Exploring Health-Enabled Mission Concepts in the Vehicle Swarm Technology Lab

Bieniawski, Stefan R.; Pigg, Paul; Vian, John; Bethke, Brett; How, Jon

doi:10.2514/6.2009-1918

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Also, a failure model of the sensors onboard each UAV allowed the planner to recognize that the UAVs' sensors may fail during the mission [5], [6], [10]. This research extends these previous formulations to represent a slightly more complex mission scenario as well as giving flight results of the whole system in operation in the Boeing vehicle swarm rapid prototyping testbed [11], [12]. The extended scenario includes the connection of an agent-level Health Monitoring and Diagnostics System (HMDS) with the planner to account for estimated agent capabilities due to such failures as mentioned previously.…”

Section: Introductionmentioning

confidence: 68%

Agent capability in persistent mission planning using approximate dynamic programming

Bethke¹,

Redding

How

et al. 2010

Proceedings of the 2010 American Control Conference

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Abstract-This paper presents an extension of our previous work on the persistent surveillance problem. An extended problem formulation incorporates real-time changes in agent capabilities as estimated by an onboard health monitoring system in addition to the existing communication constraints, stochastic sensor failure and fuel flow models, and the basic constraints of providing surveillance coverage using a team of autonomous agents. An approximate policy for the persistent surveillance problem is computed using a parallel, distributed implementation of the approximate dynamic programming algorithm known as Bellman Residual Elimination. This paper also presents flight test results which demonstrate that this approximate policy correctly coordinates the team to simultaneously provide reliable surveillance coverage and a communications link for the duration of the mission and appropriately retasks agents to maintain these services in the event of agent capability degradation.

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Section: Introductionmentioning

confidence: 68%

Agent capability in persistent mission planning using approximate dynamic programming

Bethke¹,

Redding

How

et al. 2010

Proceedings of the 2010 American Control Conference

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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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“…To date, the VSTL testbed has been used to test and demonstrate several multi-vehicle health-adaptive missions 13 . Missions include persistent surveillance, area search, and high density air traffic management.…”

Section: Applicationsmentioning

confidence: 99%

Vehicle Swarm Rapid Prototyping Testbed

Saad

Vian

Clark

et al. 2009

AIAA Infotech@Aerospace Conference

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Increased levels of vehicle collaboration and autonomy are seen as a means to reduce overall mission completion costs while expanding mission capabilities and increasing mission assurance for complex coupled system of systems. Systems health management technologies have made rapid advances that enable systems to know their own condition and capabilities, thus creating the opportunity for unprecedented levels of adaptive control, real-time reconfiguration, and mission contingency management. Multi-agent task allocation and mission managements systems must account for vehicle-and system-level health-related issues to ensure that these systems are reliable and cost effective to operate.

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Exploring Health-Enabled Mission Concepts in the Vehicle Swarm Technology Lab

Cited by 4 publications

References 7 publications

Agent capability in persistent mission planning using approximate dynamic programming

Agent capability in persistent mission planning using approximate dynamic programming

Partitioned searching and deconfliction: Analysis and flight tests

Vehicle Swarm Rapid Prototyping Testbed

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