15,819 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
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Continual Learning of Hand Gestures for Human-Robot Interaction
In this paper, we present an efficient method to incrementally learn to
classify static hand gestures. This method allows users to teach a robot to
recognize new symbols in an incremental manner. Contrary to other works which
use special sensors or external devices such as color or data gloves, our
proposed approach makes use of a single RGB camera to perform static hand
gesture recognition from 2D images. Furthermore, our system is able to
incrementally learn up to 38 new symbols using only 5 samples for each old
class, achieving a final average accuracy of over 90\%. In addition to that,
the incremental training time can be reduced to a 10\% of the time required
when using all data available
Grand challenges in entomology: Priorities for action in the coming decades
Entomology is key to understanding terrestrial and freshwater ecosystems at a time of unprecedented anthropogenic environmental change and offers substantial untapped potential to benefit humanity in a variety of ways, from improving agricultural practices to managing vector-borne diseases and inspiring technological advances. We identified high priority challenges for entomology using an inclusive, open, and democratic four-stage prioritisation approach, conducted among the membership and affiliates (hereafter ‘members’) of the UK-based Royal Entomological Society (RES). A list of 710 challenges was gathered from 189 RES members. Thematic analysis was used to group suggestions, followed by an online vote to determine initial priorities, which were subsequently ranked during an online workshop involving 37 participants. The outcome was a set of 61 priority challenges within four groupings of related themes: (i) ‘Fundamental Research’ (themes: Taxonomy, ‘Blue Skies’ [defined as research ideas without immediate practical application], Methods and Techniques); (ii) ‘Anthropogenic Impacts and Conservation’ (themes: Anthropogenic Impacts, Conservation Options); (iii) ‘Uses, Ecosystem Services and Disservices’ (themes: Ecosystem Benefits, Technology and Resources [use of insects as a resource, or as inspiration], Pests); (iv) ‘Collaboration, Engagement and Training’ (themes: Knowledge Access, Training and Collaboration, Societal Engagement). Priority challenges encompass research questions, funding objectives, new technologies, and priorities for outreach and engagement. Examples include training taxonomists, establishing a global network of insect monitoring sites, understanding the extent of insect declines, exploring roles of cultivated insects in food supply chains, and connecting professional with amateur entomologists. Responses to different challenges could be led by amateur and professional entomologists, at all career stages. Overall, the challenges provide a diverse array of options to inspire and initiate entomological activities and reveal the potential of entomology to contribute to addressing global challenges related to human health and well-being, and environmental change
A Design Science Research Approach to Smart and Collaborative Urban Supply Networks
Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness.
A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense.
Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice
Data-driven Grip Force Variation in Robot-Human Handovers
Handovers frequently occur in our social environments, making it imperative
for a collaborative robotic system to master the skill of handover. In this
work, we aim to investigate the relationship between the grip force variation
for a human giver and the sensed interaction force-torque in human-human
handovers, utilizing a data-driven approach. A Long-Short Term Memory (LSTM)
network was trained to use the interaction force-torque in a handover to
predict the human grip force variation in advance. Further, we propose to
utilize the trained network to cause human-like grip force variation for a
robotic giver.Comment: Contributed to "Advances in Close Proximity Human-Robot
Collaboration" Workshop in 2022 IEEE-RAS International Conference on Humanoid
Robots (Humanoids 2022
Big Tech and research funding: A bibliometric approach
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsTechnology companies have radically transformed our daily life in the recent years with help of the wide usage of internet. While transforming our lives, these companies also have grown up even bigger in the recent times and have become more powerful not only financially, but also in terms of computing power and data. Although there have been lots of research done on the influence of large digital economy players (Big Tech) in different fields, the academic influence of these companies is little understood. By drawing on 130,000 academic papers for which there is evidence of support by the Big Tech, the present work applies bibliometric approaches (on the metadata) and text mining techniques (on the contents) to shed a light on the outcomes of this relationship. In particular, we take into consideration research funding (direct strategies) and conference sponsorships (indirect strategies) to empirically explore this relatively unexplored side of Big Tech’s influence in contemporary society. While developing the analysis a key limitation was the scarcity of prior work exploring the connections between digital platforms and the scientific enterprise. There are several results that come to light from such a perspective, one of these findings is that among the research supported by Big Tech companies, there is big gap between the number of outcomes with the content about the technical perspectives (like machine learning or artificial intelligence) than the content about reflexive (say ethical or environmental) dimensions of innovation, ladder being very small. These findings may stimulate further inquiries into identifying the possible risks, if any, are generated from the direct and indirect financial support by corporate informational giants to academia. The causes and consequences of this non-market activity by companies with big market power may require further attention and research in this field
A Hierarchical Game-Theoretic Decision-Making for Cooperative Multi-Agent Systems Under the Presence of Adversarial Agents
Underlying relationships among Multi-Agent Systems (MAS) in hazardous
scenarios can be represented as Game-theoretic models. This paper proposes a
new hierarchical network-based model called Game-theoretic Utility Tree (GUT),
which decomposes high-level strategies into executable low-level actions for
cooperative MAS decisions. It combines with a new payoff measure based on agent
needs for real-time strategy games. We present an Explore game domain, where we
measure the performance of MAS achieving tasks from the perspective of
balancing the success probability and system costs. We evaluate the GUT
approach against state-of-the-art methods that greedily rely on rewards of the
composite actions. Conclusive results on extensive numerical simulations
indicate that GUT can organize more complex relationships among MAS
cooperation, helping the group achieve challenging tasks with lower costs and
higher winning rates. Furthermore, we demonstrated the applicability of the GUT
using the simulator-hardware testbed - Robotarium. The performances verified
the effectiveness of the GUT in the real robot application and validated that
the GUT could effectively organize MAS cooperation strategies, helping the
group with fewer advantages achieve higher performance.Comment: This paper is accepted by the ACM Symposium on Applied Computing
(SAC) 2023 Technical Track on Intelligent Robotics and Multi-Agent Systems
(IRMAS
Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review
In this paper, a critical bibliometric analysis study is conducted, coupled
with an extensive literature survey on recent developments and associated
applications in machine learning research with a perspective on Africa. The
presented bibliometric analysis study consists of 2761 machine learning-related
documents, of which 98% were articles with at least 482 citations published in
903 journals during the past 30 years. Furthermore, the collated documents were
retrieved from the Science Citation Index EXPANDED, comprising research
publications from 54 African countries between 1993 and 2021. The bibliometric
study shows the visualization of the current landscape and future trends in
machine learning research and its application to facilitate future
collaborative research and knowledge exchange among authors from different
research institutions scattered across the African continent
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