Autonomous Decision Making with Uncertainty for an Urban Intelligence, Surveillance and Reconnaissance (ISR) Scenario

Holsapple, Raymond; Baker, John; Chandler, Phillip; Girard, Anouck; Pachter, Meir

doi:10.2514/6.2008-6310

Cited by 17 publications

(13 citation statements)

References 4 publications

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“…They further point out the inadequacies of prior research: (1) not revisiting explored regions; (2) stationary targets; (3) excessive information exchange; and (4) often impractical search paths (requiring lots of turns etc.). Holsapple and Chandler [102] use a team of UAVs and MAVs with human-in-the-loop interaction, for surveillance and reconnaissance. The stochastic controller onboard the MAVs, for decision making, is the focus of that paper.…”

Section: Related Workmentioning

confidence: 99%

“…While, not a planning method in itself, Dynamic Programming (DP) has been extensively used for formulating cost functions for plan-based approaches and decision theoretic techniques in general [66,102,113,126]. Barto, Bradtke, and Singh [1] give an extensive description of such methods with particular emphasis on discrete time problems.…”

Section: Dynamic Programming and Decision Theoretic Techniquesmentioning

confidence: 99%

“…Ref. [102] applies controllers based on stochastic DP for Intelligent Surveillance and Reconnaissance (ISR). The need for stochastic controllers in this case is driven by the heterogeneous setting with intermittent inputs by humans.…”

Section: Explicit Consideration To Uncertaintymentioning

confidence: 99%

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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: Related Workmentioning

confidence: 99%

Section: Dynamic Programming and Decision Theoretic Techniquesmentioning

confidence: 99%

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The Multiple Unmanned Air Vehicle Persistent Surveillance Problem: A Review

Nigam

2014

Machines

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“…In [4], the authors investigated the use of human operator feedback for target recognition in an ISR scenario where a team of Micro Aerial Vehicles (MAVs) is assigned to fly over a number of objects of interest and the operator must decide whether or not the object is a target. In [5], the authors presented decision making strategies under uncertainty and adversary action for the Cooperative Operations in UrbaN TERrain program (COUNTER), where stochastic dynamic programming was employed to optimize the resource allocation.…”

Section: Literature Reviewmentioning

confidence: 99%

Strategic Path Planning by Sequential Parametric Bayesian Decisions

Hyun

Kabamba

Girard

2013

International Journal of Advanced Robotic Systems

Self Cite

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The objective of this research is to generate a path for a mobile agent that carries sensors used for classification, where the path is to optimize strategic objectives that account for misclassification and the consequences of misclassification, and where the weights assigned to these consequences are chosen by a strategist. We propose a model that accounts for the interaction between the agent kinematics (i.e., the ability to move), informatics (i.e., the ability to process data to information), classification (i.e., the ability to classify objects based on the information), and strategy (i.e., the mission objective). Within this model, we pose and solve a sequential decision problem that accounts for strategist preferences and the solution to the problem yields a sequence of kinematic decisions of a moving agent. The solution of the sequential decision problem yields the following flying tactics: "approach only objects whose suspected identity matters to the strategy". These tactics are numerically illustrated in several scenarios.

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“…Systems equipped with flexible levels of autonomy (LOAs) and decisionmaking capabilities in uncertain environments may be exploited to dynamically allocate human-machine functions by discriminating situations where operators' skills are sufficient to perform a given task from situations where system suggestions or interventions may be required [4][5][6]. The assessment of operator multitasking performance as well as the level of his/her mental effort for monitoring UAVs, generally termed as "cognitive or mental workload " [7], may be used to determine which LOA is needed for the system.…”

Section: Introductionmentioning

confidence: 99%

Adjustable Autonomy for UAV Supervision Applications Through Mental Workload Assessment Techniques

Bazzano

Grimaldi

Lamberti

et al. 2017

Intelligent Human Computer Interaction

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Abstract. In recent years, unmanned aerial vehicles have received a significant attention in the research community, due to their adaptability in different applications, such as surveillance, disaster response, traffic monitoring, transportation of goods, first aid, etc. Nowadays, even though UAVs can be equipped with some autonomous capabilities, they often operate in high uncertainty environments in which supervisory systems including human in the control loop are still required. Systems envisaging decision-making capabilities and equipped with flexible levels of autonomy are needed to support UAVs controllers in monitoring operations. The aim of this paper is to build an adjustable autonomy system able to assist UAVs controllers by predicting mental workload changes when the number of UAVs to be monitored highly increases. The proposed system adjusts its level of autonomy by discriminating situations in which operators' abilities are sufficient to perform UAV supervision tasks from situations in which system suggestions or interventions may be required. Then, a user study was performed to create a mental-workload prediction model based on operators' cognitive demand in drone monitoring operations. The model is exploited to train the system developed to infer the appropriate level of autonomy accordingly. The study provided precious indications to be possibly exploited for guiding next developments of the adjustable autonomy system proposed.

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Autonomous Decision Making with Uncertainty for an Urban Intelligence, Surveillance and Reconnaissance (ISR) Scenario

Cited by 17 publications

References 4 publications

The Multiple Unmanned Air Vehicle Persistent Surveillance Problem: A Review

The Multiple Unmanned Air Vehicle Persistent Surveillance Problem: A Review

Strategic Path Planning by Sequential Parametric Bayesian Decisions

Adjustable Autonomy for UAV Supervision Applications Through Mental Workload Assessment Techniques

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