基于势场蚁群算法的仓储搬运机器人避障控制方法

Obstacle Avoidance Control Method for Warehouse Handling Robots Based on Potential Ant Colony Algorithm

针对现有仓储搬运机器人避障控制算法存在的路径寻优易陷入局部最优解,及多机器人同时作业易发生碰撞等问题,对物流机器人的避障控制进行了研究,并提出一种基于改进势场蚁群的控制算法;对机器人搬运过程中的移动轨迹进行了研究,给出了机器人空间运动学方程;采用了蚁群算法对经典人工势场算法进行优化,提升全局寻优能力并平衡引力和斥力的相互作用关系;在仓储搬运机器人的局部区域避障方面,基于策略梯度算法对人工势场做二次优化,通过分析下一动作指令的发生概率,改善多机器人同时作业时行进路径选择的随机性;经测试,提出控制算法的路径最短,完成单次运输任务耗时仅为12.3 s,而且在复杂路径规划条件下,机器人之间发生碰撞的次数也显著少于传统避障控制算法,经实际应用能够满足提升仓储物流管理效率的需求。

Existing obstacle avoidance control algorithms for warehouse handling robots are prone to path optimization and falling into local optimization,and multiple robot operations are prone to collisions simultaneously.To address these issues,,this paper studies the obstacle avoidance control algorithm of warehouse handling robots,proposes a control algorithm based on potential ant colony,researches the moving trajectory of the robot in the process of transport,and describes the space kinematics equation.The ant colony algorithm is used to optimize the classical artificial potential field algorithm,improve the global optimization ability and balance the relationship between gravity and repulsion.In local obstacle avoidance of warehouse handling robots,the artificial potential field undergoes the secondary optimization based on the strategy gradient algorithm,which improves the randomness of path selection during multiple robots operating by analyzing the occurrence probability of the next operation instruction.After testing,the results show that the proposed control algorithm has the shortest path,and it takes only 12.3 s to complete single transportation task.Moreover,under the complex path planning conditions,the number of collisions between robots is significantly lower than that of the traditional obstacle avoidance control algorithms,which can improve the efficiency of warehousing and logistics management through practical application.