Multi-vehicle Bayesian Search for Multiple Lost Targets

• Published in: Proceedings of the 2005 IEEE International Conference on Robotics and Automation pp. 3169 - 3174
• Main Authors: El-Mane Wong, Bourgault, F., Furukawa, T.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Australia; Bayesian methods; Probability density function; Pulp manufacturing; Remotely operated vehicles; Robot sensing systems; Sensor systems; Trajectory; Vehicle detection; Vehicle dynamics
• ISBN: 078038914X; 9780780389144
• ISSN: 1050-4729; 2577-087X
• DOI: 10.1109/ROBOT.2005.1570598

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.