An Intelligent Path Planning Method for Mobile Robots

A mobile robot and path planning technology, which is applied in the direction of instruments, non-electric variable control, vehicle position/route/height control, etc., can solve the problems of local optimal oscillation and optimization failure of artificial potential field method, and achieve real-time avoidance. Effects of disability capability, high usability, and research value

Active Publication Date: 2022-06-28
SHANGHAI INST OF TECH
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AI Technical Summary

Problems solved by technology

[0004] In the actual environment, unknown dynamic obstacles may randomly appear. The common methods for dynamic environments include artificial potential field method and dynamic window method. Because the artificial potential field method is easy to fall into the local optimum and oscillate, the problem of optimization failure

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  • An Intelligent Path Planning Method for Mobile Robots
  • An Intelligent Path Planning Method for Mobile Robots
  • An Intelligent Path Planning Method for Mobile Robots

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Embodiment Construction

[0060] The following will clearly and completely describe and discuss the technical solutions in the embodiments of the present invention with reference to the accompanying drawings of the present invention. Obviously, what is described here is only a part of the examples of the present invention, not all of the examples. All other embodiments obtained by those of ordinary skill in the art without creative work, all belong to the protection scope of the present invention.

[0061] like Figure 1-5 As shown, this embodiment discloses an intelligent path planning method for a mobile robot, including the following steps:

[0062] Step 1: Build a grid map according to the known static two-dimensional space, set the grid number, and select the starting point S and the target point E;

[0063] Step 2: Improve the ant colony algorithm for global path planning;

[0064] Step 3: The mobile robot travels on the globally planned path, the mobile robot sensor module detects unknown obst...

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Abstract

The invention discloses an intelligent path planning method for a mobile robot. A static two-dimensional grid map is established, and an improved ant colony algorithm is used for global path planning; a sensor module of a mobile robot detects unknown obstacle information, and calculates obstacle motion trajectories and robot motion trajectories. , using the optimized dynamic window method for local dynamic obstacle avoidance, with the current position of the robot as the starting point, and the nearest key node on the global planning path as the temporary target point for dynamic obstacle avoidance; the robot drives along the planned path and reaches the destination safely. The present invention comprehensively considers the actual problems of static obstacles and dynamic obstacles in the map environment, improves the heuristic function of the ant colony algorithm, adjusts the pheromone update rules for global path planning, and adopts optimized The dynamic window method is used to avoid obstacles and complete local path planning. The robot has relatively high practicality and research value when running on the actual map.

Description

technical field [0001] The invention relates to the field of robot path planning, in particular to an intelligent path planning method for a mobile robot. Background technique [0002] There are two types of obstacles in the map environment where the robot actually runs, one is a known static obstacle, and the other is an unknown obstacle that appears randomly. Global path planning refers to selecting a path from the starting point to the target point under the given environmental obstacle conditions, so that the robot can pass through all obstacles safely and without collision. This method of autonomously avoiding obstacles and completing tasks is an important content in robotics research and applications. [0003] A map of static obstacles. Common robot global path planning methods include A* algorithm, Dijkstra algorithm, genetic algorithm, particle swarm algorithm and ant colony algorithm. Among them, the A* algorithm and the Dijkstra algorithm have lower complexity, a...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05D1/02G01C21/20
CPCG05D1/0214G01C21/20
Inventor 张谦马向华狄逸群
Owner SHANGHAI INST OF TECH
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