Preliminary environmental risk consideration for small UAV ground risk mapping

• Main Authors: Chung, Han Jie, Mohd Hasrizam Che Man, Sivakumar, Anush Kumar, Low, Kin Huat
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2022
• Subjects: Engineering::Mechanical engineering; Data Analysis; Temperature; Wind Speed
• Online Access: https://hdl.handle.net/10356/160547
• Institution: Nanyang Technological University

Small unmanned aerial vehicles (sUAV) or 'drones' are projected to rapidly develop in the future and be employed for a multitude of applications such as parcel delivery, search and rescue, surveillance monitoring, and farming. Consequently, an increase in sUAV traffic may lead to a higher likelihood of failure, and crash occur primarily due to environmental factors. Hence, the objective of this study was to investigate the influence of environmental factors on sUAV operations.The study identifies three environmental risk categories relevant to Singapore’s context, namely weather, obstacle, and signal. Subsequently, Python code was developed to perform data analysis on environmental risk data gathered from open source, relevant agencies, and literature surveys. Three environmental risk factors were considered for proof of concept, namely rainfall, windspeed, bird population, and cell tower signal strength.From the data analysis, the average daily rainfall across weather stations in Singapore was observed to be 8.58 mm. In August, weather stations in West of Singapore, such as Choa Chu Kang and Tengah, reported the highest rainfall, approximately 22.65 mm. In contrast, weather stations in the East such as Changi and Pasir Ris reported the lowest rainfall near 0.036 mm in February. Data of the three most invasive bird species in Singapore was examined. The site of Chinese Garden in West Singapore reported the highest bird population of 432 birds per site. The study of bird population data potentially provides an insight into the possibility of sUAV bird strike events. Similarly, signal strength data from cell towers in Singapore was studied.Overall environmental risk-tiering can be visualized using all the analyzed environmental factors data on a risk map. The regions with high rainfall, increased bird population per site and strong signal interference can be extracted.These high-risk locations can be expected to be more hazardous for sUAVs to fly in. Findings for environmental risk-tiering and risk-mapping are preliminary and based on the available environmental data as identified in this study. Further research will be done to include other environmental risk factors and to generate better accuracy of the environmental risk-tier map. Insights from this study will facilitate the demarcation of low, medium, and high-risk areas for risk assessment of sUAVs operation in Singapore.