scholarly journals An autonomous drone for search and rescue in forests using airborne optical sectioning

2021 ◽  
Vol 6 (55) ◽  
pp. eabg1188
Author(s):  
D. C. Schedl ◽  
I. Kurmi ◽  
O. Bimber
Keyword(s):  
Adaptive Sampling ◽  
Field Experiments ◽  
Sampling Technique ◽  
Flight Path ◽  
Search And Rescue ◽  
Network Coverage ◽  
Optical Sectioning ◽  
Average Precision ◽  
Time Critical ◽  
Online Sampling

Autonomous drones will play an essential role in human-machine teaming in future search and rescue (SAR) missions. We present a prototype that finds people fully autonomously in densely occluded forests. In the course of 17 field experiments conducted over various forest types and under different flying conditions, our drone found, in total, 38 of 42 hidden persons. For experiments with predefined flight paths, the average precision was 86%, and we found 30 of 34 cases. For adaptive sampling experiments (where potential findings are double-checked on the basis of initial classification confidences), all eight hidden persons were found, leading to an average precision of 100%, whereas classification confidence was increased on average by 15%. Thermal image processing, classification, and dynamic flight path adaptation are computed on-board in real time and while flying. We show that deep learning–based person classification is unaffected by sparse and error-prone sampling within straight flight path segments. This finding allows search missions to be substantially shortened and reduces the image complexity to 1/10th when compared with previous approaches. The goal of our adaptive online sampling technique is to find people as reliably and quickly as possible, which is essential in time-critical applications, such as SAR. Our drone enables SAR operations in remote areas without stable network coverage, because it transmits to the rescue team only classification results that indicate detections and can thus operate with intermittent minimal-bandwidth connections (e.g., by satellite). Once received, these results can be visually enhanced for interpretation on remote mobile devices.

scholarly journals Acceleration-Aware Path Planning with Waypoints

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 143
Author(s):  
Rudolf Ortner ◽  
Indrajit Kurmi ◽  
Oliver Bimber
Keyword(s):  
Path Planning ◽  
Probability Distributions ◽  
Search And Rescue ◽  
Optical Sectioning ◽  
Field Methods ◽  
Autonomous Search ◽  
Acceleration And Deceleration ◽  
Probability Accumulation ◽  
Time Critical ◽  
Local Gradient

In this article we demonstrate that acceleration and deceleration of direction-turning drones at waypoints have a significant influence to path planning which is important to be considered for time-critical applications, such as drone-supported search and rescue. We present a new path planning approach that takes acceleration and deceleration into account. It follows a local gradient ascend strategy which locally minimizes turns while maximizing search probability accumulation. Our approach outperforms classic coverage-based path planning algorithms, such as spiral- and grid-search, as well as potential field methods that consider search probability distributions. We apply this method in the context of autonomous search and rescue drones and in combination with a novel synthetic aperture imaging technique, called Airborne Optical Sectioning (AOS), which removes occlusion of vegetation and forest in real-time.

Beyond the Known: Detecting Novel Feasible Domains Over an Unbounded Design Space

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Wei Chen ◽  
Mark Fuge
Keyword(s):  
Real World ◽  
Adaptive Sampling ◽  
Design Space ◽  
Fundamental Problem ◽  
Domain Boundary ◽  
Sampling Technique ◽  
Standard Test ◽  
Worst Case ◽  
Trade Offs ◽  
Design Variables

To solve a design problem, sometimes it is necessary to identify the feasible design space. For design spaces with implicit constraints, sampling methods are usually used. These methods typically bound the design space; that is, limit the range of design variables. But bounds that are too small will fail to cover all possible designs, while bounds that are too large will waste sampling budget. This paper tries to solve the problem of efficiently discovering (possibly disconnected) feasible domains in an unbounded design space. We propose a data-driven adaptive sampling technique—ε-margin sampling, which learns the domain boundary of feasible designs and also expands our knowledge on the design space as available budget increases. This technique is data-efficient, in that it makes principled probabilistic trade-offs between refining existing domain boundaries versus expanding the design space. We demonstrate that this method can better identify feasible domains on standard test functions compared to both random and active sampling (via uncertainty sampling). However, a fundamental problem when applying adaptive sampling to real world designs is that designs often have high dimensionality and thus require (in the worst case) exponentially more samples per dimension. We show how coupling design manifolds with ε-margin sampling allows us to actively expand high-dimensional design spaces without incurring this exponential penalty. We demonstrate this on real-world examples of glassware and bottle design, where our method discovers designs that have different appearance and functionality from its initial design set.

scholarly journals Thermal Airborne Optical Sectioning

2019 ◽  
Vol 11 (14) ◽  
pp. 1668 ◽  
Author(s):  
Indrajit Kurmi ◽  
David C. Schedl ◽  
Oliver Bimber
Keyword(s):  
Search And Rescue ◽  
Synthetic Aperture ◽  
Heat Radiation ◽  
Border Control ◽  
Thermal Camera ◽  
Optical Sectioning ◽  
Control Applications ◽  
Precise Estimation ◽  
Rgb Images ◽  
Position And Orientation

We apply a multi-spectral (RGB and thermal) camera drone for synthetic aperture imaging to computationally remove occluding vegetation for revealing hidden objects, as required in archeology, search-and-rescue, animal inspection, and border control applications. The radiated heat signal of strongly occluded targets, such as a human bodies hidden in dense shrub, can be made visible by integrating multiple thermal recordings from slightly different perspectives, while being entirely invisible in RGB recordings or unidentifiable in single thermal images. We collect bits of heat radiation through the occluder volume over a wide synthetic aperture range and computationally combine them to a clear image. This requires precise estimation of the drone’s position and orientation for each capturing pose, which is supported by applying computer vision algorithms on the high resolution RGB images.

scholarly journals Physical Mechanisms Underlying Selected Adaptive Sampling Techniques for Tropical Cyclones

2013 ◽  
Vol 141 (11) ◽  
pp. 4008-4027 ◽  
Author(s):  
Brett T. Hoover ◽  
Chris S. Velden ◽  
Sharanya J. Majumdar
Keyword(s):  
Tropical Cyclone ◽  
Adaptive Sampling ◽  
Field Experiments ◽  
Guidance System ◽  
Naval Research ◽  
Initial Condition ◽  
Downstream Targets ◽  
Physical Mechanisms ◽  
Indirect Measures ◽  
Adaptive Observations

Abstract To efficiently and effectively prioritize resources, adaptive observations can be targeted by using some objective criteria to estimate the potential impact an initial condition perturbation (or analysis increment) in a specific region would have on the future forecast. Several objective targeting guidance techniques have been developed, including total-energy singular vectors (TESV), adjoint-derived sensitivity steering vectors (ADSSV), and the ensemble transform Kalman filter (ETKF), all of which were tested during the 2008 The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC) and the Office of Naval Research Tropical Cyclone Structure-2008 (TCS-08) field experiments. An intercomparison between these techniques is performed in order to find underlying physical mechanisms in the respective guidance products, based on four tropical cyclone (TC) cases from the T-PARC/TCS-08 field campaigns. It is found that the TESV energy norm and the ADSSV response function are largely indirect measures of the TC track divergence that can be produced by an initial condition perturbation, explaining the strong correlation between these products. The downstream targets routinely chosen by the ETKF guidance system are often not found in the TESV and ADSSV guidance products, and it is found that downstream perturbations can affect the steering of a TC through the development of a Rossby wave in the subtropics that modulates the strength of the nearby subtropical ridge. It is hypothesized that the ubiquitousness of these downstream targets in the ETKF is largely due to the existence of large uncertainties downstream of the TC that are not taken into consideration by either the TESV or ADSSV techniques.

Efficient Probabilistic Analysis and Design Optimization Using Data Classification Decision Boundaries

2010 ◽  
Author(s):  
Liping Wang ◽  
Arun K. Subramaniyan ◽  
Don Beeson
Keyword(s):  
Probabilistic Analysis ◽  
Adaptive Sampling ◽  
Optimization Design ◽  
Data Classification ◽  
Sampling Technique ◽  
Support Vector ◽  
Classification Methods ◽  
Analysis And Design ◽  
Using Data ◽  
Decision Boundaries

A new technique for performing probabilistic analysis and optimization design using data classification methods is investigated. The approach is based on nonlinear decision boundaries constructed from data classification methods. A statistical learning tool known as support vector machine (SVM) is used to construct the boundaries. An adaptive sampling technique is used to generate samples and update the approximated decision function. The proposed approach is demonstrated with several benchmark and engineering problems.

A dynamic adaptive sampling technique in frequency-domain transient analysis

2002 ◽  
Vol 44 (4) ◽  
pp. 522-528 ◽  
Author(s):  
Choy Yoong Tham ◽  
A. McCowen ◽  
M.S. Towers ◽  
D. Poljak
Keyword(s):  
Frequency Domain ◽  
Adaptive Sampling ◽  
Transient Analysis ◽  
Sampling Technique

Nonlinear Modelling and Identification of a Small-Scale Bridge

2008 ◽  
Author(s):  
Jose´ L. Zapico ◽  
David H. Bassir ◽  
Mari´a P. Gonza´lez-Marti´nez ◽  
Marta Garci´a-Die´guez
Keyword(s):  
Experimental Data ◽  
Adaptive Sampling ◽  
Dynamic Modelling ◽  
Sampling Technique ◽  
Elastic Stiffness ◽  
Small Scale ◽  
Finite Element Models ◽  
Viscous Damping ◽  
Slip Model ◽  
Linear Elastic

The dynamic modelling and identification of a small-scale bridge is addressed in this paper. Two finite element models with linear elastic stiffness and different damping modelling has been tried. They correspond to a linear viscous damping and a nonlinear elasto-slip one. Both models were fitted to the available experimental data by a novel adaptive sampling technique that was repeated several times. In all the runs the technique yielded consistent results, which confirms its robustness. The elasto-slip model gave excellent fitting to the experimental data, while the results of the linear viscous one were poor.

Image coding using an adaptive sampling technique

1989 ◽  
Vol 1 (1) ◽  
pp. 75-80 ◽  
Author(s):  
J.L de Bougrenet de la Tocnaye ◽  
J.F Cavassilas
Keyword(s):  
Image Coding ◽  
Adaptive Sampling ◽  
Sampling Technique
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