Unmanned Aerial Systems for Wildland and Forest Fires

Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. In recent years, we witness an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small scale environments. However, wildfires cover large areas making some of the proposed ground-based systems unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial Systems (UAS) were proposed. UAS have proven to be useful due to their maneuverability, allowing for the implementation of remote sensing, allocation strategies and task planning. They can provide a low-cost alternative for the prevention, detection and real-time support of firefighting. In this paper we review previous work related to the use of UAS in wildfires. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered. In addition, we present some of the recent frameworks proposing the use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at: https://doi.org/10.3390/drones501001

DEVELOPMENT OF A DESIGN METHOD TO REDUCE CHANGE PROPAGATION EFFECTS

ABSTRACT This dissertation presents a design method to reduce engineering changes caused due to change propagation effect. The method helps designers to systematically plan a verification, validation, and test (VV&T) plan. The rationale behind such a method is founded on a well-accepted principle that a robust validation plan can reduce design changes. However, such method has not yet been developed in mechanical engineering domain, so a method from software engineering has been adopted and extended to address the limitations in the existing design evaluation tools. Tools extensively used in industry, such as FMEA, and in academia have been reviewed to determine if they can identify different propagation pathways including variant, behavior, organization, and geometric pathways. As a result, it is found that variant and organizational pathways are not identified in any of these tools -- propagation in these pathways have caused major product failure in commercial vehicle and automatic fire sprinkler manufacturing industries. A seven-step VV&T method is proposed to address the aforementioned gap in which each step is tailored to suit mechanical engineering needs. The major contribution is developing the construct to identify variant and organization pathways and a prescriptive method. It has been validated in a leading commercial vehicle manufacturer, one of the passenger car manufacturing giants, and an automatic fire sprinkler manufacturer. The results from these three companies indicate the proposed VV&T method enables designers to identify variant and organizational pathways and evaluate them, which in turn can reduce design changes due to propagation effects. Objective evidence obtained from the fire sprinkler manufacturing company supports this claim. \u27If we know what assembly combination to test with, testing is not a problem...and if it can prevent a failure of this magnitude --I think this method can --it can be extremely beneficial...\u27 - Project engineer, commercial vehicle manufacture

Application of big data in transportation safety analysis using statistical and deep learning methods

The emergence of new sensors and data sources provides large scale high-resolution big data from instantaneous vehicular movements, driver decision and states, surrounding environment, roadway characteristics, weather condition, etc. Such a big data can be served to expand our understanding regarding the current state of the transportation and help us to proactively evaluate and monitor the system performance. The key idea behind this dissertation is to identify the moments and locations where drivers are exhibiting different behavior comparing to the normal behavior. The concept of driving volatility is utilized which quantifies deviation from normal driving in terms of variations in speed, acceleration/deceleration, and vehicular jerk. This idea is utilized to explore the association of volatility in different hierarchies of transportation system, i.e.: 1) Instance level; 2) Event level; 3) Driver level; 4) Intersection level; and 5) Network level. In summary, the main contribution of this dissertation is exploring the association of variations in driving behavior in terms of driving volatility at different levels by harnessing big data generated from emerging data sources under real-world condition, which is applicable to the intelligent transportation systems and smart cities. By analyzing real-world crashes/near-crashes and predicting occurrence of extreme event, proactive warnings and feedback can be generated to warn drivers and adjacent vehicles regarding potential hazard. Furthermore, the results of this study help agencies to proactively monitor and evaluate safety performance of the network and identify locations where crashes are waiting to happen. The main objective of this dissertation is to integrate big data generated from emerging sources into safety analysis by considering different levels in the system. To this end, several data sources including Connected Vehicles data (with more than 2.2 billion seconds of observations), naturalistic driving data (with more than 2 million seconds of observations from vehicular kinematics and driver behavior), conventional data on roadway factors and crash data are integrated