Control and Management of an Indoor, Health Enabled, Heterogenous Fleet

Halaas, David J.; Bieniawski, Stefan R.; Pigg, Paul; Vian, John

doi:10.2514/6.2009-2036

Cited by 15 publications

(5 citation statements)

References 10 publications

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“…Persistent surveillance with efficiency, robustness, and cooperation has also been demonstrated on the VSTL testbed by the authors of this paper [166]. Boeing Research and Technology has been developing this facility to provide an environment for testing a variety of vehicles in an indoor, controlled, safe environment [194,195]. This type of facility provides a large number of flight test hours and the payload to fly is significantly reduced through off-board processing.…”

Section: Hardware Implementationsmentioning

confidence: 97%

The Multiple Unmanned Air Vehicle Persistent Surveillance Problem: A Review

Nigam

2014

Machines

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Control of autonomous vehicles for applications such as surveillance, search, and exploration has been a topic of great interest over the past two decades. In particular, there has been a rising interest in control of multiple vehicles for reasons such as increase in system reliability, robustness, and efficiency, with a possible reduction in cost. The exploration problem is NP hard even for a single vehicle/agent, and the use of multiple vehicles brings forth a whole new suite of problems associated with communication and cooperation between vehicles. The persistent surveillance problem differs from exploration since it involves continuous/repeated coverage of the target space, minimizing time between re-visits. The use of aerial vehicles demands consideration of vehicle dynamic and endurance constraints as well. Another aspect of the problem that has been investigated to a lesser extent is the design of the vehicles for particular missions. The intent of this paper is to thoroughly review the persistent surveillance problem, with particular focus on multiple Unmanned Air Vehicles (UAVs), and present some of our own work in this area. We investigate the different aspects of the problem and slightly digress into techniques that have been applied to exploration and coverage, but a comprehensive survey of all the work in multiple vehicle control for search, exploration, and coverage is beyond the scope of this paper.

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Section: Hardware Implementationsmentioning

confidence: 97%

The Multiple Unmanned Air Vehicle Persistent Surveillance Problem: A Review

Nigam

2014

Machines

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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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“…The algorithms were tested in the Vehicle Swarm Technology Laboratory (VSTL) developed by the Boeing Research and Technology group [15,16]. This facility provides a large, indoor flight test arena where heterogenous teams may conduct various types of missions.…”

Section: A Boeing Hardware Environmentmentioning

confidence: 99%

Partitioned searching and deconfliction: Analysis and flight tests

Lum

Vagners

Jang

et al. 2010

Proceedings of the 2010 American Control Conference

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Searching a complex environment for a hidden target is a common problem encountered by many autonomous systems. Many modern autonomous systems use a hierarchical structure for mission management where different algorithms perform different tasks to give agents desired behavior. This work investigates a search policy that guarantees both an exhaustive search of the map and conflict free paths of all agents. Agents formulate control decisions for a fixed number of time steps using a modular algorithm that allows parameterizations of agent capabilities. High fidelity simulation and flight test data using multiple autonomous vehicles are used to verify and validate the algorithms in real time.

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Control and Management of an Indoor, Health Enabled, Heterogenous Fleet

Cited by 15 publications

References 10 publications

The Multiple Unmanned Air Vehicle Persistent Surveillance Problem: A Review

The Multiple Unmanned Air Vehicle Persistent Surveillance Problem: A Review

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

Partitioned searching and deconfliction: Analysis and flight tests

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