Title :
Multi-vehicle Bayesian Search for Multiple Lost Targets
Author :
Wong, El-Mane ; Bourgault, Frédéric ; Furukawa, Tomonari
Author_Institution :
ARC Centre of Excellence in Autonomous Systems; School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney 2052 Australia
Abstract :
This paper presents a Bayesian approach to the problem of searching for multiple lost targets in a dynamic environment by a team of autonomous sensor platforms. The probability density function (PDF) for each individual target location is accurately maintained by an independent instance of a general Bayesian filter. The team utility for the search vehicles trajectories is given by the sum of the `cumulative´ probability of detection for each target. A dual-objective switching function is also introduced to direct the search towards the mode of the nearest target PDF when the utility becomes too low in a region to distinguish between trajectories. Simulation results for both clustered and isolated targets demonstrate the effectiveness of the proposed search strategy for multiple targets.
Keywords :
Australia; Bayesian methods; Probability density function; Pulp manufacturing; Remotely operated vehicles; Robot sensing systems; Sensor systems; Trajectory; Vehicle detection; Vehicle dynamics;
Conference_Titel :
Robotics and Automation, 2005. ICRA 2005. Proceedings of the 2005 IEEE International Conference on
Print_ISBN :
0-7803-8914-X
DOI :
10.1109/ROBOT.2005.1570598