动态环境下改进蚁群算法的多Agent路径规划

针对动态环境下的多Agent路径规划问题,提出了一种改进的蚁群算法与烟花算法相结合的动态路径规划方法。通过自适应信息素强度值及信息素缩减因子来加快算法的迭代速度,并利用烟花算法来解决路径规划过程中的死锁问题,避免陷入局部最优。在多Agent动态避碰过程中,根据动态障碍物与多Agent之间的运行轨迹是否相交制定相应的避碰策略,并利用路径转变函数解决多Agent的正面碰撞问题。仿真实验表明,该方法优于经典蚁群算法,能够有效解决多Agent路径规划中的碰撞问题,从而快速找到最优无碰路径。

An improved ant colony algorithm for multi-agent path planning in dynamic environments

Aiming at the problem of multi-agent path planning in dynamic environments, we propose an improved dynamic path planning method by combining the ant colony algorithm and fireworks algorithm. This method accelerates the iteration speed of the algorithm by adapting the pheromone intensity value and the pheromone reduction factor, and uses the fireworks algorithm to solve the deadlock problem in the path planning process and avoid falling into a local optimum. In the process of multi-agent dynamic collision avoidance, corresponding collision avoidance strategies are made according to whether the motion trajectory between dynamic obstacles and multi agent intersects, and the path collision function is used to solve the multi-agent frontal collision problem. Simulation results show that the proposed algorithm is superior to the traditional ant colony algorithm. It can effectively solve the collision problem in multi agent path planning, and quickly find the optimal collision-free path.