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pro vyhledávání: '"PAUL, STEVE"'
The advent of Urban Air Mobility (UAM) presents the scope for a transformative shift in the domain of urban transportation. However, its widespread adoption and economic viability depends in part on the ability to optimally schedule the fleet of airc
Externí odkaz:
http://arxiv.org/abs/2407.12113
Autor:
KrisshnaKumar, Prajit, Paul, Steve, Manjunatha, Hemanth, Corra, Mary, Esfahani, Ehsan, Chowdhury, Souma
The collective performance or capacity of collaborative autonomous systems such as a swarm of robots is jointly influenced by the morphology and the behavior of individual systems in that collective. In that context, this paper explores how morpholog
Externí odkaz:
http://arxiv.org/abs/2406.16612
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. Th
Externí odkaz:
http://arxiv.org/abs/2403.07131
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. This fleet scheduling problem i
Externí odkaz:
http://arxiv.org/abs/2401.04851
Autor:
KrisshnaKumar, Prajit, Witter, Jhoel, Paul, Steve, Cho, Hanvit, Dantu, Karthik, Chowdhury, Souma
Urban Air Mobility (UAM) promises a new dimension to decongested, safe, and fast travel in urban and suburban hubs. These UAM aircraft are conceived to operate from small airports called vertiports each comprising multiple take-off/landing and batter
Externí odkaz:
http://arxiv.org/abs/2308.09075
Efficient multi-robot task allocation (MRTA) is fundamental to various time-sensitive applications such as disaster response, warehouse operations, and construction. This paper tackles a particular class of these problems that we call MRTA-collective
Externí odkaz:
http://arxiv.org/abs/2303.08933
Majority of aircraft under the Urban Air Mobility (UAM) concept are expected to be of the electric vertical takeoff and landing (eVTOL) vehicle type, which will operate out of vertiports. While this is akin to the relationship between general aviatio
Externí odkaz:
http://arxiv.org/abs/2302.05849
This paper presents a novel graph reinforcement learning (RL) architecture to solve multi-robot task allocation (MRTA) problems that involve tasks with deadlines and workload, and robot constraints such as work capacity. While drawing motivation from
Externí odkaz:
http://arxiv.org/abs/2205.03321