scholarly journals Design of Field Experiments for Adaptive Sampling of the Ocean with Autonomous Vehicles

2010 ◽  
Author(s):  
H. Zheng ◽  
B. H. Ooi ◽  
W. Cho ◽  
M. H. Dao ◽  
P. Tkalich ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Adaptive Sampling ◽  
Field Experiments

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.

scholarly journals Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments

2018 ◽  
Vol 89 ◽  
pp. 205-221 ◽  
Author(s):  
Raphael E. Stern ◽  
Shumo Cui ◽  
Maria Laura Delle Monache ◽  
Rahul Bhadani ◽  
Matt Bunting ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Field Experiments ◽  
Stop And Go

Mobile Sensor Networks and Control: Adaptive Sampling of Spatiotemporal Processes

2020 ◽  
Vol 3 (1) ◽  
pp. 91-114
Author(s):  
Derek A. Paley ◽  
Artur Wolek
Keyword(s):  
Sensor Networks ◽  
Autonomous Vehicles ◽  
Adaptive Sampling ◽  
Mobile Sensor Networks ◽  
Discrete Models ◽  
Cooperative Groups ◽  
Mobile Sensor ◽  
Ambient Flow ◽  
Fluid Sensor ◽  
Wide Range

The control of mobile sensor networks uses sensor measurements to update a model of an unknown or estimated process, which in turn guides the collection of subsequent measurements—a feedback control framework called adaptive sampling. Applications for adaptive sampling exist in a wide range of settings, especially for unmanned or autonomous vehicles that can be deployed cheaply and in cooperative groups. The dynamics of mobile sensor platforms are often simplified to planar self-propelled particles subject to the ambient flow of the surrounding fluid. Sensor measurements are assimilated into continuous or discrete models of the process of interest, which in general can vary in space and time. The variability of the estimated process is one metric to score future candidate sampling trajectories, along with information- and uncertainty-based metrics. Sampling tasks are allocated to the network using centralized or decentralized optimization, in order to avoid redundant measurements and observational gaps.

scholarly journals Rapidly-Exploring Adaptive Sampling Tree*: A Sample-Based Path-Planning Algorithm for Unmanned Marine Vehicles Information Gathering in Variable Ocean Environments

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2515 ◽  
Author(s):  
Chengke Xiong ◽  
Hexiong Zhou ◽  
Di Lu ◽  
Zheng Zeng ◽  
Lian Lian ◽  
...  
Keyword(s):  
Path Planning ◽  
Adaptive Sampling ◽  
Field Experiments ◽  
Optimal Path ◽  
Information Gathering ◽  
Continuous Space ◽  
Marine Vehicles ◽  
Planning Algorithm ◽  
Tournament Selection ◽  
Path Planning Algorithm

This research presents a novel sample-based path planning algorithm for adaptive sampling. The goal is to find a near-optimal path for unmanned marine vehicles (UMVs) that maximizes information gathering over a scientific interest area, while satisfying constraints on collision avoidance and pre-specified mission time. The proposed rapidly-exploring adaptive sampling tree star (RAST*) algorithm combines inspirations from rapidly-exploring random tree star (RRT*) with a tournament selection method and informative heuristics to achieve efficient searching of informative data in continuous space. Results of numerical experiments and proof-of-concept field experiments demonstrate the effectiveness and superiority of the proposed RAST* over rapidly-exploring random sampling tree star (RRST*), rapidly-exploring adaptive sampling tree (RAST), and particle swarm optimization (PSO).

Cross-domain Learning Using Optimized Pseudo Labels: Towards Adaptive Car Detection in Different Weather Conditions and Urban Cities

2021 ◽  
Author(s):  
ke wang ◽  
Lianhua Zhang ◽  
Qin Xia ◽  
Liang Pu ◽  
Junlan Chen
Keyword(s):  
Autonomous Vehicles ◽  
Adaptive Sampling ◽  
Domain Adaptation ◽  
Weather Conditions ◽  
Training Data ◽  
Target Domain ◽  
Average Precision ◽  
Sharp Drop ◽  
Car Detection ◽  
Hard Samples

Abstract Convolutional neural networks (CNN) based object detection usually assumes that training and test data have the same distribution, which, however, does not always hold in real-world applications. In autonomous vehicles, the driving scene (target domain) consists of unconstrained road environments which cannot all possibly be observed in training data (source domain) and this will lead to a sharp drop in the accuracy of the detector. In this paper, we propose a domain adaptation framework based on pseudo-labels to solve the domain shift. First, the pseudo-labels of the target domain images are generated by the baseline detector (BD) and optimized by our data optimization module to correct the errors. Then, the hard samples in a single image are labeled based on the optimization results of pseudo-labels. The adaptive sampling module is approached to sample target domain data according to the number of hard samples per image to select more effective data. Finally, a modified knowledge distillation loss is applied in the retraining module, and we investigate two ways of assigning soft-labels to the training examples from the target domain to retrain the detector. We evaluate the average precision of our approach in various source/target domain pairs and demonstrate that the framework improves over 10% average precision of BD on multiple domain adaptation scenarios on the Cityscapes, KITTI, and Apollo datasets.

scholarly journals Advanced e-Call Support Based on Non-Intrusive Driver Condition Monitoring for Connected and Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8272
Author(s):  
Marius Minea ◽  
Cătălin Marian Dumitrescu ◽  
Ilona Mădălina Costea
Keyword(s):  
Autonomous Vehicles ◽  
Field Experiments ◽  
The Internet ◽  
Cognitive State ◽  
Negative Consequences ◽  
Road Accidents ◽  
Timely Manner ◽  
Vital Functions ◽  
Sensor Structure ◽  
Call System

Background: The growth of the number of vehicles in traffic has led to an exponential increase in the number of road accidents with many negative consequences, such as loss of lives and pollution. Methods: This article focuses on using a new technology in automotive electronics by equipping a semi-autonomous vehicle with a complex sensor structure that is able to provide centralized information regarding the physiological signals (Electro encephalogram—EEG, electrocardiogram—ECG) of the driver/passengers and their location along with indoor temperature changes, employing the Internet of Things (IoT) technology. Thus, transforming the vehicle into a mobile sensor connected to the internet will help highlight and create a new perspective on the cognitive and physiological conditions of passengers, which is useful for specific applications, such as health management and a more effective intervention in case of road accidents. These sensor structures mounted in vehicles will allow for a higher detection rate of potential dangers in real time. The approach uses detection, recording, and transmission of relevant health information in the event of an incident as support for e-Call or other emergency services, including telemedicine. Results: The novelty of the research is based on the design of specialized non-invasive sensors for the acquisition of EEG and ECG signals installed in the headrest and backrest of car seats, on the algorithms used for data analysis and fusion, but also on the implementation of an IoT temperature measurement system in several points that simultaneously uses sensors based on MEMS technology. The solution can also be integrated with an e-Call system for telemedicine emergency assistance. Conclusion: The research presents both positive and negative results of field experiments, with possible further developments. In this context, the solution has been developed based on state-of-the-art technical devices, methods, and technologies for monitoring vital functions of the driver/passengers (degree of fatigue, cognitive state, heart rate, blood pressure). The purpose is to reduce the risk of accidents for semi-autonomous vehicles and to also monitor the condition of passengers in the case of autonomous vehicles for providing first aid in a timely manner. Reported abnormal values of vital parameters (critical situations) will allow interveneing in a timely manner, saving the patient’s life, with the support of the e-Call system.

scholarly journals Sensitivity to observations applied to FASTEX cases

2001 ◽  
Vol 8 (6) ◽  
pp. 467-481 ◽  
Author(s):  
A. Doerenbecher ◽  
T. Bergot
Keyword(s):  
Adaptive Sampling ◽  
Field Experiments ◽  
Background Field ◽  
Point Of View ◽  
Vertical Extension ◽  
Adaptive Observations ◽  
Targeted Observations ◽  
Definition Of ◽  
Nwp Model ◽  
Assimilation Scheme

Abstract. The concept of targeted observations was implemented during field experiments such as FASTEX, NORPEX or WSRP in order to cope with some predictability problems. The techniques of targeting used at that moment (adjoint-based or ensemble transform methods) lead to quite disappointing results: the efficiency of the additional observations deployed over sensitive areas did not turn out to remain consistent from one case to another. The influence of targeted observations on the forecasts could sometimes consist of strong improvements, or sometimes strong degradations. It turns out that the latter failure explains why the concept of optimal sampling arose. The efficiency of adaptive sampling appears to depend on the assimilation scheme that deals with the observations. It is then very useful to integrate the nature of the assimilation algorithm, as well as the deployment of the conventional network of observations (redundancy issues between targeted and conventional network) in the definition of the sensitive pattern to be sampled. Therefore, we chose the tool of the sensitivity to observations to allow us to test such an approach. The sensitivity to targeted observations (that utilizes the adjoint of the linearized NWP model and the adjoint of the assimilation operator) seems to be a suitable tool to obtain an insight into the tricky issue of the optimization of the sampling strategies. To understand better the intrinsic patterns and the influence of the 3D-Var assimilation scheme on the sensitive structures to be sampled, we present here some detailed results on a FASTEX targeting case. We focus on the dropsondes deployed by the Gulfstream IV (jet-aircraft) along its first flight during Intense Observing Period 17 that started on the 17 February 1997. The sensitivity to observation is used as a diagnostic tool for studing targeting from a critical point of view. It is shown that assimilation processes can have an important effect on the classical sensitivity fields, and particularly on their vertical extension. For example, in the studied case, the classical sensitivity fields remain at a lower level than 400 hPa, whereas the sensitivity to observations stretches up to 250 hPa. However, the maximum values can be found at approximately 700 hPa in both sensitivity fields. The studied case shows that the efficiency of observations depends not only on the sensitivity but also on the deviations between the observations and the background field. An example of the use of this diagnosis for comparing the relative efficiency of different kinds of observations is also presented. This work points out that it is very complicated to optimize the efficiency of adaptive observations, and that the assimilation of an entire set of observations (both conventional and adaptive network) needs to be considered.

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 A Backseat Control Architecture for a Slocum Glider

2021 ◽  
Vol 9 (5) ◽  
pp. 532
Author(s):  
Yaomei Wang ◽  
Craig Bulger ◽  
Worakanok Thanyamanta ◽  
Neil Bose
Keyword(s):  
Adaptive Sampling ◽  
Field Experiments ◽  
Underwater Vehicles ◽  
Vehicle Control ◽  
External Control ◽  
Control Architecture ◽  
Control Algorithms ◽  
Robot Operating System ◽  
Data Adaptive ◽  
Set Up

Adaptive sampling provides an innovative and favorable method of improving the effectiveness of underwater vehicles in collecting data. Adaptive sampling works by controlling an underwater vehicle by using measurements from sensors and states of the vehicle. A backseat driver system was developed in this work and installed on a Slocum glider to equip it with an ability to perform adaptive sampling tasks underwater. This backseat driver communicated with the main vehicle control system of the glider through a robot operating system (ROS) interface. The external control algorithms were implemented through ROS nodes, which subscribed simulated sensor measurements and states of the glider and published desired states to the glider. The glider was set up in simulation mode to test the performance of the backseat driver as integrated into the control architecture of the glider. Results from the tests revealed that the backseat driver could effectively instruct the depth, heading, and waypoints as well as activate or deactivate behaviors adaptively. The developed backseat driver will be tested in future field experiments with sensors included and safety rules implemented before being applied in adaptive sampling missions such as adaptive oil spill sampling.

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