technical solutions in the embodiments of the present invention will be described clearly and in detail below with reference to the drawings in the embodiments of the present invention. The described embodiments are only some of the embodiments of the invention.

[0047] The technical scheme that the present invention solves the problems of the technologies described above is:

[0048] like image 3 As shown, the dynamic obstacle avoidance method of mobile robot based on sensor fusion and Q learning algorithm, the method includes the following steps:

[0049] S1: Set the safety distance dm between the robot and the obstacle, and the target coordinate position information (x t ,y t ) and range Rm;

[0050] S2: Determine the current pose of the robot (x r ,y r ,θ r ), and combine the static map information with the target point (x t ,y t ) carry out navigation path planning, and start to move forward;

[0051] S3: During the navigation process, the environme...

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Abstract

The present invention claims to protect a dynamic obstacle avoidance method based on sensor fusion and improved Q-learning algorithm, including steps: S1: setting the safe distance between the robot and the obstacle, the target coordinate position information and the range when the robot moves; S2: determining the current position of the robot Attitude, carry out navigation path planning, and start to move forward; S3: During the navigation process, the environmental data detected by the sonar sensor and the environmental data detected by the laser sensor are preprocessed and characterized, and then fused to obtain the environmental data; S4: According to The fused environmental data judges whether the current robot state needs dynamic obstacle avoidance. If necessary, enter S5, and if not, enter S6; S5: use the improved Q-learning dynamic obstacle avoidance algorithm to obtain the next action state (a, θ) ; S6: judge whether the robot arrives at the target point, if not then return to S2 to continue navigation, if it has arrived then end the navigation. The method of the invention effectively overcomes the defect of a single sensor and effectively improves the obstacle avoidance efficiency in a dynamic environment.

Description

technical field [0001] The invention belongs to the technical field of robot path planning, and relates to a dynamic obstacle avoidance method of a mobile robot based on sensor fusion and Q learning algorithm. Background technique [0002] Path planning is one of the key elements of an autonomous mobile robot. It is hoped that the mobile robot can reach the destination as quickly and accurately as possible, and it is also required that the robot can safely and effectively avoid obstacles in the environment. At present, there are many better solutions to safely and effectively avoid obstacles and accurately reach the destination in a static environment. However, when there are moving obstacles in the environment, and the speed and position of the obstacles are changing all the time, the real-time and accuracy of the obstacle avoidance algorithm in the navigation process of the mobile robot are higher than those in the static environment. Higher, if you continue to use the al...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05D1/02G01C21/20
CPCG05D1/0231G05D1/0255G01C21/005G01C21/20
Inventor 张毅魏新周详宇李晋宏
Owner CHONGQING UNIV OF POSTS & TELECOMM
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