【摘要】 为降低室内火灾带来的损失，设计了一款智能消防机器人使其具备巡检、灭火等功能，通过改进蚁群算法实现消防机器人避障功能，并针对经典蚁群算法稳定性较弱、迭代效率偏低等问题进行改进。通过重构概率决策机制，引入转角惩罚函数，并采用B样条算法对路径进行二次优化;同时还采用了信息素双机制挥发规则以扩展算法在前期的搜索范围，增强后期的收敛效率。实验结果表明:改进蚁群算法相较于传统蚁群算法在路径搜索效率和收敛速度上表现得更加优越;与传统蚁群算法相比，所提方案平均拐点数量降低了约47%，运行时间和路径长度也有所优化。更多还原

【Abstract】 In order to reduce the loss caused by indoor fire,an intelligent fire-fighting robot is designed to have the functions of inspection and fire extinguishing,and the obstacle avoidance function of the fire-fighting robot is realized by improving the ant colony algorithm,and improvements are made to address the problems of weak stability and low iteration efficiency of the classical ant colony algorithm. By reconstructing the probabilistic decision-making mechanism,the corner penalty function is introduced,and the B-spline algorithm is used to optimize the path twice. At the same time,the pheromone dual-mechanism volatilization rule is used to expand the search range of the algorithm in the early stage and enhance the convergence efficiency in the later stage. Experimental results show that the improved ant colony algorithm is superior to the traditional ant colony algorithm in terms of path search efficiency and convergence speed. Compared with the traditional ant colony algorithm,the average number of inflection points in the proposed scheme is reduced by about 47%,and the running time and path length are also optimized.更多还原