Cooperative route planning of multiple fuel-constrained Unmanned Aerial Vehicles with recharging on an Unmanned Ground Vehicle

Ramasamy, Subramanian; Reddinger, Jean-Paul; Dotterweich, James; Childers, Marshal A.; Bhounsule, Pranav A.

doi:10.1109/icuas51884.2021.9476848

Cited by 11 publications

(6 citation statements)

References 10 publications

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“…They created a hybrid heuristic that combined nearest neighbor and costcutting strategies to quickly build a viable solution. In our previous research [11], [12], we explored a hierarchical bi-level optimization framework for cooperative routing of multiple fuel-constrained UAVs and a single UGV. The framework employs K-means clustering at the outer level to generate UGV visit points, which can be connected using the traveling salesman problem (TSP) to obtain the UGV route.…”

Section: A Related Workmentioning

confidence: 99%

A Bilevel Optimization Framework For fuel-constrained UAV-UGV Cooperative Routing: Planning and Experimental Validation

Mondal¹,

Subramanian²,

Bhounsule³

2023

Preprint

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Fast moving unmanned aerial vehicles (UAVs) are well suited for aerial surveillance, but are limited by their battery capacity. To increase their endurance UAVs can be refueled on slow moving unmanned ground vehicles (UGVs). The cooperative routing of UAV-UGV to survey vast regions within their speed and fuel constraints is a computationally challenging problem, but can be simplified with heuristics. Here we present multiple heuristics to enable feasible and sufficiently optimal solutions to the problem. Using the UAV fuel limits and the minimum set cover algorithm, the UGV refueling stops are determined. These refueling stops enable the allocation of mission points to the UAV and UGV. A standard traveling salesman formulation and a vehicle routing formulation with time windows, dropped visits, and capacity constraints is used to solve for the UGV and UAV route, respectively. Experimental validation of the approach on a small-scale testbed shows the efficacy of the approach.

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Section: A Related Workmentioning

confidence: 99%

A Bilevel Optimization Framework For fuel-constrained UAV-UGV Cooperative Routing: Planning and Experimental Validation

Mondal¹,

Subramanian²,

Bhounsule³

2023

Preprint

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“…On the other hand, the second approach is to consider the battery consumption of UAVs simply instead of determining the inaccurate battery consumption model. Ramasamy et al solved the routing problem of multiple fuel-constrained UAVs by recharging from a single unmanned ground vehicle to overcome the UAV's limited battery capacity [19]. In their problem, it was considered that the battery level of UAV decreases proportional to the traveled distance.…”

Section: Related Workmentioning

confidence: 99%

Extensions of Receding Horizon Task Assignment for Area Coverage in Dynamic Environments

Hong

Jung

Kim

et al. 2023

IEEE Trans. Aerosp. Electron. Syst.

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This study presents a task reassignment strategy that can flexibly handle changes in dynamic environments. Most existing studies have only considered static environments where the mission is predetermined and unexpected events do not occur. However, in practice, the environment in which unmanned aerial vehicles perform their mission is complex and includes various pop-up events. Therefore, task reassignment is essential through re-optimization to adjust the previous plan, which is no longer optimal whenever dynamic events occur. The importance of this study is that the modified receding horizon task assignment algorithm is applied for accurate and efficient task reassignment. In addition, this study aims to provide a complete system architecture from operator input to motor commands for autonomous area coverage missions. Reliable performance validation is also performed through various simulations and a challenging outdoor flight experiment with real hardware implementation.

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“…Constraint (13) forces variable L k to equal the distance of sub-tour k. Constraint ( 14) adapts a sub-tour speed based on the distance of sub-tour k and is derived from Eqn. (11).…”

Section: Minlp Formulation For the Fixed-mdmthppmentioning

confidence: 99%

Energy-Aware Drone Path Finding with a Fixed-Trajectory Ground Vehicle

Diller,

Han

2023

Preprint

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Rotary-wing Unmanned Aerial Vehicles (commonly referred to as drones) are a versatile platform that can be used for many data collection applications including emergency response, environmental monitoring, surveillance, and many others. In this work, we investigate how to plan efficient paths that minimize mission completion time for drone data collection where the drone must rendezvous with a moving ground vehicle that cannot stop and wait for the drone. More importantly, we address the limited onboard energy storage issue by adapting drone speed. We propose a mixed-integer nonlinear program solution to solve this problem optimally and provide various alternative solutions. We evaluate these approaches in extensive simulations using real drone characteristics to highlight their trade-offs. We show that our proposed solutions can greatly reduce mission completion time when compared to other baseline approaches and demonstrate the importance of drone speed adaptation in path finding for drones. We also prototype our solution on a physical drone testbed to show how the problem setup can be applied practically.

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Cooperative route planning of multiple fuel-constrained Unmanned Aerial Vehicles with recharging on an Unmanned Ground Vehicle

Cited by 11 publications

References 10 publications

A Bilevel Optimization Framework For fuel-constrained UAV-UGV Cooperative Routing: Planning and Experimental Validation

A Bilevel Optimization Framework For fuel-constrained UAV-UGV Cooperative Routing: Planning and Experimental Validation

Extensions of Receding Horizon Task Assignment for Area Coverage in Dynamic Environments

Energy-Aware Drone Path Finding with a Fixed-Trajectory Ground Vehicle

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