基于蚁群几何优化算法的全局路径规划

将改进的蚁群算法与路径几何优化相结合,用于解决移动机器人的全局路径规划问题.算法结合机器人的越障性能对移动机器人的环境空间进行建模.通过设置初始信息素加快蚂蚁的搜索速度,同时设置自适应信息素挥发机制,解决特定地图中初始信息素的干扰问题;设置自适应路径长度,筛选规划路径的优劣;提出由路径优劣程度决定的信息素散播策略,并从几何原理出发,对规划路径进行优化处理,加快最优解的收敛速度.仿真结果验证了该算法的有效性和普遍应用性,在随机给定的环境地图中,该算法能够迅速规划出最优路径.

Global Path Planning Based on Improved Ant Colony Optimization Algorithm for Geometry

The improved ant colony algorithm and path geometry optimization were applied to solve the global path planning problem of mobile robot. The obstacle performance was combined in the proposed algorithm to establish the workspace model of the robot. By setting the initial pheromone， the ant searching speed was accelerated， and through the adaptive pheromone mechanism， the interference problem of initial pheromone to the specific map was solved. In addition， the pros and cons of the path planning were screened by setting the adaptive path length. It was also proposed that the pheromone spreading strategy was decided by the path length. Meanwhile， according to the principle of geometry， the planning path was optimized to accelerate the convergence speed of the optimal solution. The effectiveness and universal application of the proposed algorithm was demonstrated by the simulation results. In the random environment map， the optimal path could be rapidly obtained with the proposed algorithm.