Open AccessProceedings Article
Optimal Search for a Lost Target in a Bayesian World.
Frédéric Bourgault,Tomonari Furukawa,Hugh Durrant-Whyte +2 more
- pp 209-222
TLDR
A Bayesian approach to the problem of searching for a single lost target by a single autonomous sensor platform, implemented for an airborne vehicle looking for both a stationary and a drifting target at sea.Abstract:
This paper presents a Bayesian approach to the problem of searching for a single lost target by a single autonomous sensor platform. The target may be static or mobile but not evading. Two candidate utility functions for the control solution are highlighted, namely the Mean Time to Detection, and the Cumulative Probability of Detection. The framework is implemented for an airborne vehicle looking for both a stationary and a drifting target at sea. Simulation results for different control solutions are investigated and compared to demonstrate the effectiveness of the method.read more
Citations
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Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey
TL;DR: This article describes the most promising aerial drone applications and outline characteristics of aerial drones relevant to operations planning, and provides insights into widespread and emerging modeling approaches to civil applications of UAVs.
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Search and pursuit-evasion in mobile robotics
TL;DR: A taxonomy of search problems is provided that highlights the differences resulting from varying assumptions on the searchers, targets, and the environment and highlights current open problems in the area and explores avenues for future work.
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Autonomous UAV path planning and estimation
J. Tisdale,ZuWhan Kim,J. Hedrick +2 more
TL;DR: This work proposes a tightly coupled approach, in which sensor models and estimation objectives are used online for path planning, and seeks to develop decentralized, autonomous control strategies that can account for a wide variety of sensing missions.
Proceedings ArticleDOI
Recursive Bayesian search-and-tracking using coordinated uavs for lost targets
TL;DR: A unified sensor model and a unified objective function are proposed to enable search-and-tracking (SAT) within the recursive Bayesian filter framework to demonstrate the applicability of the technique to real search world scenarios.
Proceedings ArticleDOI
Coordinated decentralized search for a lost target in a Bayesian world
TL;DR: A decentralized Bayesian approach to coordinating multiple autonomous sensor platforms searching for a single non-evading target through a Bayesian DDF network that has a high degree of scalability and real time adaptability.
References
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Journal ArticleDOI
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
TL;DR: An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters, represented as a set of random samples, which are updated and propagated by the algorithm.
Journal ArticleDOI
Stastical Decision Theory and Bayesian Analysis.
Malay Ghosh,James O. Berger +1 more
Book
Bayesian Multiple Target Tracking
TL;DR: The book shows you how non-linear Multiple Hypothesis Tracking and the Theory of Unified Tracking are successful methods when multiple target tracking must be performed without contacts or association.