HyPE: Hybrid Particle-Element Approach for Recursive Bayesian Searching and Tracking

Lavis, Benjamin; Furukawa, Tomonari

doi:10.15607/rss.2008.iv.018

Cited by 9 publications

(11 citation statements)

References 18 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 gridbased methods , particle filters (Chung and Furukawa 2006), element-based techniques (Furukawa et al 2007) and hybrid particle-element approaches (Lavis and Furukawa 2008).…”

Section: Probabilistic Approachesmentioning

confidence: 99%

“…The use of automated task planning for SaT missions has received little attention so far, while probabilistic approaches based on Recursive Bayesian Estimation (RBE) have been explored in more depth. Efficient solutions to SaT have been proposed under restrictive simplifying assumptions such as the search area being small (one/two square km), the temporal horizon being short (a few minutes) and the target's motion model being simple (e.g., targets being stationary or in Markovian motion) (Stone 1975;Bourgault et al 2006;Furukawa et al 2006;Lavis and Furukawa 2008;Lin and Goodrich 2014). Although this purely probabilistic approach is successful for small-scale and simple SaT problems, it fails in the face of all the constraints that characterise realworld SaT operations because it becomes computationally too expensive.…”

Section: Introductionmentioning

confidence: 99%

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Combining temporal planning with probabilistic reasoning for autonomous surveillance missions

2015

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It is particularly challenging to devise techniques for underpinning the behaviour of autonomous vehicles in surveillance missions as these vehicles operate in uncertain and unpredictable environments where they must cope with little stability and tight deadlines in spite of their restricted resources. State-of-the-art techniques typically use probabilistic algorithms that suffer a high computational cost in complex real-world scenarios. To overcome these limitations, we propose a hybrid approach that combines the probabilistic reasoning based on the target motion model offered by Monte Carlo simulation with long-term strategic capabilities provided by automated task planning. We demonstrate our approach by focusing on one particular surveillance mission, search-and-tracking, and by using two different vehicles, a fixed-wing UAV deployed in simulation and the "Parrot AR.Drone2.0" quadcopter deployed in a physical environment. Our experimental results show that our unique way of integrating probabilistic and deterministic reasoning pays off when we tackle realistic missions. Electronic supplementary materialThe online version of this article

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Section: Probabilistic Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combining temporal planning with probabilistic reasoning for autonomous surveillance missions

2015

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“…In surveillance missions, tasks is specifically defined as searching a target of interest in a given environment [12,13]. Mission is operated in environments modeled as a twodimensional space.…”

Section: Problem Statementmentioning

confidence: 99%

“…However, estimating the targets and determining tasks from the crude data are challenging. Many estimation processes applies the probabilistic approach to determine the state of environment because of the uncertainties of measurements [12,15]. To couple this presented process with task assignment algorithm, we need an objective function that collaborates information and theoretic procedures.…”

Section: Information Gathering Processmentioning

confidence: 99%

Decentralized information-theoretic task assignment for searching and tracking of moving targets

Moon

Yang

Gan

et al. 2015

2015 International Conference on Unmanned Aircraft Systems (ICUAS)

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This paper presents a framework for informationtheoretic based task assignment of multiple UAVs for searching and tracking moving targets. A stochastic approach which uses probability density functions is introduced as the information gathering metric. This algorithm yields trajectory that minimizes the uncertainty of unknown information. The presented task assignment is based on the negotiation activated when one of the defined conditions is satisfied. This task assignment can generate admissible solutions with minimal computational time. Cost computed by each agent from its information gathering layer is used in the task assignment layer to allocate the priority of tasks for each agent in a decentralized architecture. Our algorithm is validated on a search and track scenario with three fixed-wing UAVs and twelve moving targets.

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“…The good accuracy is obtained by the subtle discretization of the target space but leads to an inefficient computation at the same time. Furukawa et al [9,10] refined the grid-based RBE by developing a more general element-based RBE. The generalized element can help accurately represent the arbitrary target space with only the small number of elements compared with the grid-based RBE so as to reduce the computation of the RBE.…”

Section: Introductionmentioning

confidence: 99%

Computational modeling for parallel grid-based recursive Bayesian estimation: parallel computation using graphics processing unit

Tong

Furukawa

Durrant‐Whyte

2013

J. Uncertain. Anal. Appl.

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This paper presents the performance modeling of the real-time grid-based recursive Bayesian estimation (RBE), particularly the parallel computation using graphics processing unit (GPU). The proposed modeling formulates data transmission between the central processing unit (CPU) and the GPU as well as floating point operations to be carried out in each CPU and GPU necessary for one iteration of the real-time grid-based RBE. Given the specifications of the computer hardware, the proposed modeling can thus estimate the total amount of time cost for performing the grid-based RBE in a real-time environment. A new prediction formulation, which adopted separable convolution, is proposed to further accelerate the real-time grid-based RBE. The performance of the proposed modeling was investigated, and parametric studies have first demonstrated its validity in various conditions by showing that the average error of estimation in computational performance stays below 6% to 7%. Utilizing the prediction with separable convolution, the grid-based RBE has also been found to perform within 1 ms, although the size of the problem was relatively large.

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HyPE: Hybrid Particle-Element Approach for Recursive Bayesian Searching and Tracking

Cited by 9 publications

References 18 publications

Combining temporal planning with probabilistic reasoning for autonomous surveillance missions

Combining temporal planning with probabilistic reasoning for autonomous surveillance missions

Decentralized information-theoretic task assignment for searching and tracking of moving targets

Computational modeling for parallel grid-based recursive Bayesian estimation: parallel computation using graphics processing unit

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