Abstract: For the traditional ACO has the disadvantages of slow convergence,low efficiency and easy to fall into local optimum,a new variant of ACO is proposed.Firstly,a new heuristic mechanism with directional information is introduced to add directional guidance in the iterative process,which further improves the convergence speed of the algorithm.Second,an improved heuristic function is proposed to enhance the purpose of the objective and reduce the number of turns in the path.Then,an improved state transfer probability rule is introduced to improve the search efficiency and increase the population diversity.In addition,a new method of unevenly distributing the initial pheromone concentration is proposed to avoid blind search.The new ACO variant is called the modified adaptive ant colony optimization algorithm(MAACO).To verify the effectiveness of the proposed MAACO,a series of experiments are conducted with seven other existing algorithms based on three different obstacle distribution environment patterns.In all simulation experiments,the proposed MAACO generates the shortest path with zero standard deviation and achieves the minimum number of turns within the minimum convergence generation.For the three experiments,the average reduction in the number of turns compared to the best available results is two,with a typical reduction of 22.2%.Experimental results demonstrate the advantages of MAACO in reducing path length,reducing the number of turns and increasing the convergence speed and its usefulness and efficiency in path planning.

Key words: Ant colony algorithm, Heuristic function, Transfer probability, Mobile robot, Path planning