Search and Rescue Working Group Report

Haley, K. B.; Stone, Lawrence D.

doi:10.1007/978-1-4615-9188-7_4

Cited by 3 publications

(4 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The dominant theoretical framework for the formulation of search is probability theory, where Bayesian inference is used to update sequentially the posterior probability distribution of target locations, as the new measurements are collected over time [9,[11][12][13][14]. Sensor motion control is typically formulated as a partially observed Markov decision process (POMDP) [15].…”

Section: Introductionmentioning

confidence: 99%

A Possibilistic Formulation of Autonomous Search for Targets

Chen,

Ristic,

Kim

2024

Preprint

View full text Add to dashboard Cite

Autonomous search is an ongoing cycle of sensing, statistical estimation and motion control with objective to find and localise targets in a designated search area. Traditionally, the theoretical framework for autonomous search combines the sequential Bayesian estimation with the information theoretic motion control. This paper formulates autonomous search in the framework of possibility theory. Although the possibilistic formulation is slightly more involved than the traditional, it provides means for quantitative modelling and reasoning in the presence of epistemic uncertainty. This feature is demonstrated in the paper in the context of partially known probability of detection, expressed as an interval value. The paper presents an elegant Bayes-like solution to sequential estimation, with the reward function for motion control defined to take into account the epistemic uncertainty. The advantages of the proposed search algorithm are demonstrated by numerical simulations.

show abstract

Section: Introductionmentioning

confidence: 99%

A Possibilistic Formulation of Autonomous Search for Targets

Chen,

Ristic,

Kim

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The dominant theoretical framework for the formulation of search is probability theory, where Bayesian inference is used to sequentially update the posterior probability distribution of target locations as new measurements are collected over time [9,[11][12][13][14]. Sensor motion control is typically formulated as a partially observed Markov decision process (POMDP) [15].…”

Section: Introductionmentioning

confidence: 99%

A Possibilistic Formulation of Autonomous Search for Targets

Chen,

Ristic,

Kim

2024

Entropy

View full text Add to dashboard Cite

Autonomous search is an ongoing cycle of sensing, statistical estimation, and motion control with the objective to find and localise targets in a designated search area. Traditionally, the theoretical framework for autonomous search combines sequential Bayesian estimation with information theoretic motion control. This paper formulates autonomous search in the framework of possibility theory. Although the possibilistic formulation is slightly more involved than the traditional method, it provides a means for quantitative modelling and reasoning in the presence of epistemic uncertainty. This feature is demonstrated in the paper in the context of partially known probability of detection, expressed as an interval value. The paper presents an elegant Bayes-like solution to sequential estimation, with the reward function for motion control defined to take into account the epistemic uncertainty. The advantages of the proposed search algorithm are demonstrated by numerical simulations.

show abstract

“…Searching strategies for finding targets using appropriate sensing modalities are of great importance in many aspects of life, from national security [ 1 , 2 ], rescue and recovery missions [ 3 , 4 ], to biological applications [ 5 , 6 , 7 ]. A taxonomy of search problems is proposed in [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…The earliest theoretical studies of search strategies [ 9 , 10 ] were based on systematic search along a predetermined (deterministic) path, such as the parallel sweep or the Archimedean spiral [ 3 , 11 ]. The search patterns of animals, on the contrary, are random rather than deterministic.…”

Section: Introductionmentioning

confidence: 99%

Intermittent Information-Driven Multi-Agent Area-Restricted Search

Ristić

Skvortsov

2020

Entropy

View full text Add to dashboard Cite

The problem is a two-dimensional area-restricted search for a target using a coordinated team of autonomous mobile sensing platforms (agents). Sensing is characterised by a range-dependent probability of detection, with a non-zero probability of false alarms. The context is underwater surveillance using a swarm of amphibious drones equipped with active sonars. The paper develops an intermittent information-driven search strategy, which alternates between two phases: the fast and non-receptive displacement phase (called the ballistic phase) with a slow displacement and sensing phase (called the diffusive phase). The proposed multi-agent search strategy is carried out in a decentralised manner, which means that all computations (estimation and motion control) are done locally. Coordination of agents is achieved by exchanging the data with the neighbours only, in a manner which does not require global knowledge of the communication network topology.

show abstract

Search and Rescue Working Group Report

Cited by 3 publications

References 1 publication

A Possibilistic Formulation of Autonomous Search for Targets

A Possibilistic Formulation of Autonomous Search for Targets

A Possibilistic Formulation of Autonomous Search for Targets

Intermittent Information-Driven Multi-Agent Area-Restricted Search

Contact Info

Product

Resources

About