仓储物流机器人多启发蚁群路径规划与避障

Warehousing Logistics Robot Path Planning and Anti-Obstacle Based on Multi-Inspiring Ant Colony Algorithm

为了实现仓储物流机器人在动态环境下高效安全工作,提出了基于多启发蚁群算法的全局路径规划方法和基于滚动窗口的避障策略。针对仓储物流环境中的障碍物特点进行了分析和分类,建立了环境的动态栅格模型。为了提高蚁群算法初期搜索效率,给出了信息素的梯度分布初始化方法;同时引入距离启发因子和平滑启发因子,提出了多启发因群算法,并用于全局路径规划。根据机器人和工作人员不同的行为特点,制定了基于滚动窗口理论的动态避障策略。经验证,与传统蚁群算法比,多启发蚁群算法规划的路径长度更短、拐点更少、收敛更快;在仓储物流动态环境下,滚动窗口避障策略能够保证机器人沿较优路径安全行驶,证明了避障策略的可行性。

In order to realize the efficient and safe work of warehousing and logistics robot in dynamic environment,a global path planning method based on multi heuristic ant colony algorithm and an obstacle avoidance strategy based on rolling window are proposed.According to the characteristics of obstacles in warehousing and logistics environment,the dynamic grid model of environment is established.In order to improve the initial search efficiency of ant colony algorithm,a gradient distribution initialization method of pheromone is given;At the same time,distance heuristic factor and smoothing heuristic factor are introduced,and a multi heuristic group algorithm is proposed for global path planning.According to the different behavior characteristics of robot and staff,a dynamic obstacle avoidance strategy based on rolling window theory is formulated.Compared with the traditional ant colony algorithm,the multi heuristic ant colony algorithm has shorter path length,fewer inflection points and faster convergence;In the dynamic environment of warehousing and logistics,the rolling window obstacle avoidance strategy can ensure the robot to travel safely along the optimal path,which proves the feasibility of the obstacle avoidance strategy.