Navigation, Path Planning, and Task Allocation Framework For Mobile Co-Robotic Service Applications in Indoor Building Environments

Recent advances in computing and robotics offer significant potential for improved autonomy in the operation and utilization of today’s buildings. Examples of such building environment functions that could be improved through automation include: a) building performance monitoring for real-time system control and long-term asset management; and b) assisted indoor navigation for improved accessibility and wayfinding. To enable such autonomy, algorithms related to task allocation, path planning, and navigation are required as fundamental technical capabilities. Existing algorithms in these domains have primarily been developed for outdoor environments. However, key technical challenges that prevent the adoption of such algorithms to indoor environments include: a) the inability of the widely adopted outdoor positioning method (Global Positioning System - GPS) to work indoors; and b) the incompleteness of graph networks formed based on indoor environments due to physical access constraints not encountered outdoors. The objective of this dissertation is to develop general and scalable task allocation, path planning, and navigation algorithms for indoor mobile co-robots that are immune to the aforementioned challenges. The primary contributions of this research are: a) route planning and task allocation algorithms for centrally-located mobile co-robots charged with spatiotemporal tasks in arbitrary built environments; b) path planning algorithms that take preferential and pragmatic constraints (e.g., wheelchair ramps) into consideration to determine optimal accessible paths in building environments; and c) navigation and drift correction algorithms for autonomous mobile robotic data collection in buildings. The developed methods and the resulting computational framework have been validated through several simulated experiments and physical deployments in real building environments. Specifically, a scenario analysis is conducted to compare the performance of existing outdoor methods with the developed approach for indoor multi-robotic task allocation and route planning. A simulated case study is performed along with a pilot experiment in an indoor built environment to test the efficiency of the path planning algorithm and the performance of the assisted navigation interface developed considering people with physical disabilities (i.e., wheelchair users) as building occupants and visitors. Furthermore, a case study is performed to demonstrate the informed retrofit decision-making process with the help of data collected by an intelligent multi-sensor fused robot that is subsequently used in an EnergyPlus simulation. The results demonstrate the feasibility of the proposed methods in a range of applications involving constraints on both the environment (e.g., path obstructions) and robot capabilities (e.g., maximum travel distance on a single charge). By focusing on the technical capabilities required for safe and efficient indoor robot operation, this dissertation contributes to the fundamental science that will make mobile co-robots ubiquitous in building environments in the near future.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143969/1/baddu_1.pd

Editorial : automation and artificial intelligence in construction and management of civil infrastructure

The construction industry, similar to other industries, has been moving toward automation and implementation of AI tools to enhance analysis, management and decision-making. The construction industry typically takes longer than other industries in inaugurating innovation and advanced technologies due to the complex nature of the industry. Research effort is deemed necessary to understand needs and overcome challenges in terms of studying the capabilities and potential of existing tools and technologies in areas related to construction engineering and management. The current Research Topic attempted to collect relevant research work in terms of automation and AI application in constructing new assets and managing existing infrastructure. Further research is needed to standardize these processes and align research work with current needs

Galaxy Zoo: Kinematics of strongly and weakly barred galaxies

We study the bar pattern speeds and corotation radii of 225 barred galaxies, using IFU data from MaNGA and the Tremaine-Weinberg method. Our sample, which is divided between strongly and weakly barred galaxies identified via Galaxy Zoo, is the largest that this method has been applied to. We find lower pattern speeds for strongly barred galaxies than for weakly barred galaxies. As simulations show that the pattern speed decreases as the bar exchanges angular momentum with its host, these results suggest that strong bars are more evolved than weak bars. Interestingly, the corotation radius is not different between weakly and strongly barred galaxies, despite being proportional to bar length. We also find that the corotation radius is significantly different between quenching and star forming galaxies. Additionally, we find that strongly barred galaxies have significantly lower values for R, the ratio between the corotation radius and the bar radius, than weakly barred galaxies, despite a big overlap in both distributions. This ratio classifies bars into ultrafast bars (R < 1.0; 11% of our sample), fast bars (1.0 < R < 1.4; 27%) and slow bars (R > 1.4; 62%). Simulations show that R is correlated with the bar formation mechanism, so our results suggest that strong bars are more likely to be formed by different mechanisms than weak bars. Finally, we find a lower fraction of ultrafast bars than most other studies, which decreases the recently claimed tension with {\Lambda}CDM. However, the median value of R is still lower than what is predicted by simulations.Comment: 20 pages, 16 figure

From fat droplets to floating forests: cross-domain transfer learning using a PatchGAN-based segmentation model

Many scientific domains gather sufficient labels to train machine algorithms through human-in-the-loop techniques provided by the Zooniverse.org citizen science platform. As the range of projects, task types and data rates increase, acceleration of model training is of paramount concern to focus volunteer effort where most needed. The application of Transfer Learning (TL) between Zooniverse projects holds promise as a solution. However, understanding the effectiveness of TL approaches that pretrain on large-scale generic image sets vs. images with similar characteristics possibly from similar tasks is an open challenge. We apply a generative segmentation model on two Zooniverse project-based data sets: (1) to identify fat droplets in liver cells (FatChecker; FC) and (2) the identification of kelp beds in satellite images (Floating Forests; FF) through transfer learning from the first project. We compare and contrast its performance with a TL model based on the COCO image set, and subsequently with baseline counterparts. We find that both the FC and COCO TL models perform better than the baseline cases when using >75% of the original training sample size. The COCO-based TL model generally performs better than the FC-based one, likely due to its generalized features. Our investigations provide important insights into usage of TL approaches on multi-domain data hosted across different Zooniverse projects, enabling future projects to accelerate task completion.Comment: 5 pages, 4 figures, accepted for publication at the Proceedings of the ACM/CIKM 2022 (Human-in-the-loop Data Curation Workshop

Distinguishing Mergers and Disks in High Redshift Observations of Galaxy Kinematics

The majority of massive star-forming galaxies at $z\sim2$ have velocity gradients suggestive of rotation, in addition to large amounts of disordered motions. In this paper, we demonstrate that it is challenging to distinguish the regular rotation of a disk galaxy from the orbital motions of merging galaxies with seeing-limited data. However, the merger fractions at $z\sim2$ are likely too low for this to have a large effect on measurements of disk fractions. To determine how often mergers pass for disks, we look to galaxy formation simulations. We analyze $\sim$24000 synthetic images and kinematic maps of 31 high-resolution simulations of isolated galaxies and mergers at $z\sim2$. We determine if the synthetic observations pass criteria commonly used to identify disk galaxies, and whether the results are consistent with their intrinsic dynamical states. Galaxies that are intrinsically mergers pass the disk criteria for anywhere from 0 to 100$\%$ of sightlines. The exact percentage depends strongly on the specific disk criteria adopted, and weakly on the separation of the merging galaxies. Therefore, one cannot tell with certainty whether observations of an individual galaxy indicate a merger or a disk. To estimate the fraction of mergers passing as disks in current kinematics samples, we combine the probability that a merger will pass as a disk with theoretical merger fractions from a cosmological simulation. Taking the latter at face-value, the observed disk fractions are overestimated by small amounts: at most by $5\%$ at high stellar mass ($10^{10-11}$ M$_{\odot}$) and $15\%$ at low stellar mass ($10^{9-10}$ M$_{\odot}$).Comment: 15 pages, 10 figures, accepted for publication in Ap

Practical galaxy morphology tools from deep supervised representation learning

Astronomers have typically set out to solve supervised machine learning problems by creating their own representations from scratch. We show that deep learning models trained to answer every Galaxy Zoo DECaLS question learn meaningful semantic representations of galaxies that are useful for new tasks on which the models were never trained. We exploit these representations to outperform several recent approaches at practical tasks crucial for investigating large galaxy samples. The first task is identifying galaxies of similar morphology to a query galaxy. Given a single galaxy assigned a free text tag by humans (e.g. ‘#diffuse’), we can find galaxies matching that tag for most tags. The second task is identifying the most interesting anomalies to a particular researcher. Our approach is 100 per cent accurate at identifying the most interesting 100 anomalies (as judged by Galaxy Zoo 2 volunteers). The third task is adapting a model to solve a new task using only a small number of newly-labelled galaxies. Models fine-tuned from our representation are better able to identify ring galaxies than models fine-tuned from terrestrial images (ImageNet) or trained from scratch. We solve each task with very few new labels; either one (for the similarity search) or several hundred (for anomaly detection or fine-tuning). This challenges the longstanding view that deep supervised methods require new large labelled datasets for practical use in astronomy. To help the community benefit from our pretrained models, we release our fine-tuning code zoobot. Zoobot is accessible to researchers with no prior experience in deep learning

Optimized Photometric Redshifts for the Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey (CANDELS)

We present the first comprehensive release of photometric redshifts (photo-z's) from the Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey (CANDELS) team. We use statistics based upon the Quantile-Quantile (Q--Q) plot to identify biases and signatures of underestimated or overestimated errors in photo-z probability density functions (PDFs) produced by six groups in the collaboration; correcting for these effects makes the resulting PDFs better match the statistical definition of a PDF. After correcting each group's PDF, we explore three methods of combining the different groups' PDFs for a given object into a consensus curve. Two of these methods are based on identifying the minimum f-divergence curve, i.e., the PDF that is closest in aggregate to the other PDFs in a set (analogous to the median of an array of numbers). We demonstrate that these techniques yield improved results using sets of spectroscopic redshifts independent of those used to optimize PDF modifications. The best photo-z PDFs and point estimates are achieved with the minimum f-divergence using the best 4 PDFs for each object (mFDa4) and the Hierarchical Bayesian (HB4) methods, respectively. The HB4 photo-z point estimates produced $\sigma_{\rm NMAD} = 0.0227/0.0189$ and $|\Delta z/(1+z)| > 0.15$ outlier fraction = 0.067/0.019 for spectroscopic and 3D-HST redshifts, respectively. Finally, we describe the structure and provide guidance for the use of the CANDELS photo-z catalogs, which are available at https://archive.stsci.edu/hlsp/candels.Comment: 35 pages, 19 figures, submitted to ApJ, data available at https://archive.stsci.edu/hlsp/candel

Investigating the Effect of Galaxy Interactions on Star Formation at 0.5<z<3.0

Observations and simulations of interacting galaxies and mergers in the local universe have shown that interactions can significantly enhance the star formation rates (SFR) and fueling of Active Galactic Nuclei (AGN). However, at higher redshift, some simulations suggest that the level of star formation enhancement induced by interactions is lower due to the higher gas fractions and already increased SFRs in these galaxies. To test this, we measure the SFR enhancement in a total of 2351 (1327) massive ($M_*>10^{10}M_\odot$) major ($1<M_1/M_2<4$) spectroscopic galaxy pairs at 0.5<z<3.0 with $\Delta V <5000$ km s$^{-1}$ (1000 km s$^{-1}$) and projected separation <150 kpc selected from the extensive spectroscopic coverage in the COSMOS and CANDELS fields. We find that the highest level of SFR enhancement is a factor of 1.23$^{+0.08}_{-0.09}$ in the closest projected separation bin (<25 kpc) relative to a stellar mass-, redshift-, and environment-matched control sample of isolated galaxies. We find that the level of SFR enhancement is a factor of $\sim1.5$ higher at 0.5<z<1 than at 1<z<3 in the closest projected separation bin. Among a sample of visually identified mergers, we find an enhancement of a factor of 1.86$^{+0.29}_{-0.18}$ for coalesced systems. For this visually identified sample, we see a clear trend of increased SFR enhancement with decreasing projected separation (2.40$^{+0.62}_{-0.37}$ vs.\ 1.58$^{+0.29}_{-0.20}$ for 0.5<z<1.6 and 1.6<z<3.0, respectively). The SFR enhancement seen in our interactions and mergers are all lower than the level seen in local samples at the same separation, suggesting that the level of interaction-induced star formation evolves significantly over this time period.Comment: 23 pages, 13 figures, Accepted for publication in Ap

Major merging history in CANDELS. I. Evolution of the incidence of massive galaxy–galaxy pairs from z = 3 to z ∼ 0

The rate of major galaxy–galaxy merging is theoretically predicted to steadily increase with redshift during the peak epoch of massive galaxy development (1 ≤ z ≤ 3). We use close-pair statistics to objectively study the incidence of massive galaxies (stellar M1 > 2 × 1010 M⊙) hosting major companions (1 ≤ M1/M2 ≤ 4; i.e. 4:1) companions at z > 1. We show that these evolutionary trends are statistically robust to changes in companion proximity. We find disagreements between published results are resolved when selection criteria are closely matched. If we compute merger rates using constant fraction-to-rate conversion factors (Cmerg,pair = 0.6 and Tobs,pair = 0.65 Gyr), we find that MR rates disagree with theoretical predictions at z > 1.5. Instead, if we use an evolving Tobs,pair(z) ∝ (1 + z)−2 from Snyder et al., our MR-based rates agree with theory at 0 < z < 3. Our analysis underscores the need for detailed calibration of Cmerg,pair and Tobs,pair as a function of redshift, mass, and companion selection criteria to better constrain the empirical major merger history

The Lyman Continuum Escape Fraction of Star-forming Galaxies at 2.4≲z≲3.72.4\lesssim z\lesssim3.7 from UVCANDELS

The UltraViolet Imaging of the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey Fields (UVCANDELS) survey is a Hubble Space Telescope (HST) Cycle-26 Treasury Program, allocated in total 164 orbits of primary Wide-Field Camera 3 Ultraviolet and Visible light F275W imaging with coordinated parallel Advanced Camera for Surveys F435W imaging, on four of the five premier extragalactic survey fields: GOODS-N, GOODS-S, EGS, and COSMOS. We introduce this survey by presenting a thorough search for galaxies at $z\gtrsim2.4$ that leak significant Lyman continuum (LyC) radiation, as well as a stringent constraint on the LyC escape fraction ($f_{\rm esc}$) from stacking the UV images of a population of star-forming galaxies with secure redshifts. Our extensive search for LyC emission and stacking analysis benefit from the catalogs of high-quality spectroscopic redshifts compiled from archival ground-based data and HST slitless spectroscopy, carefully vetted by dedicated visual inspection efforts. We report a sample of five galaxies as individual LyC leaker candidates, showing $f_{\rm esc}^{\rm rel}\gtrsim60\%$ estimated using detailed Monte Carlo analysis of intergalactic medium attenuation. We develop a robust stacking method to apply to five samples of in total 85 non-detection galaxies in the redshift range of $z\in[2.4,3.7]$. Most stacks give tight 2-$\sigma$ upper limits below $f_{\rm esc}^{\rm rel}<6\%$. A stack for a subset of 32 emission-line galaxies shows tentative LyC leakage detected at 2.9-$\sigma$, indicating $f_{\rm esc}^{\rm rel}=5.7\%$ at $z\sim2.65$, supporting the key role of such galaxies in contributing to the cosmic reionization and maintaining the UV ionization background. These new F275W and F435W imaging mosaics from UVCANDELS have been made publicly available on the Barbara A. Mikulski Archive for Space Telescopes.Comment: 33 pages, 21 figures, and 5 tables. Resubmitted after addressing the referee repor