考虑冲突避免的多AGV路径规划研究

目的 提高物流企业“货到人”拣选系统在实际生产中的工作效率,避免自动导引小车(AGV)间的冲突死锁,研究大规模多AGV的无冲突路径规划和协同避障问题。方法 首先考虑AGV空载、负载情况和路径扩展成本,改进A^*算法,动态调整代价函数,优化路径扩展方式。其次,提出冲突检测及避免算法,对可能产生局部冲突的路径交叉点进行避障调度,通过预约锁格,实现局部冲突的检测,制定优先级避障策略,解决AGV动态行驶路径上产生的局部冲突和死锁,进而实现全局无冲突路径规划。结果 对多组不同任务量和不同AGV规模的场景进行仿真,实验结果表明,考虑冲突避免的改进A^*算法能有效实现100个任务、90个货架单位和7个拣选站场景下的多AGV动态路径规划,相较于传统A^*算法,其平均拣选时长缩短了52.61%。结论 该方法可实现大规模场景下的多AGV动态路径规划,在付出较小转弯代价的同时有效避免局部动态冲突,该方法可为相关企业实现多AGV协同调度提供新的思路和理论依据。

Multi-AGV Path Planning Considering Conflict Avoidance

The work aims to improve the efficiency of the "goods to people" picking system in logistics enterprises during actual production, avoid conflict deadlock between automatic guided vehicles (AGVs), and study the conflict free path planning and collaborative obstacle avoidance problem of large-scale multi AGVs. Firstly, A algorithm was improved considering the empty load, load situation, and path expansion cost of AGV, the cost function was adjusted dynamically and the path expansion method was optimized. Then, a conflict detection and avoidance algorithm was proposed, which scheduled path intersections that might generate local conflicts. Local conflict detection was achieved through reserved lock grids, and priority obstacle avoidance strategies were developed to solve local conflicts and deadlocks generated on AGV dynamic driving paths, to achieve global conflict free path planning. Multiple scenarios with different task volumes and AGV scales were simulated. The experimental results showed that the improved A algorithm considering conflict avoidance could effectively achieve dynamic path planning for multiple AGVs in scenarios with 100 tasks, 90 shelf units, and 7 picking stations. Compared to the traditional A algorithm, the average picking time was optimized by 52.61%. This method can achieve dynamic path planning for multiple AGVs in large-scale scenarios, effectively avoiding local dynamic conflicts while paying less turning costs. This method can provide new ideas and theoretical basis for relevant enterprises to achieve collaborative scheduling of multiple AGVs.