DocumentCode :
184018
Title :
Continuous-time intruder isolation using Unattended Ground Sensors on graphs
Author :
Hua Chen ; Kalyanam, Krishnamoorthy ; Wei Zhang ; Casbeer, D.
Author_Institution :
Dept. of Electr. & Comput. Eng., Ohio State Univ., Columbus, OH, USA
fYear :
2014
fDate :
4-6 June 2014
Firstpage :
5270
Lastpage :
5275
Abstract :
This paper studies the continuous-time intruder isolation problem on a general road network graph under delayed information scenario. Several Unattended Ground Sensors (UGSs) are pre-installed along certain edges of the graph for detecting intruder motion and recording the detection time. Measurements of a UGS can only be obtained when the UAV is within its communication range. The goal of this paper is to find the optimal path for the UAV to follow in order to capture the intruder within the shortest time, based on the delayed information from the visited UGSs. We propose an unfolding strategy to transform the road network graph to a decision tree incorporating delayed measurement information. Based on the decision tree, both optimal and sub-optimal min-max solutions are developed. Several interesting properties of the corresponding optimal value function are also derived. Numerical simulations based on a real road network are presented to demonstrate the effectiveness of the proposed strategies.
Keywords :
autonomous aerial vehicles; decision trees; delays; minimax techniques; network theory (graphs); optimal control; roads; UAV; UGS measurement; communication range; continuous time intruder isolation; decision tree; delayed information; delayed measurement; intruder motion detection; numerical simulation; optimal min-max solution; optimal value function; road network graph; shortest time; unattended ground sensor; unfolding strategy; Aerospace electronics; Decision trees; Games; Optimal control; Roads; Sensors; Uncertainty; Autonomous systems; Control applications;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference (ACC), 2014
Conference_Location :
Portland, OR
ISSN :
0743-1619
Print_ISBN :
978-1-4799-3272-6
Type :
conf
DOI :
10.1109/ACC.2014.6858895
Filename :
6858895
Link To Document :
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