Robust Robotic Fleet Management for Warehouse Operations

适用于仓库运营的强大机器人车队管理

• 批准号: 2736854
• 金额: --
• 依托单位: University of Lincoln
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2736854
• 关键词: Robust; Robotic; Fleet; Management; Warehouse

Scientific background:The demand for click-and-deliver e-commerce services for food and grocery has increased. This, in turn, has increased the demand for deploying robotic-fleets in warehouses. There are very commercial players in the field with a feasible commercial solution. In literature, researchers address either the multi-robot task allocation, ignoring the path planning or the multi-robot path planning, ignoring the task planning approaches. However, simultaneously addressing these can ensure efficient collaborations between the robots in sharing the workload of different orders and efficient collision-free multi-robot navigation. There is also a high demand for performance guarantees if robots fail during their operation or if they end up in congested situations causing delays in fulfilling their assigned tasks. This project will explore the development of robust robotic fleet management algorithms with performance guarantees for robustness while deploying in a warehouse.Research methodology:The student will investigate (i) clustering of tasks based on the subtasks (items in each order), (ii) a hybrid centralised-decentralised approach for allocation of clusters of pick and delivery, and replenishment tasks preferably using market-based approaches and (iii) associated collision-free and time and energy efficient multi-robot path planning (MRPP) using prioritised multi-robot path planning with time-windowed reservation tables. The MRPP component may also be addressed in a hybrid manner to give robustness guarantees and to ensure scalability to a large number of robots operating in large environments.Training:Forwarding the thoughts and vision from a single robot's perspective to those of a fleet of coordinated robots Understanding of mathematical optimisation problem formulation Working on physical robotic platforms Developing and working on simulated environments Programming and developing algorithms Understanding and incorporating industry partner's requirements into the research Academic writing and publishing Dissemination of research contributions to a wide range of audience

科学背景：对食品和杂货的点击和交付电子商务服务的需求有所增加。这反过来又增加了在仓库中部署机器人车队的需求。在这个领域有非常商业化的参与者，他们有一个可行的商业解决方案。在文献中，研究者要么解决多机器人任务分配，忽略路径规划，或多机器人路径规划，忽略任务规划方法。然而，同时解决这些问题可以确保机器人之间的有效协作，以分担不同订单的工作量和有效的无碰撞多机器人导航。如果机器人在运行过程中出现故障，或者如果机器人最终处于拥挤的情况下，导致其完成指定任务的延迟，则对性能保证的要求也很高。该项目将探索开发强大的机器人车队管理算法，并在仓库中部署时提供性能保证。研究方法：学生将研究（i）基于子任务（每个订单中的项目）的任务聚类，（ii）混合集中-分散方法，用于分配拣选和交付的集群，以及最好使用基于市场的方法的补充任务，（iii）使用优先级多机器人路径规划和时间窗口预留表的相关无碰撞和时间和能源效率的多机器人路径规划（MRPP）。MRPP组件也可以以混合方式来处理，以提供鲁棒性保证并确保在大型环境中操作的大量机器人的可扩展性。从单个机器人的角度将思想和愿景转发到协调机器人的角度理解数学优化问题的制定在物理机器人平台上工作开发和模拟环境编程和开发算法理解并将行业合作伙伴的要求纳入研究学术写作和出版研究传播对广泛的受众作出贡献