Author name cluster

Christopher Leet

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

2 author rows

IROS Conference 2025 Conference Paper

An Anytime, Scalable and Complete Algorithm for Embedding a Manufacturing Procedure in a Smart Factory

Christopher Leet
Aidan Sciortino
Sven Koenig

Modern automated factories increasingly run manufacturing procedures using a matrix of programmable machines, such as 3D printers, interconnected by a programmable transport system, such as a fleet of tabletop robots. To embed a manufacturing procedure into a smart factory, an operator must: (a) assign each of its processes to a machine and (b) specify how agents should transport parts between machines. The problem of embedding a manufacturing process into a smart factory is termed the Smart Factory Embedding (SFE) problem. State-of-the-art SFE solvers can only scale to factories containing a couple dozen machines. Modern smart factories, however, may contain hundreds of machines. We fill this hole by introducing the first highly scalable solution to the SFE, TSACES, the Traffic System based Anytime Cyclic Embedding Solver. We show that TS-ACES is complete and can scale to SFE instances based on real industrial scenarios with more than a hundred machines.

Details

ICRA Conference 2025 Conference Paper

Jointly Assigning Processes to Machines and Generating Plans for Autonomous Mobile Robots in a Smart Factory

Christopher Leet
Aidan Sciortino
Sven Koenig

A modern smart factory runs a manufacturing procedure using a collection of programmable machines. Typically, materials are ferried between these machines using a team of mobile robots. To embed a manufacturing procedure in a smart factory, a factory operator must a) assign its processes to the smart factory's machines and b) determine how agents should carry materials between machines. A good embedding maximizes the smart factory's throughput; the rate at which it outputs products. Existing smart factory management systems solve the aforementioned problems sequentially, limiting the throughput that they can achieve. In this paper we introduce ACES, the Anytime Cyclic Embedding Solver, the first solver which jointly optimizes the assignment of processes to machines and the assignment of paths to agents. We evaluate ACES and show that it can scale to real industrial scenarios.

Details

IROS Conference 2023 Conference Paper

Task Assignment, Scheduling, and Motion Planning for Automated Warehouses for Million Product Workloads

Christopher Leet
Chanwook Oh
Michele Lora
Sven Koenig
Pierluigi Nuzzo 0002

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. 9×.

Details

AAAI Conference 2022 Conference Paper

Shard Systems: Scalable, Robust and Persistent Multi-Agent Path Finding with Performance Guarantees

Christopher Leet
Jiaoyang Li
Sven Koenig

Modern multi-agent robotic systems increasingly require scalable, robust and persistent Multi-Agent Path Finding (MAPF) with performance guarantees. While many MAPF solvers that provide some of these properties exist, none provides them all. To fill this need, we propose a new MAPF framework, the shard system. A shard system partitions the workspace into geographic regions, called shards, linked by a novel system of buffers. Agents are routed optimally within a shard by a local controller to local goals set by a global controller. The buffer system novelly allows shards to plan with perfect parallelism, providing scalability. A novel global controller algorithm can rapidly generate an inter-shard routing plan for thousands of agents while minimizing the traffic routed through any shard. A novel workspace partitioning algorithm produces shards small enough to replan rapidly. These innovations allow a shard system to adjust its routing plan in real time if an agent is delayed or assigned a new goal, enabling robust, persistent MAPF. A shard system’s local optimality and optimized inter-shard routing bring the sum-ofcosts of its solutions to single-shot MAPF problems to between 25% and 70% of optimal on a diversity of workspaces. Its scalability allows it to plan paths for thousands of agents in seconds. If any of their goals change or move actions fails, a shard system can replan in under a second.

PDF Details