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Bigraph Matching Weighted with Learnt Incentive Function for Multi-Robot Task Allocation
Most real-world Multi-Robot Task Allocation (MRTA) problems require fast and efficient decision-making, which is often achieved using heuristics-aided methods such as genetic algorithms, auction-based methods, and bipartite graph matching methods.
Graph Learning-based Fleet Scheduling for Urban Air Mobility under Operational Constraints, Varying Demand & Uncertainties
This paper develops a graph reinforcement learning approach to online planning of the schedule and destinations of electric aircraft that comprise an urban air mobility (UAM) fleet operating across multiple vertiports.
Fast Decision Support for Air Traffic Management at Urban Air Mobility Vertiports using Graph Learning
This paper provides a novel approach to this problem of Urban Air Mobility - Vertiport Schedule Management (UAM-VSM), which leverages graph reinforcement learning to generate decision-support policies.
Learning to Solve Multi-Robot Task Allocation with a Covariant-Attention based Neural Architecture
This paper presents a novel graph (reinforcement) learning method to solve an important class of multi-robot task allocation (MRTA) problems that involve tasks with deadlines, and robots with ferry range and payload constraints (thus requiring multiple tours per robot).
