A Comparison of Decision Making Criteria and Optimization Methods for Active Robotic Sensing

Mihaylova, Lyudmila; Lefebvre, Tine; Bruyninckx, Herman; Gadeyne, Klaas; Schutter, Joris De

doi:10.1007/3-540-36487-0_35

Cited by 39 publications

(30 citation statements)

References 22 publications

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“…Mihaylova et al [31] review some criteria for uncertainty minimization in the context of active sensing. They consider a multi-objective setting, linearly trading off expected information extraction with expected costs and utilities.…”

Section: Pomdp Framework For Rewarding Information Gainmentioning

confidence: 99%

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Decision-theoretic planning under uncertainty with information rewards for active cooperative perception

Spaan

Veiga

Lima

2014

Auton Agent Multi-Agent Syst

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Partially observable Markov decision processes (POMDPs) provide a principled framework for modeling an agent's decision-making problem when the agent needs to consider noisy state estimates. POMDP policies take into account an action's influence on the environment as well as the potential information gain. This is a crucial feature for robotic agents which generally have to consider the effect of actions on sensing. However, building POMDP models which reward information gain directly is not straightforward, but is important in domains such as robot-assisted surveillance in which the value of information is hard to quantify. Common techniques for uncertainty reduction such as expected entropy minimization lead to non-standard POMDPs that are hard to solve. We present the POMDP with Information Rewards (POMDP-IR) modeling framework, which rewards an agent for reaching a certain level of belief regarding a state feature. By remaining in the standard POMDP setting we can exploit many known results as well as successful approximate algorithms. We demonstrate our ideas in a toy problem as well as in real robot-assisted surveillance, showcasing their use for active cooperative perception scenarios. Finally, our experiments show that the POMDP-IR framework compares favorably with a related approach on benchmark domains.

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Section: Pomdp Framework For Rewarding Information Gainmentioning

confidence: 99%

“…Information gain has been studied in the active sensing framework [31], which can be formalized as acting so as to acquire knowledge about certain state variables.…”

Section: Active Sensingmentioning

confidence: 99%

Decision-theoretic planning under uncertainty with information rewards for active cooperative perception

Spaan

Veiga

Lima

2014

Auton Agent Multi-Agent Syst

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“…Our approach is based on the multi-objective performance criterion described by Mihaylova et al in [18] and has the following form:…”

Section: Next-best-view Plannermentioning

confidence: 99%

3D Sensor planning framework for leaf probing

Foix

Alenyà

Torras

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Modern plant phenotyping requires active sensing technologies and particular exploration strategies. This article proposes a new method for actively exploring a 3D region of space with the aim of localizing special areas of interest for manipulation tasks over plants. In our method, exploration is guided by a multi-layer occupancy grid map. This map, together with a multiple-view estimator and a maximuminformation-gain gathering approach, incrementally provides a better understanding of the scene until a task termination criterion is reached.This approach is designed to be applicable for any task entailing 3D object exploration where some previous knowledge of its general shape is available. Its suitability is demonstrated here for an eye-in-hand arm configuration in a leaf probing application.

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“…By exploiting recent results in adaptive submodularity [11], [6], we provide theoretical bounds for the worst-case performance of the greedy strategy. Such dynamic state estimation techniques have been proposed in the context of Markov jump linear systems [3], information gathering in robotics [13], [20], active hypothesis testing [16], and active learning [9]. To the best of our knowledge, these ideas have not been applied before in electric power system state estimation and fault diagnosis problems.…”

Section: Introductionmentioning

confidence: 99%

Dynamic state estimation in distributed aircraft electric control systems via adaptive submodularity

Maillet

Özay

et al. 2013

52nd IEEE Conference on Decision and Control

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Abstract-We consider the problem of estimating the discrete state of an aircraft electric system under a distributed control architecture through active sensing. The main idea is to use a set of controllable switches to reconfigure the system in order to gather more information about the unknown state. By adaptively making a sequence of reconfiguration decisions with uncertain outcome, then correlating measurements and prior information to make the next decision, we aim to reduce the uncertainty. A greedy strategy is developed that maximizes the one-step expected uncertainty reduction. By exploiting recent results on adaptive submodularity, we give theoretical guarantees on the worst-case performance of the greedy strategy. We apply the proposed method in a fault detection scenario where the discrete state captures possible faults in various circuit components. In addition, simple abstraction rules are proposed to alleviate state space explosion and to scale up the strategy. Finally, the efficiency of the proposed method is demonstrated empirically on different circuits.

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A Comparison of Decision Making Criteria and Optimization Methods for Active Robotic Sensing

Cited by 39 publications

References 22 publications

Decision-theoretic planning under uncertainty with information rewards for active cooperative perception

Decision-theoretic planning under uncertainty with information rewards for active cooperative perception

3D Sensor planning framework for leaf probing

Dynamic state estimation in distributed aircraft electric control systems via adaptive submodularity

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