Firebots: Autonomous Fire Detecting Robots




Loss due to fire damage has always been a major area of concern for both industrial and residential areas. After performing more in depth research, it was identified that the US Navy has had an increased demand for improved fire detection technology in order to reduce costs incurred due to fire damage and false alarms [1]. A formal needs assessment was carried out in order to determine the design requirements of such technology. Through a problem formulation procedure, it was determined that the use of an autonomous robot equipped with advanced fire detection technology can minimize costs, reduce false alarms, and be highly extensible to other industries. The design of this system was broken down into three main components: navigation, localization and fire detection.

For the localization process, during the Initial Training Phase, two techniques are considered. The first, Manual Placement, involves recording Wi-fi Access Point Signal Strengths and LASER sensor values at location spaced by 1m intervals in both horizontal and vertical direction on the area to map, during ‘offline’ mode when the fires are not being detected. The second technique, Gaussian Process Regression, involves heavily computational stochastic calculations while the fire-detection is also going on, in ‘online’ mode. Since Gaussian Process Regression requires significant processing power and may interfere with fire-detection process, Manual Placement was chosen as selected technique. For Location Determination, two algorithms: Euclidean Distance and Monte Carlo were considered. Monte Carlo algorithm is able to give position information within an accuracy of 0.5m, as opposed to 1m. Since the desired accuracy is 0f 1m, Monte Carlo algorithm was chosen.

Navigation can be subcategorized into Global and Local Path Planning. Global path planning involves finding the most optimal path (in a known environment) from one point to another. Local path planning involves performing fast, real-time obstacle avoidance. The vector field histogram (VFH) algorithm is selected for as the local path planner due to its speed, efficiency and local path optimality. The Wavefront algorithm was chosen as the global path planner since it is computationally efficient and offers optimal global paths.

The latest high-end fire detection technology was researched in order to determine which sensor types produce the most accurate results. A variety of lower-end and higher-end sensors were grouped together in sensor packages, and evaluated based on a set of cost and performance criteria. Resultantly, a sensor suite was selected that is comprised of a high-end NetSafety Triple IR flame detector, a USB nightvision web camera, a Hamamatsu UVTron flame detector, and a combination photoelectric-ionization smoke alarm. The web camera is to be used with a custom built vision system using open source vision libraries. A method of detecting smoke and flames through video is also described with reference to recent research done on the matter. These sensors readings are all to be evaluated in parallel in order to have built in redundancy when deciding to sound a fire alert. This enables the reduction of false alarm signals, and allows for the detection of most common fires (class A fires) in the most common locations. Lastly, this system fell within our cost criterion of $5000/unit as the total system costs less than $4000.


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