2,989 research outputs found
Position-Agnostic Autonomous Navigation in Vineyards with Deep Reinforcement Learning
No full textPrecision agriculture is rapidly attracting research to efficiently introduce
automation and robotics solutions to support agricultural activities. Robotic
navigation in vineyards and orchards offers competitive advantages in
autonomously monitoring and easily accessing crops for harvesting, spraying and
performing time-consuming necessary tasks. Nowadays, autonomous navigation
algorithms exploit expensive sensors which also require heavy computational
cost for data processing. Nonetheless, vineyard rows represent a challenging
outdoor scenario where GPS and Visual Odometry techniques often struggle to
provide reliable positioning information. In this work, we combine Edge AI with
Deep Reinforcement Learning to propose a cutting-edge lightweight solution to
tackle the problem of autonomous vineyard navigation without exploiting precise
localization data and overcoming task-tailored algorithms with a flexible
learning-based approach. We train an end-to-end sensorimotor agent which
directly maps noisy depth images and position-agnostic robot state information
to velocity commands and guides the robot to the end of a row, continuously
adjusting its heading for a collision-free central trajectory. Our extensive
experimentation in realistic simulated vineyards demonstrates the effectiveness
of our solution and the generalization capabilities of our agent
Waypoint Generation in Row-Based Crops with Deep Learning and Contrastive Clustering
No full textThe development of precision agriculture has gradually introduced automation in the agricultural process to support and rationalize all the activities related to field management. In particular, service robotics plays a predominant role in this evolution by deploying autonomous agents able to navigate in fields while executing different tasks without the need for human intervention, such as monitoring, spraying and harvesting. In this context, global path planning is the first necessary step for every robotic mission and ensures that the navigation is performed efficiently and with complete field coverage. In this paper, we propose a learning-based approach to tackle waypoint generation for planning a navigation path for row-based crops, starting from a top-view map of the region-of-interest. We present a novel methodology for waypoint clustering based on a contrastive loss, able to project the points to a separable latent space. The proposed deep neural network can simultaneously predict the waypoint position and cluster assignment with two specialized heads in a single forward pass. The extensive experimentation on simulated and real-world images demonstrates that the proposed approach effectively solves the waypoint generation problem for both straight and curved row-based crops, overcoming the limitations of previous state-of-the-art methodologies
Rozpor ako východisko, láska ako smer u Simone Weilovej (Contradiction as base, Love as direction in writings of Simone Weil)
No full textArticle is explaining contradiction and love, Simone Weil‘s essential terms of hermeneutics of human Being. It introduces close relation of these terms with her understanding of God as well as with her overall concept of religion. Author also mentions Simone Weil‘s inspirations with philosophical and spiritual concepts of the East
Deep Semantic Segmentation at the Edge for Autonomous Navigation in Vineyard Rows
No full textPrecision agriculture is a fast-growing field that aims at introducing affordable and effective automation into agricultural processes. Nowadays, algorithmic solutions for navigation in vineyards require expensive sensors and high computational workloads that preclude large-scale applicability of autonomous robotic platforms in real business case scenarios. From this perspective, our novel proposed control leverages the latest advancement in machine perception and edge AI techniques to achieve highly affordable and reliable navigation inside vineyard rows with low computational and power consumption. Indeed, using a custom-trained segmentation network and a low-range RGB-D camera, we are able to take advantage of the semantic information of the environment to produce smooth trajectories and stable control in different vineyards scenarios. Moreover, the segmentation maps generated by the control algorithm itself could be directly exploited as filters for a vegetative assessment of the crop status. Extensive experimentations and evaluations against real-world data and simulated environments demonstrated the effectiveness and intrinsic robustness of our methodology
“I beg you to tell me what has become of Djamila”: The Political Mobilization of Simone de Beauvoir’s Readers During the Boupacha Affair
No full textBy Sophia Millman This is a condensed version of a Masters thesis dedicated to the political mobilization of Simone de Beauvoir’s readers. The citations from the letters were translated from French by the author. *** On June 2, 1960, the French government ordered all copies of the daily Algiers edition of Le Monde seized and destroyed to suppress the publication of Simone de Beauvoir’s article “Pour Djamila Boupacha.” Beauvoir, a self-professed “woman of letters”, not “of action[1]”, and one ..
An Adaptive Row Crops Path Generator with Deep Learning Synergy
No full textThe autonomous navigation of agricultural field machines strongly depends on the global path generation system. Indeed, a correct and effective path construction heavily influences the overall navigation stack compromising the successfulness of the robot mission. However, the most commonly used search algorithms struggle to adapt to environments where a significant prior knowledge of the domain is not negligible. Despite this crucial factor, path generation for row-based crops has received little attention from the research community so far. The proposed research introduces a novel global path planning system that works in synergy with a deep learning model to provide an accurate and centered path with respect to the rows of the analyzed crop. It guarantees the full coverage of the given occupancy grid with less processing time compared to other available literature solutions. Moreover, the presented methodology can detect an anomaly in the path generation and provide the hypothetical user feedback of the missing full coverage of the given crop. Indeed, especially in a practical application, the correct coverage and centrality of the path are essential for effective autonomous navigation. Experimentation with synthetic and real-world satellite occupancy grid maps clearly show the advantages of the proposed methodology and its intrinsic robustness
A comparative study of form and theology in the works of Flannery O'Connor and Simone Weil
No full textIn this comparative study of the form and theology of Flannery O'Connor and Simone Weil I interrogate how Weil's philosophical writings and her theology illuminate O'Connor's use of both narrative and non-fictional forms, and her Catholicism. The Introduction analyses how Weil's concept of superposed reading provides a new method of approaching both O'Connor, her writings, and O'Connor
studies, and focuses on how such apparently different women interconnect. Chapter One explores how both Weil and O'Connor attempt to write their theologies on the
souls of their readers yet are each subject to constraints imposed by form. Weil's concept of locating equilibrium between incommensurates is discussed, and her
distinctively philosophical approach to fictions and fictionality is used to investigate O'Connor's notion of prophetic fictions and the writer's role. Chapter Two assesses how both writers revivify Christian paradoxes. Weil's monstrous concept of affiiction, and O'Connor's use of the grotesque genre to jolt secular man into an
awareness of the sacred are scrutinised. Chapter Three studies how both writers consider an encounter between God and man is possible through the action of grace. My Conclusion interrogates how Weil's work can deepen our understanding of O'Connor's writings, and examines how successful O'Connor is at realising a truly
Christian literature. I conclude that despite being a writer of powerful fictions, O'Connor can not be totally successful in her mission as writer-prophet because
ultimately fiction escapes orthodoxy
A Deep Learning Driven Algorithmic Pipeline for Autonomous Navigation in Row-Based Crops
No full textExpensive sensors and inefficient algorithmic pipelines significantly affect the overall cost of autonomous machines. However, affordable robotic solutions are essential to practical usage, and their financial impact constitutes a fundamental requirement for employing service robotics in most fields of application. Among all, researchers in the precision agriculture domain strive to devise robust and cost-effective autonomous platforms in order to provide genuinely large-scale competitive solutions. In this article, we present a complete algorithmic pipeline for row-based crops autonomous navigation, specifically designed to cope with low-range sensors and seasonal variations. Firstly, we build on a robust data-driven methodology to generate a viable path for the autonomous machine, covering the full extension of the crop with only the occupancy grid map information of the field. Moreover, our solution leverages on latest advancement of deep learning optimization techniques and synthetic generation of data to provide an affordable solution that efficiently tackles the well-known Global Navigation Satellite System unreliability and degradation due to vegetation growing inside rows. Extensive experimentation and simulations against computer-generated environments and real-world crops demonstrated the robustness and intrinsic generalizability to different factors of variations of our methodology that open the possibility of highly affordable and fully autonomous machines
PIC4rl-gym: a ROS2 modular framework for Robots Autonomous Navigation with Deep Reinforcement Learning
No full textLearning agents can optimize standard autonomous navigation improving
flexibility, efficiency, and computational cost of the system by adopting a
wide variety of approaches. This work introduces the \textit{PIC4rl-gym}, a
fundamental modular framework to enhance navigation and learning research by
mixing ROS2 and Gazebo, the standard tools of the robotics community, with Deep
Reinforcement Learning (DRL). The paper describes the whole structure of the
PIC4rl-gym, which fully integrates DRL agent's training and testing in several
indoor and outdoor navigation scenarios and tasks. A modular approach is
adopted to easily customize the simulation by selecting new platforms, sensors,
or models. We demonstrate the potential of our novel gym by benchmarking the
resulting policies, trained for different navigation tasks, with a complete set
of metrics
Microlinices benthovus Simone 2014
No full textbenthovus, Microlinices Simone, 2014 Microlinices benthovus Simone, 2014: 575–578 (figs 6A–J, 7A–H, 11A–C). Gastropoda, Naticidae Paratypes (22 spc): MZSP 105269. Paratypes 1 (15 spc): MZSP 105270. Paratypes 2 (16 spc): MZSP 105271. Paratypes 3 (7 spc): MZSP 105272. Localities: Brazil, Espírito Santo, off Itaúnas, Abrolhos Slope, 18°59' S, 37°50' W, MD55 sta. DC 73, 637 m depth, 27 May 1987; 1) 19°00' S, 37°48' W, MD55 sta. DC72, 950– 1050 m, 27 May 1987; 2) off Regência, 19°40' S, 37°48' W, MD55 sta. CB77, 790– 940 m depth, 27 May 1987; 3) off Itaúnas, Abrolhos Slope, 19°01' S, 37°47' W, MD55 sta. CB79, 1500–1575 m depth, 28 May 1987. Collectors: P. Bouchet, J.H. Leal and B. Métivier. Preservation: Dry. Remarks: Former MNHN, Paris. The catalogue number MZSP 105250 is mentioned twice in Simone’s (2014) paper, among the paratypes of M. ibitingus Simone, 2014 and M. benthovus. This duplicity was a mistake by the author: the latter is an erroneous designation and should be disregarded. The only valid paratype lots for M. benthovus are the ones shown above.Published as part of Cavallari, Daniel C., Dornellas, Ana Paula S. & Simone, Luiz Ricardo L., 2016, Second annotated list of type specimens of molluscs deposited in the Museu de Zoologia da Universidade de São Paulo, Brazil, pp. 1-59 in European Journal of Taxonomy 213 on page 10, DOI: 10.5852/ejt.2016.213, http://zenodo.org/record/384012
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