A Study of Applying Decentralized Constraint Optimization to Observation Tasks with Mobile Sensors for Observation and Position Estimation Based on Range Data

Abstract

Observation with multiple autonomous mobile sensors has been studied for wide area surveillance and monitoring hazardous zones. In those observation tasks, it is necessary to dynamically and cooperatively determine the actions of sensors for capturing environment and estimating sensors' locations based on specific sensor data. As a case study, we apply a decentralized constraint optimization method to the decision making in an observation task in which the observation of environment and the position estimation of sensors are performed using range data from the sensors moving on a floor of facility. We experimentally investigate the proposed approach with a simulation and show that the moves of agents are determined according to the situations of observation and estimated positions of the agents.