Computational multicopter design

Du, Tao; Schulz, Adriana; Zhu, Bo; Bickel, Bernd; Matusik, Wojciech

doi:10.1145/2980179.2982427

Cited by 61 publications

(38 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Multispectral sensors may help address this problem. Widely used for landcover classification and vegetation monitoring [68][69][70][71][72][73] this technology uses green, red, red-edge and near infrared wavebands to capture detail not available to standard RGB cameras. Green vegetation materials are characterized by high reflectance in the near infra-red (NIR) domain (outside of the spectral range of human vision); a multispectral camera can provide useful contrast to discriminate between live and dead vegetation.…”

Section: Discussionmentioning

confidence: 99%

Assessment of Chimpanzee Nest Detectability in Drone-Acquired Images

Bonnin

Andel²,

Kerby

et al. 2018

Drones

View full text Add to dashboard Cite

Abstract:As with other species of great apes, chimpanzee numbers have declined over the past decades. Proper conservation of the remaining chimpanzees requires accurate and frequent data on their distribution and density. In Tanzania, 75% of the chimpanzees live at low densities on land outside national parks and little is known about their distribution, density, behavior or ecology. Given the sheer scale of chimpanzee distribution across western Tanzania (>20,000 km 2 ), we need new methods that are time and cost efficient while providing precise and accurate data across broad spatial scales. Scientists have recently demonstrated the usefulness of drones for detecting wildlife, including apes. Whilst direct observation of chimpanzees is unlikely given their elusiveness, we investigated the potential of drones to detect chimpanzee nests in the Issa valley, western Tanzania. Between 2015 and 2016, we tested and compared the capabilities of two fixed-wing drones. We surveyed twenty-two plots (50 × 500 m) in gallery forests and miombo woodlands to compare nest observations from the ground with those from the air. We performed mixed-effects logistic regression models to evaluate the impact of image resolution, seasonality, vegetation type, nest height and color on nest detectability. An average of 10% of the nests spotted from the ground were detected from the air. From the factors tested, only image resolution significantly influenced nest detectability in drone-acquired images. We discuss the potential, but also the limitations, of this technology for determining chimpanzee distribution and density and to provide guidance for future investigations on the use of drones for ape population surveys. Combining traditional and novel technological methods of surveying allows more accurate collection of data on animal distribution and habitat connectivity that has important implications for ape conservation in an increasingly anthropogenically-disturbed landscape.

show abstract

Section: Discussionmentioning

confidence: 99%

Assessment of Chimpanzee Nest Detectability in Drone-Acquired Images

Bonnin

Andel²,

Kerby

et al. 2018

Drones

View full text Add to dashboard Cite

show abstract

“…Multicopter Design [91] Design DJI [107], AeroVironment [108], Parrot [109] D-MUNS [96] R&D UAVNet security, Academic Curriculum…”

Section: Discussionmentioning

confidence: 99%

“…The goal is to find an optimized design that involves adjusting various settings and finally arriving at a trade-off between the chosen metrics. This is a real-time interactive simulator; users can change the control parameters and shapes during simulation [91,92,93,94,95].…”

Section: Computational Multicopter Designmentioning

confidence: 99%

Application specific drone simulators: Recent advances and challenges

Mairaj

Baba

Javaid

2019

Simulation Modelling Practice and Theory

107

View full text Add to dashboard Cite

Over the past two decades, Unmanned Aerial Vehicles (UAVs), more commonly known as drones, have gained a lot of attention, and are rapidly becoming ubiquitous because of their diverse applications such as surveillance, disaster management, pollution monitoring, film-making, and military reconnaissance. However, incidents such as fatal system failures, malicious attacks, and disastrous misuses have raised concerns in the recent past. Security and viability concerns in drone-based applications are growing at an alarming rate. Besides, UAV networks (UAVNets) are distinctive from other ad-hoc networks. Therefore, it is necessary to address these issues to ensure proper functioning of these UAVs while keeping their uniqueness in mind. Furthermore, adequate security and functionality require the consideration of many parameters that may include an accurate cognizance of the working mechanism of vehicles, geographical and weather conditions, and UAVNet communication. This is achievable by creating a simulator that includes these aspects. A performance evaluation through relevant drone simulator becomes indispensable procedure to test features, configurations, and designs to demonstrate superiority to comparative schemes and suitability. Thus, it becomes of paramount importance to establish the credibility of simulation results by investigating the merits and limitations of each simulator prior to selection. Based on this motivation, we present a comprehensive survey of current drone simulators. In addition, open research issues and research challenges are discussed and presented.

show abstract

“…These methods generate smooth deformations which have been shown to work on shapes that are more organic and for fabrication methods such as 3D printing [Prévost et al 2013] or water jet cutting of smooth 2D parametric curves [Bharaj et al 2015]. For more mechanical structures such as furniture [Schulz et al 2014] and drones [Du et al 2016], cages techniques have been applied together with algebraic techniques to preserve discrete regular patterns [Bokeloh et al 2012]. These, however, have been limited to very small datasets, with highly constrained variations (such as dimensions of wooden plates or carbon tubes) that require laborious annotations from mechanical engineers.…”

Section: Related Workmentioning

confidence: 99%

Interactive design space exploration and optimization for CAD models

Schulz¹,

Xu²,

Zhu³

et al. 2017

ACM Trans. Graph.

Self Cite

View full text Add to dashboard Cite

Fig. 1. Our method harnesses data from CAD systems, which are parametric from construction and capture the engineer's design intent, but require long regeneration times and output meshes with different combinatorics. We sample the parametric space in an adaptive grid and propose techniques to smoothly interpolate this data. We show how this can be used for shape optimization and to drive interactive exploration tools that allow designers to visualize the shape space while geometry and physical properties are updated in real time.Computer Aided Design (CAD) is a multi-billion dollar industry used by almost every mechanical engineer in the world to create practically every existing manufactured shape. CAD models are not only widely available but also extremely useful in the growing field of fabrication-oriented design because they are parametric by construction and capture the engineer's design intent, including manufacturability. Harnessing this data, however, is challenging, because generating the geometry for a given parameter value requires time-consuming computations. Furthermore, the resulting meshes have different combinatorics, making the mesh data inherently discontinuous with respect to parameter adjustments. In our work, we address these challenges and develop tools that allow interactive exploration and optimization of parametric CAD data. To achieve interactive rates, we use precomputation on an adaptively sampled grid and propose a novel scheme for interpolating in this domain where each sample is a mesh with different combinatorics. Specifically, we extract partial correspondences from CAD representations for local mesh morphing and propose a novel interpolation method for adaptive grids that is both continuous/smooth and local (i.e., the influence of each sample is constrained to the local regions where mesh morphing can be computed). We show examples of how our method can be used to interactively visualize and optimize objects with a variety of physical properties.

show abstract

Computational multicopter design

Cited by 61 publications

References 50 publications

Assessment of Chimpanzee Nest Detectability in Drone-Acquired Images

Assessment of Chimpanzee Nest Detectability in Drone-Acquired Images

Application specific drone simulators: Recent advances and challenges

Interactive design space exploration and optimization for CAD models

Contact Info

Product

Resources

About