Object surveillance using reinforcement learning based sensor dispatching

Michael D. Naish, Elizabeth A. Croft, Beno Benhabib

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther


This paper outlines an approach to the coordination of multiple mobile sensors for the surveillance of a single moving target. A real-time dispatching algorithm is used to select and position groups of sensors in response to the observed object motion. The aim is to provide robust, high-quality data while ensuring that the system can react to unexpected object manoeuvres. Sensors are assigned to collect data at specific points on the object trajectory. A dispatching strategy learned via reinforcement learning is used to control the sensor poses with respect to these points. In using the learned strategy, each sensor adopts an egocentric view of the system state to determine the most appropriate action. Simulations demonstrate the performance of the RL-based dispatcher, in comparison to similar static-sensor systems.

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Robotics and Automation 2004
Number of pages6
Publication statusPublished - 5 Jul 2004
Externally publishedYes
EventIEEE International Conference on Robotics and Automation 2004 - New Orleans, United States of America
Duration: 26 Apr 20041 May 2004
https://ieeexplore.ieee.org/xpl/conhome/9126/proceeding?isnumber=29020 (Proceedings)

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729


ConferenceIEEE International Conference on Robotics and Automation 2004
Abbreviated titleICRA 2004
Country/TerritoryUnited States of America
CityNew Orleans
Internet address

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