Autonomous Fire Suppression Using Feedback Control for Robotic Firefighting

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Virginia Tech


There is an increasing demand for robotics in dangerous and extreme conditions to limit human exposure and risk. An area in which robots are being considered as a support tool is in firefighting operations to reduce the number of firefighter injuries and deaths. One such application is to increase firefighting performance through localized fire suppression.

This research focused on developing an autonomous suppression system for use on a mobile robotic platform. This included a real-time close proximity fire suppression approach, appropriate feature selection and probabilistic classification of water leaks and sprays, real-time trajectory estimation, and a feedback controller for error correction in longer-range firefighting. The close proximity suppression algorithm uses IR fire detection IR stereo processing to localize a fire. Feedback of the fire size and fire target was used to manipulate the nozzle for effective placement of the suppressant onto the fire and experimentally validated with tests in high and low visibility environments.

To improve performance of autonomous suppression and for inspection tasks, identification of water sprays and leaks is a critical component. Bayesian classification was used to identify the features associated with water leaks and sprays in thermal images. Appropriate first and second order features were selected by using a multi-objective genetic algorithm optimization. Four textural features were selected as a method of discriminating water sprays and leaks from other non-water, high motion objects.

Water classification was implemented into a real-time suppression system as a method of determining the yaw and pitch angle of a water nozzle. Estimation of the angle orientation provided an error estimate between the current path and desired nozzle orientation. A proportional-integral (PI) controller was used to correct for forced errors in fire targeting and performance and response was shown through indoor and outdoor suppression tests with wood-crib fires. The autonomous suppression algorithm was demonstrated through fire testing to be at least three times faster compared with suppression by an operator using tele-operation.



Robotics, Firefighting, Machine Vision, Autonomous Fire Suppression, Probabilistic Classification, Feedback Control