Multi-Agent Planning for Persistent Missions with Automated Battery Management

Redding, Joshua; Toksoz, Tuna; Üre, Nazım Kemal; Geramifard, Alborz; How, Jonathan P.; Vavrina, Matthew A.; Vian, John

doi:10.2514/6.2011-6480

Cited by 9 publications

(3 citation statements)

References 14 publications

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“…They used charging platforms for the UAVs for truly persistent missions. Boeing's Vehicle Swarm Technology Laboratory (VSTL) [16], [21]- [23] and MIT's RAVEN laboratory [24] testbed have conducted significant UAV flight testing demonstrations in indoor lab scale setups.…”

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

View full text Add to dashboard Cite

show abstract

“…22 Valenti et al 23 consider that the persistent surveillance problem belongs to the coordination of resources and present a health management technique for 24/7 persistent surveillance trying to keep at least one UAV in a target area. Redding et al 24 introduce an automated battery management for longduration mission of multiple agents. Zhang et al 25 provide methods of energy conservation and energy balance for prolonging the lifetime of the group of agents in order to collect data continuously.…”

Section: Related Workmentioning

confidence: 99%

Potential field method for persistent surveillance of multiple unmanned aerial vehicle sensors

Huang

Xiao

et al. 2018

International Journal of Distributed Sensor Networks

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Persistent surveillance is one of the major tasks envisioned for unmanned aerial vehicles in that some regions of interest need to be continuously surveyed. This distinction from the one-time coverage/exploration does not allow a straightforward application of the most exploration techniques to address the persistent surveillance problem. This article introduces and demonstrates a method of alterable artificial potential field to control a group of unmanned aerial vehicles to stay over the region of interest. We present attractive potential field to drive the unmanned aerial vehicles to desirable areas, obstacle potential field to move away from obstacles, collision avoidance potential field to control unmanned aerial vehicles not to collide with others, and formation of potential field to make unmanned aerial vehicles gather in a group. The simulation results show that the proposed approach could generate collision-free path for unmanned aerial vehicles staying over the region of interest for a long endurance.

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“…Mario et al 12 present the health management techniques for UAVs performing 24/7 persistent surveillance operations. Joshua et al 13 discuss the problem of power management for persistent missions of multiple UAVs. Howden et al 14 provide a collective intelligence algorithm to survey complex areas for fires.…”

Section: Related Workmentioning

confidence: 99%

Persistent surveillance for a swarm of micro aerial vehicles by flocking algorithm

2014

Proceedings of the Institution of Mechanical Engineers, Part G:

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Persistent surveillance is a major role envisioned for autonomous unmanned vehicles. The mission of persistent surveillance requires the vehicles to continuously survey a target region. This paper investigates the techniques of persistent surveillance control for a swarm of micro aerial vehicles. We present a flocking algorithm to drive the micro aerial vehicles flying in a coordinate formation with a capability of obstacle avoidance. We propose a new digital pheromone mechanism to control and coordinate the swarms of micro aerial vehicles to search a field of interest and to reduce the uncertainty of every region in the field over time. Simulation results show the effectiveness of our proposed algorithm in generating collision-free persistent surveillance trajectories for a swarm of micro aerial vehicles in a coordinated manner.

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Multi-Agent Planning for Persistent Missions with Automated Battery Management

Cited by 9 publications

References 14 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

Potential field method for persistent surveillance of multiple unmanned aerial vehicle sensors

Persistent surveillance for a swarm of micro aerial vehicles by flocking algorithm

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