Design of Unmanned Vehicle System for Disaster Detection

Chen, Mingchih; Chen, Chien‐Hsing; Huang, Mingsheng; Ciou, Jheng-Yu; Zhang, Guotai

doi:10.1155/2015/784298

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…In addition, the highly selective MQ-4 semiconductor sensor for CH4 detection ( Figure 6) [42] is a good candidate for deployment with UASs. Although the MQ-4 sensor has been designed to monitor CH4, a lower selectivity for detecting the gases propane and butane is possible [42].…”

Section: Implementation Of Sensor Technology Onboard Uassmentioning

confidence: 99%

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Unmanned Aerial Systems for Monitoring Trace Tropospheric Gases

Schuyler

Guzmán

2017

Atmosphere

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Abstract:The emission of greenhouse gases (GHGs) has changed the composition of the atmosphere during the Anthropocene. Accurately documenting the sources and magnitude of GHGs emission is an important undertaking for discriminating the contributions of different processes to radiative forcing. Currently there is no mobile platform that is able to quantify trace gases at altitudes <100 m above ground level that can achieve spatiotemporal resolution on the order of meters and seconds. Unmanned aerial systems (UASs) can be deployed on-site in minutes and can support the payloads necessary to quantify trace gases. Therefore, current efforts combine the use of UASs available on the civilian market with inexpensively designed analytical systems for monitoring atmospheric trace gases. In this context, this perspective introduces the most relevant classes of UASs available and evaluates their suitability to operate three kinds of detectors for atmospheric trace gases. The three subsets of UASs discussed are: (1) micro aerial vehicles (MAVs); (2) vertical take-off and landing (VTOL); and, (3) low-altitude short endurance (LASE) systems. The trace gas detectors evaluated are first the vertical cavity surface emitting laser (VCSEL), which is an infrared laser-absorption technique; second two types of metal-oxide semiconductor sensors; and, third a modified catalytic type sensor. UASs with wingspans under 3 m that can carry up to 5 kg a few hundred meters high for at least 30 min provide the best cost and convenience compromise for sensors deployment. Future efforts should be focused on the calibration and validation of lightweight analytical systems mounted on UASs for quantifying trace atmospheric gases. In conclusion, UASs offer new and exciting opportunities to study atmospheric composition and its effect on weather patterns and climate change.

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Section: Implementation Of Sensor Technology Onboard Uassmentioning

confidence: 99%

“…Although the MQ-4 sensor has been designed to monitor CH4, a lower selectivity for detecting the gases propane and butane is possible [42]. The cheap and commercially available MQ-4 sensor can be easily paired to a microcontroller mounted to either a fixed-wing or rotary-wing UAS.…”

Section: Implementation Of Sensor Technology Onboard Uassmentioning

confidence: 99%

Unmanned Aerial Systems for Monitoring Trace Tropospheric Gases

Schuyler

Guzmán

2017

Atmosphere

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“…The key idea of the LSAR algorithm is that in natural disasters there is a center where most of the survivors are located. Assuming that these centers can be detected [48]- [50], the SAR mission should be planned in a way that focuses more attention on the center, and gradually less attention with increasing distance from the center [51].…”

Section: The Lsar Algorithmmentioning

confidence: 99%

“…The proposed LSAR algorithm is a centralized approach. The cloud server receives information on the disaster center [48]- [50], as an emergency call from the area that has a number of survivors in unknown locations (Figure 4 (a)). In addition, the cloud server running the LSAR algorithm divides the disaster area into a set of incremental, numbered, square shaped layers L (Figure 4 (b)).…”

Section: A System Modelmentioning

confidence: 99%

LSAR: Multi-UAV Collaboration for Search and Rescue Missions

2019

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In this paper, we consider the use of a team of multiple unmanned aerial vehicles (UAVs) to accomplish a search and rescue (SAR) mission in the minimum time possible while saving the maximum number of people. A novel technique for the SAR problem is proposed and referred to as the layered search and rescue (LSAR) algorithm. The novelty of LSAR involves simulating real disasters to distribute SAR tasks among UAVs. The performance of LSAR is compared, in terms of percentage of rescued survivors and rescue and execution times, with the max-sum, auction-based, and locust-inspired approaches for multi UAV task allocation (LIAM) and opportunistic task allocation (OTA) schemes. The simulation results show that the UAVs running the LSAR algorithm on average rescue approximately 74% of the survivors, which is 8% higher than the next best algorithm (LIAM). Moreover, this percentage increases with the number of UAVs, almost linearly with the least slope, which means more scalability and coverage is obtained in comparison to other algorithms. In addition, the empirical cumulative distribution function of LSAR results shows that the percentages of rescued survivors clustered around the [78%-100%] range under an exponential curve, meaning most results are above 50%. In comparison, all the other algorithms have almost equal distributions of their percentage of rescued survivor results. Furthermore, because the LSAR algorithm focuses on the center of the disaster, it finds more survivors and rescues them faster than the other algorithms, with an average of 55%∼77%. Moreover, most registered times to rescue survivors by LSAR are bounded by a time of 04:50:02 with 95% confidence for a one-month mission time.INDEX TERMS Autonomous agents, drones, search and rescue, unmanned aerial vehicles.

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