City rescue intelligent agent dynamic path planning method based on improved ant colony algorithm

Abstract

The invention provides a city rescue intelligent agent dynamic path planning method based on an improved ant colony algorithm, and belongs to the technical field of robot simulation. Aiming at the issue of path planning of a dynamic changing environment in robot rescue simulation, the classic ant colony algorithm is improved, the target dominance is introduced, the approach for calculating the probability of state transfer of ants is modified, and the pheromone updating rules adapt to a situation in which the road condition is unknown and dynamically changes, a rescue intelligent agent is given complex tasks, and the path planning requirements are inconsistent in a rescue environment, so that a rescue robot can search the optimum path according to the task attribute. Compared with the classic ant colony algorithm, the improved ant colony algorithm of the invention is effective and reliable.

Description

technical field [0001] The invention belongs to the technical field of robot simulation, and in particular relates to an improved ant colony algorithm dynamic path planning method for urban rescue agents. Background technique [0002] Uncertain factors caused by earthquakes, fires, etc. in urban rescue operations, as well as the rescue work of rescue agents, will cause continuous changes in urban road conditions. Therefore, searching for the optimal path for the rescue robot in a dynamically changing rescue scene and shortening the rescue operation time are the primary challenges and key issues of robot rescue. [0003] The dynamic path planning problem in robot rescue simulation can be summarized as follows: according to the actual needs of the rescue mission, the agent searches out the path from the starting node to the target node through the path planning algorithm. In the traditional path planning problem, the global environment information is known, The optimization m...

AI Technical Summary

Problems solved by technology

However, the conventional ant colony algorithm is prone to fall into local optimum, premature and slow convergence in the search process.

Different from TSP (Travelling Salesman Problem), the entire map information in the TSP problem is noiseless, while the environment in the world model of the rescue system is unknown and noisy. For example, it is impossible to determine road congestion, building burning, intelligent Due to the distribution of volumes, etc., it is impossible to obtain accurate path planning schemes in advance.

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