Coordinated Search-and-Capture Using Particle Filters

Chung, Chern Ferng; Furukawa, Tomonari

doi:10.1109/icarcv.2006.345338

Cited by 8 publications

(7 citation statements)

References 6 publications

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“…The prediction stage calculates the PD of the next state using the posterior distribution and the target's motion model. Since the implementation of these two stages is computationally expensive, several approaches have been explored to compute them efficiently, including grid-based methods (Bourgault et al, 2006), particle filters (Chung & Furukawa, 2006), element-based techniques (Furukawa, Durrant-Whyte, & Lavis, 2007) and hybrid particle-element approaches (Lavis & Furukawa, 2008).…”

Section: Probabilistic Approachesmentioning

confidence: 99%

Autonomous Target Search with Multiple Coordinated UAVs

Piacentini¹,

Bernardini²,

Beck³

2019

jair

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Search and tracking is the problem of locating a moving target and following it to its destination. In this work, we consider a scenario in which the target moves across a large geographical area by following a road network and the search is performed by a team of unmanned aerial vehicles (UAVs). We formulate search and tracking as a combinatorial optimization problem and prove that the objective function is submodular. We exploit this property to devise a greedy algorithm. Although this algorithm does not offer strong theoretical guarantees because of the presence of temporal constraints that limit the feasibility of the solutions, it presents remarkably good performance, especially when several UAVs are available for the mission. As the greedy algorithm suffers when resources are scarce, we investigate two alternative optimization techniques: Constraint Programming (CP) and AI planning. Both approaches struggle to cope with large problems, and so we strengthen them by leveraging the greedy algorithm. We use the greedy solution to warm start the CP model and to devise a domain-dependent heuristic for planning. Our extensive experimental evaluation studies the scalability of the different techniques and identifies the conditions under which one approach becomes preferable to the others.

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Section: Probabilistic Approachesmentioning

confidence: 99%

Autonomous Target Search with Multiple Coordinated UAVs

Piacentini¹,

Bernardini²,

Beck³

2019

jair

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show abstract

“…The particle filter has been successfully applied to tracking applications [17,18]; it allows non-linear sensor and motion models and can represent arbitrary distributions. In a particle filter, a distribution p(X ) is represented by a set of N weighted Dirac delta functions (particles) at locations {x i } in the same domain as x: (21).…”

Section: Filteringmentioning

confidence: 99%

Particle filter based information-theoretic active sensing

Ryan

Hedrick

2010

Robotics and Autonomous Systems

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“…Optimising this trade-off is the goal of automated decision making for search. Since search naturally lends itself to this Bayesian formulation, several solutions within this framework have been attempted (Chung and Furukawa 2006;Furukawa, Durrant-Whyte, and Lavis 2007;Lavis and Furukawa 2008). However, we have recently shown that a Bayesian formulation of search can be compiled into a deterministic planning problem and solved with automated planning tools (Bernardini et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Deterministic versus Probabilistic Methods for Searching for an Evasive Target

Bernardini

Fox

Long

et al. 2017

AAAI

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Several advanced applications of autonomous aerial vehicles in civilian and military contexts involve a searching agent with imperfect sensors that seeks to locate a mobile target in a given region. Effectively managing uncertainty is key to solving the related search problem, which is why all methods devised so far hinge on a probabilistic formulation of the problem and solve it through branch-and-bound algorithms, Bayesian filtering or POMDP solvers. In this paper, we consider a class of hard search tasks involving a target that exhibits an intentional evasive behaviour and moves over a large geographical area, i.e., a target that is particularly difficult to track down and uncertain to locate. We show that, even for such a complex problem, it is advantageous to compile its probabilistic structure into a deterministic model and use standard deterministic solvers to find solutions. In particular, we formulate the search problem for our uncooperative target both as a deterministic automated planning task and as a constraint programming task and show that in both cases our solution outperforms POMDPs methods.

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Coordinated Search-and-Capture Using Particle Filters

Cited by 8 publications

References 6 publications

Autonomous Target Search with Multiple Coordinated UAVs

Autonomous Target Search with Multiple Coordinated UAVs

Particle filter based information-theoretic active sensing

Deterministic versus Probabilistic Methods for Searching for an Evasive Target

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