A Decision Policy for the Routing and Munitions Management of Multiformations of Unmanned Combat Vehicles in Adversarial Urban Environments

Léchevin, N.; Rabbath, C.A.; Lauzon, M.

doi:10.1109/tcst.2008.2000980

Cited by 23 publications

(11 citation statements)

References 22 publications

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“…Let be the probability distribution for the search process for the 2 distinct state values in our case. We define the information entropy at at time as (6) If…”

Section: The Uncertainty Mapmentioning

confidence: 99%

“…The actual maximum achieved search uncertainty turns out to be 2. 6 according to the simulation results. Fig.…”

Section: A Simulation Examplementioning

confidence: 99%

“…In [5], the target existence probability gain from searching a point is used as a cost function to determine the vehicle's optimal path. In [6], an optimal decision policy for the routing and munitions management of multiple formations of unmanned combat vehicles is proposed with imperfect information about adversarial ground units. A recursive Bayesian filter (RBF) is used for environment estimate.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian-Based Decision-Making for Object Search and Classification

Wang

Hussein

2011

IEEE Trans. Contr. Syst. Technol.

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This paper focuses on the development of real-time decision-making criteria for an autonomous vehicle whose tasks to be performed are competing under limited sensory resources. More specifically, we are interested in the search and classification of multiple static objects of unknown number and positions given a single autonomous sensor vehicle. In this case, search and classification are two competing demands since an autonomous vehicle can perform either task but not both at the same time. During a search task, once the vehicle finds an object of interest, it has to decide on whether to neglect the found object and continue searching, or stop and observe that object. If the vehicle decides to stop and classify the object, it has to decide on how much time it can afford to do so. This is a very critical decision as choosing one option over the other may mean missing other, more important objects not yet found, or missing the opportunity to satisfactorily classify a found critical object. Building on previous deterministic work by the authors, in this paper we develop Bayesian-based probabilistic and information-theoretic search versus classification decision-making criteria that result in guaranteed detection and classification of all the unknown objects in the domain. Simulation-based results are provided to study the performance of the proposed decision-making strategy.

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“…Let be the probability distribution for the search process for the 2 distinct state values in our case. We define the information entropy at at time as (6) If…”

Section: The Uncertainty Mapmentioning

confidence: 99%

“…The actual maximum achieved search uncertainty turns out to be 2. 6 according to the simulation results. Fig.…”

Section: A Simulation Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bayesian-Based Decision-Making for Object Search and Classification

Wang

Hussein

2011

IEEE Trans. Contr. Syst. Technol.

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“…Targeted applications comprise persistent surveillance missions as well as route and munitions planning [18] where (i) losses of vehicles are governed by imprecise MDPs and (ii) criteria of coherence regarding risk assessment made by decision makers are violated [20].…”

Section: Discussionmentioning

confidence: 99%

Sequential finite-horizon Choquet-expected decision problems with uncertainty aversion

Léchevin

Rabbath

2010

Proceedings of the 2010 American Control Conference

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This paper proposes a finite-horizon, sequential decision problem formulation where the probability measures used in Markov decision processes are replaced by a class of capacity measures. Subjective probability, arising for example in risk assessment carried out by humans, and modeling uncertainty, such as imprecise probability, can be represented by capacity measures. The aggregation operator employed to formulate the criterion is the so-called Choquet integral. A recursive equation is derived by applying results from sequential, stochastic, zero-sum games and cores of convex capacity. The recursion is applied to the finite-horizon control of Markovian jump linear systems involving a capacity measure. We show that a suboptimal solution, expressed as a Riccati equation, can be obtained by approximating the computation of the Choquet integral.

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“…The most popular probabilistic strategy used to address the SM problem is Bayesian Networks [Cortez et al 2008;Liao et al 2009;Lechevin et al 2009]. However, the system operates under assumptions of the Prior and the Error in the estimations, which can affect system performance significantly.…”

Section: State-of-the-art Smmentioning

confidence: 99%

A Collaborative Energy-Aware Sensor Management System Using Team Theory

Hilal

Basir

2016

ACM Trans. Embed. Comput. Syst.

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With limited battery supply, power is a scarce commodity in wireless sensor networks. Thus, to prolong the lifetime of the network, it is imperative that the sensor resources are managed effectively. This task is particularly challenging in heterogeneous sensor networks for which decisions and compromises regarding sensing strategies are to be made under time and resource constraints. In such networks, a sensor has to reason about its current state to take actions that are deemed appropriate with respect to its mission, its energy reserve, and the survivability of the overall network. Sensor Management controls and coordinates the use of the sensory suites in a manner that maximizes the success rate of the system in achieving its missions. This article focuses on formulating and developing an autonomous energy-aware sensor management system that strives to achieve network objectives while maximizing its lifetime. A team-theoretic formulation based on the Belief-Desire-Intention (BDI) model and the Joint Intention theory is proposed as a mechanism for effective and energy-aware collaborative decision-making. The proposed system models the collective behavior of the sensor nodes using the Joint Intention theory to enhance sensors' collaboration and success rate. Moreover, the BDI modeling of the sensor operation and reasoning allows a sensor node to adapt to the environment dynamics, situation-criticality level, and availability of its own resources. The simulation scenario selected in this work is the surveillance of the Waterloo International Airport. Various experiments are conducted to investigate the effect of varying the network size, number of threats, threat agility, environment dynamism, as well as tracking quality and energy consumption, on the performance of the proposed system. The experimental results demonstrate the merits of the proposed approach compared to the state-of-the-art centralized approach adapted from Atia et al. [2011] and the localized approach in Hilal and Basir [2015] in terms of energy consumption, adaptability, and network lifetime. The results show that the proposed approach has 12× less energy consumption than that of the popular centralized approach. ACM Reference Format:Allaa R. Hilal and Otman Basir. 2016. A collaborative energy-aware sensor management system using team theory.

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A Decision Policy for the Routing and Munitions Management of Multiformations of Unmanned Combat Vehicles in Adversarial Urban Environments

Cited by 23 publications

References 22 publications

Bayesian-Based Decision-Making for Object Search and Classification

Bayesian-Based Decision-Making for Object Search and Classification

Sequential finite-horizon Choquet-expected decision problems with uncertainty aversion

A Collaborative Energy-Aware Sensor Management System Using Team Theory

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