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：对单击和杂货服务对食品和杂货的需求增加。反过来，这增加了对在仓库中部署机器人呢的需求。该领域的商业参与者具有可行的商业解决方案。在文献中，研究人员要么讨论多机器人任务分配，忽略了路径计划或多机器人路径计划，而忽略了任务计划方法。但是，同时解决这些问题可以确保机器人之间在共享不同订单的工作量和有效的无碰撞多机器人导航方面有效合作。如果机器人在操作过程中或最终处于拥挤的情况下导致延迟完成分配的任务，则对性能保证的需求也很高。该项目将探索在部署在仓库中的稳健性的稳健机器人管理算法的开发。研究方法：学生将根据子任务（每次订单中的项目）（项目）（ii）相关的中心化方法，并使用挑选的秘密和重建任务，调查（i）基于子任务的任务聚类（i）无碰撞，时间和节能的多机器人路径计划（MRPP）使用优先的多机器人路径计划，并带有时间窗口的预订表。 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对广泛受众的研究贡献的撰写和发布传播