15,506 research outputs found
Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control
This paper provides an overview of the current state-of-the-art in selective
harvesting robots (SHRs) and their potential for addressing the challenges of
global food production. SHRs have the potential to increase productivity,
reduce labour costs, and minimise food waste by selectively harvesting only
ripe fruits and vegetables. The paper discusses the main components of SHRs,
including perception, grasping, cutting, motion planning, and control. It also
highlights the challenges in developing SHR technologies, particularly in the
areas of robot design, motion planning and control. The paper also discusses
the potential benefits of integrating AI and soft robots and data-driven
methods to enhance the performance and robustness of SHR systems. Finally, the
paper identifies several open research questions in the field and highlights
the need for further research and development efforts to advance SHR
technologies to meet the challenges of global food production. Overall, this
paper provides a starting point for researchers and practitioners interested in
developing SHRs and highlights the need for more research in this field.Comment: Preprint: to be appeared in Journal of Field Robotic
Search for heavy Majorana or Dirac neutrinos and right-handed gauge bosons in final states with charged leptons and jets in collisions at TeV with the ATLAS detector
A search for heavy right-handed Majorana or Dirac neutrinos
and heavy right-handed gauge bosons is performed in events
with energetic electrons or muons, with the same or opposite electric charge,
and energetic jets. The search is carried out separately for topologies of
clearly separated final-state products (``resolved'' channel) and topologies
with boosted final states with hadronic products partially overlapping and
reconstructed as a large-radius jet (``boosted'' channel). The events are
selected from collision data at the LHC with an integrated luminosity of
139 fb collected by the ATLAS detector at = 13 TeV. No
significant deviations from the Standard Model predictions are observed. The
results are interpreted within the theoretical framework of a left-right
symmetric model, and lower limits are set on masses in the heavy right-handed
boson and plane. The excluded region extends
to about TeV for both Majorana and Dirac
neutrinos at TeV. with
masses of less than 3.5 (3.6) TeV are excluded in the electron (muon) channel
at TeV for the Majorana neutrinos, and limits of
up to 3.6 TeV for () TeV in
the electron (muon) channel are set for the Dirac neutrinos.Comment: 48 pages in total, author list starting page 31, 9 figures, 5 tables,
submitted to EPJC. All figures including auxiliary figures are available at
https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/EXOT-2019-39
Vision- and tactile-based continuous multimodal intention and attention recognition for safer physical human-robot interaction
Employing skin-like tactile sensors on robots enhances both the safety and
usability of collaborative robots by adding the capability to detect human
contact. Unfortunately, simple binary tactile sensors alone cannot determine
the context of the human contact -- whether it is a deliberate interaction or
an unintended collision that requires safety manoeuvres. Many published methods
classify discrete interactions using more advanced tactile sensors or by
analysing joint torques. Instead, we propose to augment the intention
recognition capabilities of simple binary tactile sensors by adding a
robot-mounted camera for human posture analysis. Different interaction
characteristics, including touch location, human pose, and gaze direction, are
used to train a supervised machine learning algorithm to classify whether a
touch is intentional or not with an F1-score of 86%. We demonstrate that
multimodal intention recognition is significantly more accurate than monomodal
analyses with the collaborative robot Baxter. Furthermore, our method can also
continuously monitor interactions that fluidly change between intentional or
unintentional by gauging the user's attention through gaze. If a user stops
paying attention mid-task, the proposed intention and attention recognition
algorithm can activate safety features to prevent unsafe interactions. We also
employ a feature reduction technique that reduces the number of inputs to five
to achieve a more generalized low-dimensional classifier. This simplification
both reduces the amount of training data required and improves real-world
classification accuracy. It also renders the method potentially agnostic to the
robot and touch sensor architectures while achieving a high degree of task
adaptability.Comment: 11 pages, 8 figures, preprint under revie
Joint Activity Detection, Channel Estimation, and Data Decoding for Grant-free Massive Random Access
In the massive machine-type communication (mMTC) scenario, a large number of
devices with sporadic traffic need to access the network on limited radio
resources. While grant-free random access has emerged as a promising mechanism
for massive access, its potential has not been fully unleashed. In particular,
the common sparsity pattern in the received pilot and data signal has been
ignored in most existing studies, and auxiliary information of channel decoding
has not been utilized for user activity detection. This paper endeavors to
develop advanced receivers in a holistic manner for joint activity detection,
channel estimation, and data decoding. In particular, a turbo receiver based on
the bilinear generalized approximate message passing (BiG-AMP) algorithm is
developed. In this receiver, all the received symbols will be utilized to
jointly estimate the channel state, user activity, and soft data symbols, which
effectively exploits the common sparsity pattern. Meanwhile, the extrinsic
information from the channel decoder will assist the joint channel estimation
and data detection. To reduce the complexity, a low-cost side information-aided
receiver is also proposed, where the channel decoder provides side information
to update the estimates on whether a user is active or not. Simulation results
show that the turbo receiver is able to reduce the activity detection, channel
estimation, and data decoding errors effectively, while the side
information-aided receiver notably outperforms the conventional method with a
relatively low complexity
Reinforcement Learning from Passive Data via Latent Intentions
Passive observational data, such as human videos, is abundant and rich in
information, yet remains largely untapped by current RL methods. Perhaps
surprisingly, we show that passive data, despite not having reward or action
labels, can still be used to learn features that accelerate downstream RL. Our
approach learns from passive data by modeling intentions: measuring how the
likelihood of future outcomes change when the agent acts to achieve a
particular task. We propose a temporal difference learning objective to learn
about intentions, resulting in an algorithm similar to conventional RL, but
which learns entirely from passive data. When optimizing this objective, our
agent simultaneously learns representations of states, of policies, and of
possible outcomes in an environment, all from raw observational data. Both
theoretically and empirically, this scheme learns features amenable for value
prediction for downstream tasks, and our experiments demonstrate the ability to
learn from many forms of passive data, including cross-embodiment video data
and YouTube videos.Comment: Accompanying website at https://dibyaghosh.com/icvf
Chandra X-ray Measurement of Gas-phase Heavy Element Abundances in the Central Parsec of the Galaxy
Elemental abundances are key to our understanding of star formation and
evolution in the Galactic center. Previous work on this topic has been based on
infrared (IR) observations, but X-ray observations have the potential of
constraining the abundance of heavy elements, mainly through their K-shell
emission lines. Using 5.7 Ms Chandra observations, we provide the first
abundance measurement of Si, S, Ar, Ca and Fe, in four prominent diffuse X-ray
features located in the central parsec of the Galaxy, which are the
manifestation of shock-heated hot gas. A two-temperature, non-equilibrium
ionization spectral model is employed to derive the abundances of these five
elements. In this procedure, a degeneracy is introduced due to uncertainties in
the composition of light elements, in particular, H, C and N. Assuming that the
hot gas is H-depleted but C- and N-enriched, as would be expected for a
standard scenario in which the hot gas is dominated by Wolf-Rayet star winds,
the spectral fit finds a generally subsolar abundance for the heavy elements.
If, instead, the light elements had a solar-like abundance, the heavy elements
have a fitted abundance of 1--2 solar. The /Fe abundance ratio,
on the other hand, is mostly supersolar and insensitive to the exact
composition of the light elements. These results are robust against potential
biases due to either a moderate spectral S/N or the presence of non-thermal
components. Implications of the measured abundances for the Galactic center
environment are addressed.Comment: 13 pages, 6 figures, Accepted for publication in MNRA
Exploiting Symmetry and Heuristic Demonstrations in Off-policy Reinforcement Learning for Robotic Manipulation
Reinforcement learning demonstrates significant potential in automatically
building control policies in numerous domains, but shows low efficiency when
applied to robot manipulation tasks due to the curse of dimensionality. To
facilitate the learning of such tasks, prior knowledge or heuristics that
incorporate inherent simplification can effectively improve the learning
performance. This paper aims to define and incorporate the natural symmetry
present in physical robotic environments. Then, sample-efficient policies are
trained by exploiting the expert demonstrations in symmetrical environments
through an amalgamation of reinforcement and behavior cloning, which gives the
off-policy learning process a diverse yet compact initiation. Furthermore, it
presents a rigorous framework for a recent concept and explores its scope for
robot manipulation tasks. The proposed method is validated via two
point-to-point reaching tasks of an industrial arm, with and without an
obstacle, in a simulation experiment study. A PID controller, which tracks the
linear joint-space trajectories with hard-coded temporal logic to produce
interim midpoints, is used to generate demonstrations in the study. The results
of the study present the effect of the number of demonstrations and quantify
the magnitude of behavior cloning to exemplify the possible improvement of
model-free reinforcement learning in common manipulation tasks. A comparison
study between the proposed method and a traditional off-policy reinforcement
learning algorithm indicates its advantage in learning performance and
potential value for applications
Pollution-induced community tolerance in freshwater biofilms – from molecular mechanisms to loss of community functions
Exposure to herbicides poses a threat to aquatic biofilms by affecting their community structure, physiology and function. These changes render biofilms to become more tolerant, but on the downside community tolerance has ecologic costs. A concept that addresses induced community tolerance to a pollutant (PICT) was introduced by Blanck and Wängberg (1988). The basic principle of the concept is that microbial communities undergo pollution-induced succession when exposed to a pollutant over a long period of time, which changes communities structurally and functionally and enhancing tolerance to the pollutant exposure. However, the mechanisms of tolerance and the ecologic consequences were hardly studied up to date. This thesis addresses the structural and functional changes in biofilm communities and applies modern molecular methods to unravel molecular tolerance mechanisms.
Two different freshwater biofilm communities were cultivated for a period of five weeks, with one of the communities being contaminated with 4 μg L-1 diuron. Subsequently, the communities were characterized for structural and functional differences, especially focusing on their crucial role of photosynthesis. The community structure of the autotrophs was assessed using HPLC-based pigment analysis and their functional alterations were investigated using Imaging-PAM fluorometry to study photosynthesis and community oxygen profiling to determine net primary production. Then, the molecular fingerprints of the communities were measured with meta-transcriptomics (RNA-Seq) and GC-based community metabolomics approaches and analyzed with respect to changes in their molecular functions. The communities were acute exposed to diuron for one hour in a dose-response design, to reveal a potential PICT and uncover related adaptation to diuron exposure. The combination of apical and molecular methods in a dose-response design enabled the linkage of functional effects of diuron exposure and underlying molecular mechanisms based on a sensitivity analysis.
Chronic exposure to diuron impaired freshwater biofilms in their biomass accrual. The contaminated communities particularly lost autotrophic biomass, reflected by the decrease in specific chlorophyll a content. This loss was associated with a change in the molecular fingerprint of the communities, which substantiates structural and physiological changes. The decline in autotrophic biomass could be due to a primary loss of sensitive autotrophic organisms caused by the selection of better adapted species in the course of chronic exposure. Related to this hypothesis, an increase in diuron tolerance has been detected in the contaminated communities and molecular mechanisms facilitating tolerance have been found. It was shown that genes of the photosystem, reductive-pentose phosphate cycle and arginine metabolism were differentially expressed among the communities and that an increased amount of potential antioxidant degradation products was found in the contaminated communities. This led to the hypothesis that contaminated communities may have adapted to oxidative stress, making them less sensitive to diuron exposure. Moreover, the photosynthetic light harvesting complex was altered and the photoprotective xanthophyll cycle was increased in the contaminated communities. Despite these adaptation strategies, the loss of autotrophic biomass has been shown to impair primary production. This impairment persisted even under repeated short-term exposure, so that the tolerance mechanisms cannot safeguard primary production as a key function in aquatic systems.:1. The effect of chemicals on organisms and their functions .............................. 1
1.1 Welcome to the anthropocene .......................................................................... 1
1.2 From cellular stress responses to ecosystem resilience ................................... 3
1.2.1 The individual pursuit for homeostasis ....................................................... 3
1.2.2 Stability from diversity ................................................................................. 5
1.3 Community ecotoxicology - a step forward in monitoring the effects of chemical
pollution? ................................................................................................................. 6
1.4 Functional ecotoxicological assessment of microbial communities ................... 9
1.5 Molecular tools – the key to a mechanistic understanding of stressor effects
from a functional perspective in microbial communities? ...................................... 12
2. Aims and Hypothesis ......................................................................................... 14
2.1 Research question .......................................................................................... 14
2.2 Hypothesis and outline .................................................................................... 15
2.3 Experimental approach & concept .................................................................. 16
2.3.1 Aquatic freshwater biofilms as model community ..................................... 16
2.3.2 Diuron as model herbicide ........................................................................ 17
2.3.3 Experimental design ................................................................................. 18
3. Structural and physiological changes in microbial communities after chronic
exposure - PICT and altered functional capacity ................................................. 21
3.1 Introduction ..................................................................................................... 21
3.2 Methods .......................................................................................................... 23
3.2.1 Biofilm cultivation ...................................................................................... 23
3.2.2 Dry weight and autotrophic index ............................................................. 23
3.2.4 Pigment analysis of periphyton ................................................................. 23
3.2.4.1 In-vivo pigment analysis for community characterization ....................... 24
3.2.4.2 In-vivo pigment analysis based on Imaging-PAM fluorometry ............... 24
3.2.4.3 In-vivo pigment fluorescence for tolerance detection ............................. 26
3.2.4.4 Ex-vivo pigment analysis by high-pressure liquid-chromatography ....... 27
3.2.5 Community oxygen metabolism measurements ....................................... 28
3.3 Results and discussion ................................................................................... 29
3.3.1 Comparison of the structural community parameters ............................... 29
3.3.2 Photosynthetic activity and primary production of the communities after
selection phase ................................................................................................. 33
3.3.3 Acquisition of photosynthetic tolerance .................................................... 34
3.3.4 Primary production at exposure conditions ............................................... 36
3.3.5 Tolerance detection in primary production ................................................ 37
3.4 Summary and Conclusion ........................................................................... 40
4. Community gene expression analysis by meta-transcriptomics ................... 41
4.1 Introduction to meta-transcriptomics ............................................................... 41
4.2. Methods ......................................................................................................... 43
4.2.1 Sampling and RNA extraction................................................................... 43
4.2.2 RNA sequencing analysis ......................................................................... 44
4.2.3 Data assembly and processing................................................................. 45
4.2.4 Prioritization of contigs and annotation ..................................................... 47
4.2.5 Sensitivity analysis of biological processes .............................................. 48
4.3 Results and discussion ................................................................................... 48
4.3.1 Characterization of the meta-transcriptomic fingerprints .......................... 49
4.3.2 Insights into community stress response mechanisms using trend analysis
(DRomic’s) ......................................................................................................... 51
4.3.3 Response pattern in the isoform PS genes .............................................. 63
4.5 Summary and conclusion ................................................................................ 65
5. Community metabolome analysis ..................................................................... 66
5.1 Introduction to community metabolomics ........................................................ 66
5.2 Methods .......................................................................................................... 68
5.2.1 Sampling, metabolite extraction and derivatisation................................... 68
5.2.2 GC-TOF-MS analysis ............................................................................... 69
5.2.3 Data processing and statistical analysis ................................................... 69
5.3 Results and discussion ................................................................................... 70
5.3.1 Characterization of the metabolic fingerprints .......................................... 70
5.3.2 Difference in the metabolic fingerprints .................................................... 71
5.3.3 Differential metabolic responses of the communities to short-term exposure
of diuron ............................................................................................................ 73
5.4 Summary and conclusion ................................................................................ 78
6. Synthesis ............................................................................................................. 79
6.1 Approaches and challenges for linking molecular data to functional
measurements ...................................................................................................... 79
6.2 Methods .......................................................................................................... 83
6.2.1 Summary on the data ............................................................................... 83
6.2.2 Aggregation of molecular data to index values (TELI and MELI) .............. 83
6.2.3 Functional annotation of contigs and metabolites using KEGG ................ 83
6.3 Results and discussion ................................................................................... 85
6.3.1 Results of aggregation techniques ........................................................... 85
6.3.2 Sensitivity analysis of the different molecular approaches and endpoints 86
6.3.3 Mechanistic view of the molecular stress responses based on KEGG
functions ............................................................................................................ 89
6.4 Consolidation of the results – holistic interpretation and discussion ............... 93
6.4.1 Adaptation to chronic diuron exposure - from molecular changes to
community effects.............................................................................................. 93
6.4.2 Assessment of the ecological costs of Pollution-induced community
tolerance based on primary production ............................................................. 94
6.5 Outlook ............................................................................................................ 9
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