双向蚁群算法在无人驾驶车辆路径规划中的应用

针对传统蚁群算法在无人驾驶车辆路径规划中收敛速度慢、易陷入局部最优等问题,提出一种全局路径规划的双向蚁群算法.通过双向搜索策略改进蚁群算法,设计相遇机制求解更多可行路径,提高算法全局搜索能力;引入奖惩因子分别扩大和减小双向搜索后的较优路径和较差路径对信息素浓度的影响,加快求解最优路径的速度;最后在Matlab中模拟无人...

Application of Bidirectional Ant Colony Algorithm in Path Planning of Autonomous Vehicle

Aiming at the problems of the traditional ant colony algorithm in the path planning of autonomous vehicles,such as the convergence speed is slow,and it is easy to fall into the local optimum,a bidirectional ant colony algorithm was proposed.The ant colony algorithm was improved through the bidirectional search strategy,and the encounter mechanism was designed to solve more feasible paths and improve the algorithm’s global search ability.The reward and punishment factors were introduced to expand and reduce the influence of the better path and the worse path on the pheromone concentration respectively after bidirectional search,so as to speed up the speed of solving the optimal pat.Finally,the autonomous vehicle environment was simulated in Matlab,and vehicle simulation grid maps with different map areas and obstacle occurrence rates were generated randomly to compare the experimental effects of the traditional ant colony algorithm and the bidirectional ant colony algorithm.The results show that the bidirectional ant colony algorithm reduces the number of iterations and solution time significantly.It has great improvement in accelerating the convergence speed,in improving the global search ability and in avoiding local optimization.