基于改进型蚁群算法的灭火机器人路径规划研究

Path planning research for fire-fighting robot based on improved ant colony algorithm

在传统蚁群算法基础上,采用随机选择和惯性保持相结合的方式搜寻节点,在获得不同路径的同时提高算法收敛速度。从已获得的路径两端沿惯性方向逼近优化,将无障碍中间节点剔除,减少机器人在最短路径上转弯次数的同时增强算法的搜索性能。通过自适应方式动态调整信息素,改善算法适应能力。仿真结果表明,通过以上改进能有效提升路径质量,可有效降低灭火机器人在室内环境中寻找火源的时间,提高灭火效率。

Based on the traditional ant colony algorithm, in order to gain different paths meanwhile improve the algorithm convergence speed, random select and inertia ways to search for nodes are adopt. From the ends of the acquired path optimized the robot along the direction of inertia, removed the barrier-free intermediate nodes, so as to reduce the robot′s turning times on the shortest path and simultaneously to increase the search performance of algorithm. According to adaptive approach, the robort will dynamically adjust the pheromone, in the meantime, improve its algorithm adaptability. The simulation results indicate that the above improvement can effectively improve the path quality, and reduce the fire-fighting robot′ s seeking time of fire source in indoor environment so as to improve fire extinguishing efficiency.