M2M solves the many-to-many MAPD problem with two variants and outperforms prior one-to-one methods by completing up to 22,000 more tasks on average in 8-hour warehouse simulations.
Task and path planning for multi- agent pickup and delivery
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VCST-RCP reduces multi-robot delivery fleet travel distance by 31% on average by routing packages through a Voronoi-constrained Steiner tree relay backbone rather than direct source-to-destination paths.
ARMATA is a new end-to-end autoregressive model with multi-stage decoding that unifies allocation and routing for multi-agent systems and reports up to 20% better solutions than OR-Tools, CPLEX, and LKH-3 in seconds instead of hours.
citing papers explorer
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Many-to-Many Multi-Agent Pickup and Delivery
M2M solves the many-to-many MAPD problem with two variants and outperforms prior one-to-one methods by completing up to 22,000 more tasks on average in 8-hour warehouse simulations.
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Relay-Based Coordination for Energy-Efficient Multi-Robot Pickup and Delivery
VCST-RCP reduces multi-robot delivery fleet travel distance by 31% on average by routing packages through a Voronoi-constrained Steiner tree relay backbone rather than direct source-to-destination paths.
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ARMATA: Auto-Regressive Multi-Agent Task Assignment
ARMATA is a new end-to-end autoregressive model with multi-stage decoding that unifies allocation and routing for multi-agent systems and reports up to 20% better solutions than OR-Tools, CPLEX, and LKH-3 in seconds instead of hours.