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In order to solve the problems of ant colony algorithm in the path planning of mobile robots, such as slow convergence speed and easy to fall into local optimum, an improved algorithm of adaptive ant colony algorithm is proposed in this paper. The algorithm improves the ant’s transition probability function by adding the number of revolutions and the angle of rotation to the heuristic function, and adding the target guide element, which can make the algorithm converge faster. The pheromone update formula is based on the wolfs food competition and distribution principle, which adaptively updates the pheromone volatility that satisfies the Cauchy distribution to avoid the local optimal solution found by the algorithm. Finally, by comparing and analyzing the statistical data results obtained by simulation, it can be concluded that the improved ant colony algorithm in this paper is better.

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