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Application method of adaptive ant colony algorithm (AACO) in mobile robot path planning

A mobile robot and ant colony algorithm technology, applied in the direction of adaptive control, instrumentation, general control system, etc., can solve the problems of long selection method, lack of global measurement information, easy to fall into deadlock environment, way point search time, etc., to achieve reduction The effects of convergence time, improved search efficiency, and reduced number of cycles

Active Publication Date: 2018-07-03
JINGDEZHEN CERAMIC INSTITUTE
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Problems solved by technology

However, the standard ant colony optimization method also has some problems such as: easy to fall into a deadlock environment, the search time of the path point is too long, and the selection method lacks global measurement information, etc.

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  • Application method of adaptive ant colony algorithm (AACO) in mobile robot path planning
  • Application method of adaptive ant colony algorithm (AACO) in mobile robot path planning
  • Application method of adaptive ant colony algorithm (AACO) in mobile robot path planning

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Embodiment Construction

[0088] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0089] In the present invention, the adaptive ant colony algorithm and the standard ant colony algorithm are referred to as AACO and ACO respectively.

[0090] The present invention provides an application method and system of an adaptive ant colony algorithm (AACO) in path planning of a mobile robot. The designed adaptive ant colony algorithm revolves around the path point selection method, obstacle avoidance process, Deadlock Handling Strategies The three core issues are respectively designed point-to-point adaptive path selection strategy, obstacle avoidance planning strategy, and hybrid deadlock handling strategy.

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Abstract

The invention belongs to the technical field of robot navigation, automatic control and pattern recognition, and discloses an application method of adaptive ant colony algorithm (AACO) in mobile robotpath planning. The method comprises steps of constructing a point-to-point adaptive path selection strategy to select the best path point by the transfer optimization mode in the segmentation combination state; using an obstacle avoidance planning strategy to identify the nature of the obstacle, and selecting different local obstacle avoidance points to avoid obstacles; and applying a mixed deadlock processing strategy in the deadlock environment, and counting deadlock point and its fallback path point distribution information to guide ants to jump out of the deadlock environment. The data inthe embodiment shows that the AACO described in the present invention has better optimization ability than the basic ant colony algorithm (ACO), has better overall performance than the ACO, and can be effectively applied to the global process of robot path planning.

Description

technical field [0001] The invention belongs to the technical fields of robot navigation, automatic control and pattern recognition, and in particular relates to an application method of an adaptive ant colony algorithm in path planning of a mobile robot. Specifically, the invention relates to an application method and system of an adaptive ant colony algorithm in path planning of a mobile robot in a dynamic environment. Background technique [0002] At present, the existing technology commonly used in the industry is as follows : [0003] Path planning technology is a key technology in the development of mobile robot technology. For a long time, it has been the technical focus of aerospace, deep sea exploration, geological and mineral exploration, industrial production and civil use. The main purpose of path planning technology is to study the ability of artificial intelligence technology in autonomous reasoning, planning and decision-making control of a system in variou...

Claims

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Application Information

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IPC IPC(8): G05D1/02G05B13/04
CPCG05B13/042G05D1/0088G05D1/0212
Inventor 汤可宗柳炳祥詹棠森杨利华舒云
Owner JINGDEZHEN CERAMIC INSTITUTE
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