Abstract

Abstract: We address the Warehouse Servicing Problem (WSP) in automated warehouses, which use teams of mobile robots to move products from shelves to packaging stations. Given a list of products, the WSP amounts to finding a motion plan which brings every product on the list from a shelf to a packaging station within a given time limit. The WSP consists of four subproblems, namely, deciding where to source and deposit a product (task formulation), who should transport each product (task assignment) and when (scheduling) and how (motion planning). These problems are NP-Hard individually and made more challenging by their interdependence. The difficulty of the WSP is compounded by the scale of automated warehouses, which use teams of hundreds of agents to transport thousands of products. In this paper, we present Contract-based Cyclic Motion Planning (CCMP), a novel contract-based methodology for solving the WSP at scale. CCMP decomposes a warehouse into a set of traffic system components. By assigning each component a contract which describes the traffic flows it can support, CCMP can generate a traffic flow which satisfies a given WSP instance. CCMP then uses a novel motion planner to transform this traffic flow into a motion plan for a team of robots. Evaluation shows that CCMP can solve WSP instances taken from real industrial scenarios with up to 1 million products while outperforming other methodologies for solving the WSP by up to 2.9x.

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