Process model, constraints, and the coordinated search strategy

Bourgault, F.; Furukawa, Tomonari; Durrant‐Whyte, Hugh

doi:10.1109/robot.2004.1302552

Cited by 38 publications

(24 citation statements)

References 6 publications

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“…Although Bourgault et al (2004) a number of possible constraints that can impact the target motion model (obstacles, force fields and terrain), the target is usually assumed to be subjected to external disturbances and not to move on the basis of its own intentions.…”

Section: Probabilistic Approachesmentioning

confidence: 99%

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%

Combining temporal planning with probabilistic reasoning for autonomous surveillance missions

2015

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“…In [111] constraints are used to generate two-dimensional Gaussian estimates for robot localization. A cooperative and constraint based object search strategy for a group of robots is described in [7]. Another localization method using bounded-error state estimation was introduced for a small truck or vehicle, which was equipped with ultrasonic sensors in [65,108].…”

Section: Theory Of Constraint Based Modelingmentioning

confidence: 99%

“…The player models were created in allo- centric coordinates as well, as described in Alg. 7. The following experiments were performed:…”

Section: Local Model Experimentsmentioning

confidence: 99%

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Constraint Based World Modeling for Multi Agent Systems in Dynamic Environments

Göhring

2010

Künstl Intell

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Mobile autonomous robotics is a very young and complex field of research. Only in recent decades have robots become able to explore, to move, navigate and to interact with their environment.Since the world is uncertain and since robots can only gain partial information about it, probabilistic navigation algorithms have become very popular whenever a robot has to localize itself or surrounding objects. Furthermore, cooperative exploration and localization approaches have become very relevant lately, as robots begin to act not just alone but in groups. Within this thesis a new approach using the concept of spatial percept-relations for cooperative environment modeling is presented and evaluated. As a second contribution, constraint based localization techniques will be introduced for having a robot or a group of robots efficiently localized and to model their environment.v Zusammenfassung Die mobile Robotik stellt ein sehr junges und komplexes Forschungsfelder unserer Zeit dar. Innerhalb der letzten Jahrzehnte wurde es Robotern möglich, sich innerhalb ihrer Umgebung zu bewegen, zu navigieren und mit ihrer Umwelt zu interagieren.Aufgrund der Tatsache, dass die Welt von Unsicherheit geprägt ist und ein Roboter immer nur partielle Information über sie erhalten kann, wurden probabilistische Navigationsverfahren entwickelt, mit denen sich Roboter lokalisieren und Objekte ihrer Umgebung modellieren können. Weiterhin wurden in letzter Zeit Verfahren untersucht, die die kooperative Exploration der Umgebung durch eine Gruppe von Robotern zum Ziel haben. In der vorliegenden Arbeit wird ein neuartiges Konzept, welches sich Perzeptrelationen für die kooperative Umweltmodellierung zu Nutze macht, vorgestellt und evaluiert. Einen zweiten Beitrag der Arbeit stellen constraintbasierte Lokalisierungstechniken dar, die es einem oder mehreren Robotern auf effiziente Art und Weise ermöglichen, sich zu lokalisieren und ihre Umwelt zu modellieren.vii

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“…Such a characterization of the target motion, such as a random walk or even more complex dynamics [18], can often be constructed in a straightforward manner, and in this way, the target's discrete trajectory through the search region can be seamlessly accounted for in the evolution of the belief distribution.…”

Section: A Multi-agent Bayesian Filteringmentioning

confidence: 99%

Multi-agent probabilistic search in a sequential decision-theoretic framework

Chung

Burdick

2008

2008 IEEE International Conference on Robotics and Automation

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Abstract-Consider the task of searching a region for the presence or absence of a target using a team of multiple searchers. This paper formulates this search problem as a sequential probabilistic decision, which enables analysis and design of efficient and robust search control strategies. Imperfect detections of the target's possible locations are made by each search agent and shared with teammates. This information is used to update the evolving decision variable which represents the belief that the target is present in the region. The sequential decision-theoretic formulation presented in this paper provides an analytic framework to evaluate team search systems, as it includes a performance metric (time until decision), a measure of uncertainty (decision confidence thresholds) and imperfect information gathering (detection error). Strategies for cooperative search are evaluated in this context, and comparisons between homogeneous and hybrid search strategies are investigated in numerical studies.

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Process model, constraints, and the coordinated search strategy

Cited by 38 publications

References 6 publications

Combining temporal planning with probabilistic reasoning for autonomous surveillance missions

Combining temporal planning with probabilistic reasoning for autonomous surveillance missions

Constraint Based World Modeling for Multi Agent Systems in Dynamic Environments

Multi-agent probabilistic search in a sequential decision-theoretic framework

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