A Reinforcement Learning Approach for Re-allocating Drone Swarm Services

Alkouz, Balsam; Bouguettaya, Athman

doi:10.1007/978-3-030-91431-8_40

Cited by 13 publications

(4 citation statements)

References 17 publications

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“…Choi et al [37] propose a drone multipackage delivery focusing on battery and payload constraints, albeit overlooking the on-demand perspective. In [38], authors propose a drone swarm redistribution approach using a centralized policy. Although our approach does not perform explicit multi-agent path planning, it supports training agents with multiple timescale autonomy systems to account for path planning, low-level control, and trajectory optimization hierarchically.…”

Section: Autonomous Mobility Fleet Coordinationmentioning

confidence: 99%

Graph Attention Multi-Agent Fleet Autonomy for Advanced Air Mobility

Fernando¹,

Senanayake²,

Choi³

et al. 2023

Robotics: Science and Systems XIX

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Autonomous mobility is emerging as a new disruptive mode of urban transportation for moving cargo and passengers. However, designing scalable autonomous fleet coordination schemes to accommodate fast-growing mobility systems is challenging primarily due to the increasing heterogeneity of the fleets, time-varying demand patterns, service area expansions, and communication limitations. We introduce the concept of partially observable advanced air mobility games to coordinate a fleet of aerial vehicles by accounting for the heterogeneity of the interacting agents and the self-interested nature inherent to commercial mobility fleets. To model the complex interactions among the agents and the observation uncertainty in the mobility networks, we propose a novel heterogeneous graph attention encoder-decoder (HetGAT Enc-Dec) neural networkbased stochastic policy. We train the policy by leveraging deep multi-agent reinforcement learning, allowing decentralized decision-making for the agents using their local observations. Through extensive experimentation, we show that the learned policy generalizes to various fleet compositions, demand patterns, and observation topologies. Further, fleets operating under the HetGAT Enc-Dec policy outperform other state-of-the-art graph neural network policies by achieving the highest fleet reward and fulfillment ratios in on-demand mobility networks.

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Section: Autonomous Mobility Fleet Coordinationmentioning

confidence: 99%

Graph Attention Multi-Agent Fleet Autonomy for Advanced Air Mobility

Fernando¹,

Senanayake²,

Choi³

et al. 2023

Robotics: Science and Systems XIX

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“…In addition, it could achieve tasks that a single drone is not capable of doing. This includes fascinating sky shows [10] and the delivery of multiple packages to the same destination at the same time [27]. Swarm-based drone delivery is an emerging application that has been recently explored in the literature [7].…”

Section: Swarm-based Drone Servicesmentioning

confidence: 99%

In-Flight Energy-Driven Composition of Drone Swarm Services

Alkouz¹,

Abusafia²,

Lakhdari³

et al. 2022

Preprint

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We propose a novel framework for swarm-based drone delivery services with in-flight energy recharging. The framework aims to enhance the delivery time of multiple packages by reducing the number of stops and recharging times at intermediate stations.The proposed framework considers various intrinsic and extrinsic delivery constraints. We propose to use support drones whose sole purpose is to recharge other drones in the swarm during their flight. In this respect, we compute the optimal set of optimal support drones to minimize the probability of delivery services and recharging time at the next stations. We also use two settings to position the support drones in a flight formation for comparative purposes. Two novel energy sharing methods are proposed, namely, Priority-based and Fairness-based methods. A re-ordering method of the delivery drones is presented to facilitate the in-flight energy composition process. An enhanced A* algorithm is implemented to compose the optimal services in terms of delivery time. Experimental results prove the efficiency of our proposed approach.

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“…Choi et al [18] proposes a drone multi-package delivery with focus on battery and payload constraints, albeit overlooking the ondemand prospective. In [19] authors propose a drone swarm redistribution approach using a centralized policy.…”

Section: A Autonomous Mobility Fleet Coordinationmentioning

confidence: 99%

CoCo Games: Graphical Game-Theoretic Swarm Control for Communication-Aware Coverage

Fernando

Senanayake

Swany

2022

IEEE Robot. Autom. Lett.

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Autonomous mobility is emerging as a new mode of urban transportation for moving cargo and passengers. However, such fleet coordination schemes face significant challenges in scaling to accommodate fast-growing fleet sizes that vary in their operational range, capacity, and communication capabilities. We introduce the concept of partially observable advanced air mobility games to coordinate a fleet of aerial vehicle agents accounting for their heterogeneity and self-interest inherent to commercial mobility fleets. We propose a novel heterogeneous graph attention-based encoder-decoder (HetGAT Enc-Dec) neural network to construct a generalizable stochastic policy stemming from the inter-and intra-agent relations within the mobility system. We train our policy by leveraging deep multiagent reinforcement learning, allowing decentralized decisionmaking for the agents using their local observations. Through extensive experimentation, we show that the fleets operating under the HetGAT Enc-Dec policy outperform other state-of-theart graph neural network-based policies by achieving the highest fleet reward and fulfillment ratios in an on-demand mobility network.

show abstract

A Reinforcement Learning Approach for Re-allocating Drone Swarm Services

Cited by 13 publications

References 17 publications

Graph Attention Multi-Agent Fleet Autonomy for Advanced Air Mobility

Graph Attention Multi-Agent Fleet Autonomy for Advanced Air Mobility

In-Flight Energy-Driven Composition of Drone Swarm Services

CoCo Games: Graphical Game-Theoretic Swarm Control for Communication-Aware Coverage

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