Novel Dynamic Vehicle Scheduling and Path Planning Algorithms for Mobile Robotic and AGV Warehouse Order Picking

工厂自动化将机器人与操作员分开。开发软机器人技术使人类能够与自动移动的机器人（AMR）密切合作，充分利用人类的操纵和决策适应技能。调度AMR运动的区域也被人类占领提供了一个关键组成部分，在这种能力。该项目的需求是由10年来互联网购物的大规模增长驱动的，互联网购物对小订单的组装产生了巨大影响，多渠道销售深刻影响了仓库设计。拣货作业通常是库存被动成本背后最高的活动成本要素。对效率和准确性的需求正在推动该行业采用各种形式的拣选自动化。行业专家预计，未来的解决方案将是视觉引导、机器人分拣和穿梭机漫游设施，在自动化材料处理、机器人和人类混合的环境中进行交付。这只能完全实现，如果适当的调度系统的发展。这个为期12个月的项目旨在证明一种高度新颖的动态AMR调度解决方案的技术可行性，该解决方案能够优化在软机器人环境中运行的大型AMR车队（>50）的调度。该项目将开发进度控制算法，并在受控工作空间和小型代表性最终用户试验中证明其可行性。该解决方案将优化大型AMR车队的路线，使每个订单的行程时间最小化，同时其定时到达拣选地点，最小化操作员在拣选点之间的步行距离，同时也确保没有车辆碰撞和最短的等待时间。指导已经与创新的高增长的分销业务，以提供最终用户洞察力和代表性的试验设施，是利用特定的时间表算法专业知识从伯明翰大学计算机科学学院作为分包商。

Factory automation separates robots from operators. Developing soft robotics enables humansto work in close collaboration with Automated Mobile Robot’s (AMR’s), leveraging ahumans full manipulative & decision-adaptive skills. Scheduling AMR movement in areasalso occupied by humans provides a key component in this capability. The projectrequirement is driven by 10 yrs of massive growth in internet shopping that has had a hugeimpact on the assembly of small orders & with multi-channel selling has profoundly affectedwarehouse design. Order picking is usually the highest active cost element behind the passivecost of inventory. The need for efficiency & accuracy is driving the sector to various forms ofpick automation. Industry experts expect future solutions to be vision guided, robotic piecepicking & shuttles roaming a facility to make a delivery in an environment where automatedmaterials handling, robotics & humans intermingle. This can only be fully realised ifappropriate scheduling systems are developed. The 12 month project aims to prove thetechnical feasibility of a highly novel dynamic AMR scheduling solution, capable ofoptimising the schedule of a large fleet (>50) of AMR’s operating in a soft roboticsenvironment. The project will develop the schedule control algorithms & demonstrate thefeasibility in both a controlled work space & in a small representative end user trial. Thescheduling solution will optimise the route of a large fleet of AMR’s so the time of travel foreach order is minimised while its timed arrival at the picking locations minimises the distancewalked between picking points by the operator, while also ensuring no vehicle collisions &minimum waiting times.Guidance has engaged with an innovative high growth distribution business to provide enduserinsight & representative trial facilities & is utilising specific schedule algorithm expertisefrom the University of Birmingham School of Computer Science as sub-contractors.