UAV Path Planning in a Dynamic Environment via Partially Observable Markov Decision Process

A path-planning algorithm to guide unmanned aerial vehicles (UAVs) for tracking multiple ground targets based on the theory of partially observable Markov decision processes (POMDPs) is presented. A variety of features of interest are shown to be easy to incorporate into the framework by plugging in...

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Bibliographic Details
Published inIEEE transactions on aerospace and electronic systems Vol. 49; no. 4; pp. 2397 - 2412
Main Authors Ragi, Shankarachary, Chong, Edwin K. P.
Format Journal Article
LanguageEnglish
Published IEEE 01.10.2013
Subjects
Acceleration
Approximation methods
Covariance matrices
Mathematical model
Sensors
Target tracking
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Summary:A path-planning algorithm to guide unmanned aerial vehicles (UAVs) for tracking multiple ground targets based on the theory of partially observable Markov decision processes (POMDPs) is presented. A variety of features of interest are shown to be easy to incorporate into the framework by plugging in the appropriate models, which demonstrates the power and flexibility of the POMDP framework. Specifically, it is shown how to incorporate the following features by appropriately formulating the POMDP action space, transition law, and objective function: 1) control UAVs with both forward acceleration and bank angle subject to constraints; 2) account for the effect of wind disturbance on UAVs; 3) avoid collisions between UAVs and obstacles and among UAVs; 4) track targets while evading threats; 5) track evasive targets; and 6) mitigate track swaps.
ISSN:0018-9251
DOI:10.1109/TAES.2013.6621824