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Gotowa bibliografia na temat „Robotics-inspired algorithms”

Autor: Grafiati

Data publikacji: 9 listopada 2024

Data aktualizacji: 22 czerwca 2025

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Artykuły w czasopismach na temat "Robotics-inspired algorithms"

Hu, Jinming. "Bridging Neuroscience and AI: A Comprehensive Investigation of Brain-Inspired Computing Models." ITM Web of Conferences 73 (2025): 03001. https://doi.org/10.1051/itmconf/20257303001.

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Artificial Intelligence (AI) has reached new heights, supported by advancements in hardware and algorithm theory. Areas like robotics and autonomous driving have made significant strides, but brain-inspired computing remains a distinctive field. Although there were early hopes of AI closely connecting with brain science, this integration has been minimal. Neuroscience has mostly inspired some early algorithms, while most neural networks only adopted the idea of neuron connections without fully replicating real neural signals. However, brain-inspired algorithms, such as Spiking Neural Networks (SNNs), have shown promising results, often outperforming traditional algorithms in specific tasks and offering lower power consumption. These advancements could inspire new AI models or improve existing ones. This review explores the development of successful brain-inspired algorithms, starting with the structure and function of neurons, including cerebellar structures. It then discussed spiking neural networks, their principles, and recent research, as well as cerebellar-inspired models. Finally, the article summarizes methods for building these models and their applications in fields like robotics.

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Mir, Imran, Faiza Gul, Suleman Mir, et al. "Multi-Agent Variational Approach for Robotics: A Bio-Inspired Perspective." Biomimetics 8, no. 3 (2023): 294. http://dx.doi.org/10.3390/biomimetics8030294.

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This study proposes an adaptable, bio-inspired optimization algorithm for Multi-Agent Space Exploration. The recommended approach combines a parameterized Aquila Optimizer, a bio-inspired technology, with deterministic Multi-Agent Exploration. Stochastic factors are integrated into the Aquila Optimizer to enhance the algorithm’s efficiency. The architecture, called the Multi-Agent Exploration–Parameterized Aquila Optimizer (MAE-PAO), starts by using deterministic MAE to assess the cost and utility values of nearby cells encircling the agents. A parameterized Aquila Optimizer is then used to further increase the exploration pace. The effectiveness of the proposed MAE-PAO methodology is verified through extended simulations in various environmental conditions. The algorithm viability is further evaluated by comparing the results with those of the contemporary CME-Aquila Optimizer (CME-AO) and the Whale Optimizer. The comparison adequately considers various performance parameters, such as the percentage of the map explored, the number of unsuccessful runs, and the time needed to explore the map. The comparisons are performed on numerous maps simulating different scenarios. A detailed statistical analysis is performed to check the efficacy of the algorithm. We conclude that the proposed algorithm’s average rate of exploration does not deviate much compared to contemporary algorithms. The same idea is checked for exploration time. Thus, we conclude that the results obtained for the proposed MAE-PAO algorithm provide significant advantages in terms of enhanced map exploration with lower execution times and nearly no failed runs.

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Gutiérrez, Álvaro. "Recent Advances in Swarm Robotics Coordination: Communication and Memory Challenges." Applied Sciences 12, no. 21 (2022): 11116. http://dx.doi.org/10.3390/app122111116.

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Du, Fengze. "Research of Bio-Inspired Motion Control in Robotics." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 378–84. http://dx.doi.org/10.62051/ay9zws79.

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Bio-inspired motion control in robotics draws inspiration from biological systems to enhance the movement capabilities of robots. This article explores the integration of bionics techniques in robots’ path planning, motion control, and design of moving parts, offering advantages over traditional robots control systems. In path planning, bio-inspired approaches, such as swarm intelligence algorithms and artificial neural networks, optimize trajectories and enable obstacle avoid ability in complex environments. Furthermore, bio-inspired design principles facilitate the creation of motion components tailored for specific locomotion modes, such as legged locomotion and aquatic propulsion, improving robots’ agility and adaptability. Reflex-based and vision-based control methods emulate biological responses and utilize visual sensors to enhance robots’ perception and responsiveness. Additionally, recent advancements include the exploration of Long Short-Term Memory Networks (LSTM) for predicting control inputs based on animal trajectories. Through a synthesis of biomechanical principles, materials science, and artificial intelligence integration, bio-inspired motion control revolutionizes robotic capabilities, with implications for autonomous navigation and task execution in dynamic environments. Future research directions include further investigation into biomechanical principles, advancements in materials science, and the integration of artificial intelligence algorithms for enhanced autonomy and adaptability in robotics.

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Latif, Rachid, Kaoutar Dahmane, Monir Amraoui, Amine Saddik, and Abdelouahed Elouardi. "Evaluation of Bio-inspired SLAM algorithm based on a Heterogeneous System CPU-GPU." E3S Web of Conferences 229 (2021): 01023. http://dx.doi.org/10.1051/e3sconf/202122901023.

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Localization and mapping are a real problem in robotics which has led the robotics community to propose solutions for this problem... Among the competitive axes of mobile robotics there is the autonomous navigation based on simultaneous localization and mapping (SLAM) algorithms: in order to have the capacity to track the localization and the cartography of robots, that give the machines the power to move in an autonomous environment. In this work we propose an implementation of the bio-inspired SLAM algorithm RatSLAM based on a heterogeneous system type CPU-GPU. The evaluation of the algorithm showed that with C/C++ we have an executing time of 170.611 ms with a processing of 5 frames/s and for the implementation on a heterogeneous system we used CUDA as language with an execution time of 160.43 ms.

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Araujo-Neto, Wolmar, Leonardo Rocha Olivi, Daniel Khede Dourado Villa, and Mário Sarcinelli-Filho. "Data Fusion Applied to the Leader-Based Bat Algorithm to Improve the Localization of Mobile Robots." Sensors 25, no. 2 (2025): 403. https://doi.org/10.3390/s25020403.

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The increasing demand for autonomous mobile robots in complex environments calls for efficient path-planning algorithms. Bio-inspired algorithms effectively address intricate optimization challenges, but their computational cost increases with the number of particles, which is great when implementing algorithms of high accuracy. To address such topics, this paper explores the application of the leader-based bat algorithm (LBBA), an enhancement of the traditional bat algorithm (BA). By dynamically incorporating robot orientation as a guiding factor in swarm distribution, LBBA improves mobile robot localization. A digital compass provides precise orientation feedback, promoting better particle distribution, thus reducing computational overhead. Experiments were conducted using a mobile robot in controlled environments containing obstacles distributed in diverse configurations. Comparative studies with leading algorithms, such as Manta Ray Foraging Optimization (MRFO) and Black Widow Optimization (BWO), highlighted the proposed algorithm’s ability to achieve greater path accuracy and faster convergence, even when using fewer particles. The algorithm consistently demonstrated robustness in bypassing local minima, a notable limitation of conventional bio-inspired approaches. Therefore, the proposed algorithm is a promising solution for real-time localization in resource-constrained environments, enhancing the accuracy and efficiency in the guidance of mobile robots, thus highlighting its potential for broader adoption in mobile robotics.

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Verma, Jyotsna, and Nishtha Kesswani. "A Review on Bio-Inspired Migration Optimization Techniques." International Journal of Business Data Communications and Networking 11, no. 1 (2015): 24–35. http://dx.doi.org/10.4018/ijbdcn.2015010103.

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Nature inspired computing techniques has become a very popular topic in recent years. Number of applications in computer networks, robotics, biology, combinatorial optimization, etc. can be seen in literatures which are based on the bio-inspired techniques. Nature inspired techniques are proven to solve complex optimization problems irrespective of their problem size. This review summarizes various nature inspired migration algorithms and comparison between them, based on the automated tools, evolutionary techniques and applications.

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Kumar, Suresh, and Patricia Sha. "Human Brain inspired Artificial Intelligence & Developmental Robotics: A Review." Sukkur IBA Journal of Computing and Mathematical Sciences 1, no. 1 (2017): 43. http://dx.doi.org/10.30537/sjcms.v1i1.6.

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Along with the developments in the field of the robotics, fascinating contributions and developments can be seen in the field of Artificial intelligence (AI). In this paper we will discuss about the developments is the field of artificial intelligence focusing learning algorithms inspired from the field of Biology, particularly large scale brain simulations, and developmental Psychology. We will focus on the emergence of the Developmental robotics and its significance in the field of AI.

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Muhsen, Dena Kadhim, Ahmed T. Sadiq, and Firas Abdulrazzaq Raheem. "A Survey on Swarm Robotics for Area Coverage Problem." Algorithms 17, no. 1 (2023): 3. http://dx.doi.org/10.3390/a17010003.

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The area coverage problem solution is one of the vital research areas which can benefit from swarm robotics. The greatest challenge to the swarm robotics system is to complete the task of covering an area effectively. Many domains where area coverage is essential include exploration, surveillance, mapping, foraging, and several other applications. This paper introduces a survey of swarm robotics in area coverage research papers from 2015 to 2022 regarding the algorithms and methods used, hardware, and applications in this domain. Different types of algorithms and hardware were dealt with and analysed; according to the analysis, the characteristics and advantages of each of them were identified, and we determined their suitability for different applications in covering the area for many goals. This study demonstrates that naturally inspired algorithms have the most significant role in swarm robotics for area coverage compared to other techniques. In addition, modern hardware has more capabilities suitable for supporting swarm robotics to cover an area, even if the environment is complex and contains static or dynamic obstacles.

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Tiago Sant’Anna and Lucas Silva. "Biology-Inspired Innovations in Soft Robotics for Efficient Locomotion." JOURNAL OF BIOENGINEERING, TECHNOLOGIES AND HEALTH 7, no. 2 (2024): 218–20. http://dx.doi.org/10.34178/jbth.v7i2.400.

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Inspired by nature, soft robotics promises to overcome traditional robots' limitations by using the flexibility and adaptability of living organisms to navigate complex environments. This field aims to replicate natural movements, such as the peristaltic motion of earthworms, applying them to robots to enhance locomotion and manipulation capabilities. Research focuses on developing prototypes inspired by biological mechanisms, with significant advances in design, actuation, and control, highlighting applications in challenging environments. Studies include the development of mobile robots with pneumatic actuation and models that mimic earthworm locomotion and exploring the use of friction for efficient movement. Soft robotics points to a future with more adaptable and efficient robots, promising innovations in inspection, exploration, and medicine, thanks to integrating new materials, actuators, and control algorithms.

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Rozprawy doktorskie na temat "Robotics-inspired algorithms"

Denarie, Laurent. "Robotics-inspired methods to enhance protein design." Phd thesis, Toulouse, INPT, 2017. http://oatao.univ-toulouse.fr/18677/1/Denarie.pdf.

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The ability to design proteins with specific properties would yield great progress in pharmacology and bio-technologies. Methods to design proteins have been developed since a few decades and some relevant achievements have been made including de novo protein design. Yet, current approaches suffer some serious limitations. By not taking protein’s backbone motions into account, they fail at capturing some of the properties of the candidate design and cannot guarantee that the solution will in fact be stable for the goal conformation. Besides, although multi-states design methods have been proposed, they do not guarantee that a feasible trajectory between those states exists, which means that design problem involving state transitions are out of reach of the current methods. This thesis investigates how robotics-inspired algorithms can be used to efficiently explore the conformational landscape of a protein aiming to enhance protein design methods by introducing additional backbone flexibility. This work also provides first milestones towards protein motion design.

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Northcutt, Brandon D. "Biologically Inspired Algorithms for Visual Navigation and Object Perception in Mobile Robotics." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/612074.

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There is a large gap between the visual capabilities of biological organisms and visual capabilities of autonomous robots. Even the most simple of flying insects is able to fly within complex environments, locate food, avoid obstacles and elude predators with seeming ease. This stands in stark contrast to even the most advanced of modern ground based or flying autonomous robots, which are only capable of autonomous navigation within simple environments and will fail spectacularly if the expected environment is modified even slightly. This dissertation provides a narrative of the author's graduate research into biologically inspired algorithms for visual perception and navigation with autonomous robotics applications. This research led to several novel algorithms and neural network implementations, which provide improved capabilities of visual sensation with exceedingly light computational requirements. A new computationally-minimal approach to visual motion detection was developed and demonstrated to provide obstacle avoidance without the need for directional specificity. In addition, a novel method of calculating sparse range estimates to visual object boundaries was demonstrated for localization, navigation and mapping using one-dimensional image arrays. Lastly, an assembly of recurrent inhibitory neural networks was developed to provide multiple concurrent object detection, visual feature binding, and internal neural representation of visual objects. These algorithms are promising avenues for future research and are likely to lead to more general, robust and computationally minimal systems of passive visual sensation for a wide variety of autonomous robotics applications.

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Kachurka, Viachaslau. "Contribution à la perception et l’attention visuelle artificielle bio-inspirée pour acquisition et conceptualisation de la connaissance en robotique autonome." Thesis, Paris Est, 2017. http://www.theses.fr/2017PESC1072/document.

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La présente thèse du domaine de la « Perception Bio-inspirée » se focalise plus particulièrement sur l’Attention Visuelle Artificielle et la Saillance Visuelle. Un concept de l’Attention Visuelle Artificielle inspiré du vivant, conduisant un modèle d’une telle attention artificielle bio-inspirée, a été élaboré, mis en œuvre et testé dans le contexte de la robotique autonome. En effet, bien qu’il existe plusieurs dizaines de modèles de la saillance visuelle, à la fois en termes de contraste et de cognition, il n’existe pas de modèle hybridant les deux mécanismes d’attention : l’aspect visuel et l’aspect cognitif.Pour créer un tel modèle, nous avons exploré les approches existantes dans le domaine de l’attention visuelle, ainsi que plusieurs approches et paradigmes relevant des domaines connexes (tels que la reconnaissance d’objets, apprentissage artificiel, classification, etc.).Une architecture fonctionnelle d’un système d’attention visuelle hybride, combinant des principes et des mécanismes issus de l’attention visuelle humaine avec des méthodes calculatoires et algorithmiques, a été mise en œuvre, expliquée et détaillée.Une autre contribution majeure du présent travail doctoral est la modélisation théorique, le développement et l’application pratique du modèle d’Attention Visuelle bio-inspiré précité, pouvant constituer un socle pour l’autonomie des systèmes robotisés d’assistance.Les études menées ont conclu à la validation expérimentale des modèles proposés, confirmant la pertinence de l’approche proposée dans l’accroissement de l’autonomie des systèmes robotisés – et ceci dans un environnement réel Dealing with the field of "Bio-inspired Perception", the present thesis focuses more particularly on Artificial Visual Attention and Visual Saliency. A concept of Artificial Visual Attention, inspired from the human mechanisms, providing a model of such artificial bio-inspired attention, was developed, implemented and tested in the context of autonomous robotics. Although there are several models of visual saliency, in terms of contrast and cognition, there is no hybrid model integrating both mechanisms of attention: the visual aspect and the cognitive aspect.To carryout such a model, we have explored existing approaches in the field of visual attention, as well as several approaches and paradigms in related fields (such as object recognition, artificial learning, classification, etc.).A functional architecture of a hybrid visual attention system, combining principles and mechanisms derived from human visual attention with computational and algorithmic methods, was implemented, explained and detailed.Another major contribution of this doctoral work is the theoretical modeling, development and practical application of the aforementioned Bio-inspired Visual Attention model, providing a basis for the autonomy of assistance-robotic systems.The carried out studies and experimental validation of the proposed models confirmed the relevance of the proposed approach in increasing the autonomy of robotic systems within a real environment

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Labella, Thomas Halva. "Division of labour in groups of robots." Doctoral thesis, Universite Libre de Bruxelles, 2007. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210738.

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In this thesis, we examine algorithms for the division of labour in a group of robot. The algorithms make no use of direct communication. Instead, they are based only on the interactions among the robots and between the group and the environment.Division of labour is the mechanism that decides how many robots shall be used to perform a task. The efficiency of the group of robots depends in fact on the number of robots involved in a task. If too few robots are used to achieve a task, they might not be successful or might perform poorly. If too many robots are used, it might be a waste of resources. The number of robots to use might be decided a priori by the system designer. More interestingly, the group of robots might autonomously select how many and which robots to use. In this thesis, we study algorithms of the latter type.The robotic literature offers already some solutions, but most of them use a form of direct communication between agents. Direct, or explicit, communication between the robots is usually considered a necessary condition for co-ordination. Recent studies have questioned this assumption. The claim is based on observations of animal colonies, e.g. ants and termites. They can effectively co-operate without directly communicating, but using indirect forms of communication like stigmergy. Because they do not rely on communication, such colonies show robust behaviours at group level, a condition that one wishes also for groups of robots. Algorithms for robot co-ordination without direct communication have been proposed in the last few years. They are interesting not only because they are a stimulating intellectual challenge, but also because they address a situation that might likely occur when using robots for real-world out-door applications. Unfortunately, they are still poorly studied.This thesis helps the understanding and the development of such algorithms. We start from a specific case to learn its characteristics. Then we improve our understandings through comparisons with other solutions, and finally we port everything into another domain.We first study an algorithm for division of labour that was inspired by ants' foraging. We test the algorithm in an application similar to ants' foraging: prey retrieval. We prove that the model used for ants' foraging can be effective also in real conditions. Our analysis allows us to understand the underlying mechanisms of the division of labour and to define some way of measuring it.Using this knowledge, we continue by comparing the ant-inspired algorithm with similar solutions that can be found in the literature and by assessing their differences. In performing these comparisons, we take care of using a formal methodology that allows us to spare resources. Namely, we use concepts of experiment design to reduce the number of experiments with real robots, without losing significance in the results.Finally, we apply and port what we previously learnt into another application: Sensor/Actor Networks (SANETs). We develop an architecture for division of labour that is based on the same mechanisms as the ants' foraging model. Although the individuals in the SANET can communicate, the communication channel might be overloaded. Therefore, the agents of a SANET shall be able to co-ordinate without accessing the communication channel. Doctorat en sciences appliquées info:eu-repo/semantics/nonPublished

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Webster, Victoria Ann. "Simulating Complex Multi-Degree-Of-Freedom Systems and Muscle-Like Actuators." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1354289624.

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Milia, Valentin. "Couplage de modèles de chimie quantique et d'algorithmes haute performance pour l'exploration globale du paysage énergétique de systèmes atomiques et moléculaires." Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSEP095.

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L'objectif principal de cette thèse est de développer des méthodes efficaces pour caractériser les conformations des molécules à un niveau quantique. Différentes méthodes dédiées au calcul de l'énergie potentielle d’une molécule sont examinées, ainsi que les schémas d'exploration globale des surfaces d'énergie potentielle (SEP) les plus populaires sont présentés. Une contribution clé de cette thèse est le couplage de la méthode IGLOO (Iterative Global exploration and LOcal Optimization), inspirée de la robotique, mise en œuvre dans le logiciel MoMA, avec le potentiel basé sur la “Density-Functional based Tight-Binding” (DFTB), implémenté dans le logiciel deMonNano. IGLOO intègre l'algorithme de planification de mouvement “Rapidly-exploring Random Trees” (RRT) avec des optimisations locales de l’énergie et un filtrage des structures. Une preuve de concept a été réalisée par l'identification des conformations de basse énergie de la molécule de d'alanine dipeptide.Le couplage IGLOO/DFTB a été appliqué à la cartographie des SEP de trois molécules de taille proche de la famille des phtalates (dibutyl phtalate DBP, benzyl butyl phtalate BBP et di-2-éthylhexyl phtalate DEHP), donnant un aperçu détaillé de leurs différents paysages conformationnels. Divers descripteurs géométriques ont été utilisés pour analyser leurs relations structure-énergie. Les interactions de Coulomb, l'encombrement stérique et les interactions dispersives sont à l'origine des propriétés géométriques et une forte corrélation a été mise en évidence entre les deux angles diédraux décrivant l'orientation des chaînes latérales des molécules de phtalate.En complément, un algorithme innovant pour la génération à grande échelle de molécules, incluant une variété de conformations, est présenté. Il combine la génération de graphes de molécules avec des techniques d'ajout d'atomes ou de fragments. Il est appliqué pour fournir une vaste base de données de structures 3D de molécules de carbone amorphe hydrogéné (a-CH). L'analyse de la base de données générée dans cette étude permet de comprendre la relation entre les descripteurs géométriques et électroniques des structures a-C:H. Ces propriétés sont comparées à celles des hydrocarbures aromatiques polycycliques (HAP) compacts et des chaînes linéaires, qui représentent des cas limites.Enfin, une revue des méthodes visant à identifier les points de selle et les chemins de transition entre les conformations de faible énergie sur la SEP est présentée. Une première étape pour l'identification des chemins de transition entre les conformations de faible énergie à l'aide d'un algorithme de planification de mouvement, connu sous le nom de Transition-based RRT (T-RRT), est présentée. Une mesure de similarité, désignée sous le nom de Symmetrized Segment-Path Distance (SSPD), est utilisée pour comparer les trajectoires générées. Ensuite, une technique de regroupement, à savoir Analyse de regroupement hiérarchique (HCA), est employée pour regrouper les trajectoires afin d'identifier les classes de chemin donnant la dynamique des changements de conformation. La méthodologie a été appliquée avec succès à l'identification de chemins à faible énergie entre deux minima de la SEP de l’alanine dipeptide.Dans l'ensemble, les travaux présentent des avancées significatives dans l'exploration de SEP de molécules complexes au niveau quantique, y compris (i) le couplage IGLOO/DFTB (ii) un nouvel algorithme pour la génération de structures 3D de molécules à grande échelle et (iii) un schéma original permettant l'identification de multiples chemins de transition. Des corrélations entre les propriétés structurelles, énergétiques et électroniques ont été mises en évidence pour les molécules polluantes de la famille des phtalates ainsi que pour les a-CH ayant une importance du point de vue astrophysique. Ces contributions ouvrent la voie à de futures recherches visant à étendre ces méthodes à des systèmes plus grands et plus complexes The primary aim of this thesis is to develop efficient methods for characterizing molecular conformations at a quantum level. Various methods devoted to the computation of molecular potential energy are reviewed, as well as the most popular potential energy surfaces (PES) global exploration schemes. In this context, a key contribution of this thesis is the coupling of the robotics-inspired Iterative Global exploration and LOcal Optimization (IGLOO) method, implemented in the MoMA software, with the quantum Density-Functional based Tight-Binding (DFTB) potential, implemented in the deMonNano software. The IGLOO algorithm integrates the motion planning Rapidly-exploring Random Trees (RRT) algorithm with local optimization and structural filtering. A proof of concept has been done through the identification of low-energy conformations of the alanine dipeptide.The IGLOO/DFTB coupling has been applied to the mapping of the PES of three close-sized molecules of the phthalate family (dibutyl phthalate DBP, benzyl butyl phthalate BBP and di-2-ethylhexyl phthalate DEHP), providing detailed insights into their different conformational landscapes. Various geometrical descriptors have been used to analyze their structure-energy relationships. Coulomb interactions, steric hindrance, and dispersive interactions have been found to drive the geometric properties and a strong correlation has been evidenced between the two dihedral angles describing the side-chains orientation of the phthalate molecules. The results demonstrate the method's capability to identify low-energy minima without prior knowledge of the PES.Furthermore, an innovative algorithm for the large-scale generation of molecular structures, including a conformational variety, is presented. It combines molecular graph generation with atom or fragment addition techniques. It is applied to provide an extensive database of 3D structures of hydrogenated amorphous carbon (a-CH) molecules. The analysis of the database generated in this study provides a comprehensive understanding of the relationship between the geometrical and electronic descriptors of a-C:H structures. These properties are compared with those of compact Polycyclic Aromatic Hydrocarbons and linear chains, representing limit cases.Finally, a review is given on methods aiming at identifying saddle points and transition paths between low-energy conformations on the PES. A first step toward the identification of transition paths between low-energy conformations using a motion planning algorithm, known as Transition-based Rapidly-exploring Random Trees (T-RRT), is presented. A similarity measure, designated as the Symmetrized Segment-Path Distance (SSPD), is used to compare the generated trajectories. Subsequently, a clustering technique, namely the Hierarchical Clustering Analysis (HCA), is employed to group similar trajectories in order to identify the common pathways, thereby providing valuable insights into the dynamics of conformational changes. The methodology has been successfully applied to the identification of low-energy paths between two minima of the alanine dipeptide PES.Overall, the research presents significant advancements in the exploration of complex molecular PES at a quantum level including (i) the IGLOO/DFTB coupling (ii) a novel algorithm for 3D structure generation of large-scale molecules and (iii) an original scheme allowing for the identification of multiple transition paths. Correlations between the structural, energetic and electronic properties have been evidenced for the polluting phthalate molecules and astrophysically relevant hydrogenated amorphous carbon (a-CH) molecules. These contributions pave the way for future research, aiming to extend these methods to larger and more complex systems

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Juston, Raphael. "De l'oeil élémentaire à l'oeil composé artificiel : application à la stabilisation visuelle en vol stationnaire." Thesis, Aix-Marseille, 2013. http://www.theses.fr/2013AIXM4118.

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La stratégie de l'équipe biorobotique est de s'inspirer de découvertes faites en biologie chez l'insecte ailé dont la vision est adaptée à la navigation autonome dans un environnement 3D inconnu. Cette inspiration donne naissance la réalisation de capteurs visuels minimalistes permettant de rendre autonomes des robots volants, pour des tâches complexes telles que : le décollage et l'atterrissage automatiques, l'évitement d'obstacles et, dans le cas de cette thèse, le vol stationnaire.Cette thèse présente la mise en œuvre des capteurs visuels minimalistes bio-inspirés qui, grâce à des algorithmes de traitement que nous avons réalisés, sont capables de localiser la position d'objets visuels en tirant partie de propriétés souvent bannies en optique : un flou, obtenu par défocalisation, associé à un micro-mouvement rétinien actif. Nous montrons que la précision en localisation ainsi obtenue est considérablement améliorée par rapport à la résolution statique définie par l'échantillonnage spatial : ces capteurs optiques bio-inspirés sont donc dotés d'hyperacuité.Cette thèse présente aussi l'œil composé artificiel miniature CurvACE (de 2,2cm3 pour 1,75g) doté d'une vision panoramique (180x60°). Cette thèse décrit la caractérisation et la mise en œuvre du capteur CurvACE sur le robot HyperRob. En fusionnant les mesures de position données par une quarantaine de pixels couvrant un grand champ visuel, l'œil CurvACE mesure sa position par rapport à un environnement visuel texturé complexe. Nous montrons aussi que le robot volant HyperRob, attaché au bout d'un bras, stabilise son roulis et sa position, dans le plan azimutal, grâce à son œil composé artificiel doté d'hyperacuité The biorobotics team from the Institute of Movement Sciences (Marseille, France) takes its inspiration from biological studies on flying insects which are able to navigate into unknown 3D environments with a high maneuverability. These studies led us to build minimalist optical sensors to make aerial robots autonomous for achieving complex tasks such as automatic landing and take-off, obstacle avoidance and very accurate hovering flight depicted in this doctoral thesis. This work presents several bio-inspired visual sensors implemented with different visual processing algorithms. All these sensors are able to locate visual objects (contrasting edges and bars) with unusual properties for optical sensing devices: a blur obtained by defocusing optics related with active retinal micro-movements to improve the sensor resolution. We showed that the resolution in locating contrasting objects can be improved up to 160 fold better than the static resolution defined by the pixel pitch, which means that these bio-inspired optical sensors are endowed with hyperacuity.The thesis presents a miniature artificial compound eye CurvACE (of 1.75g for 2.2cm3) with a panoramic field of view (180x60°). This thesis describes thoroughly the characterization and the implementation of the CurvACE sensor onboard an aerial robot named HyperRob. This artificial compound eye acts as a position sensing device able to measure its position relative to a complex textured scene by fusing the position measurements obtained by 40 pixels. The tethered flying robot HyperRob (a 150-g bi-rotor with a 23-cm wingspan) stabilizes its roll and its position thanks to its hyperacute artificial compound eye

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"A Bio-Inspired Algorithm and Foldable Robot Platform for Collective Excavation." Master's thesis, 2018. http://hdl.handle.net/2286/R.I.50513.

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abstract: Existing robotic excavation research has been primarily focused on lunar mining missions or simple traffic control in confined tunnels, however little work attempts to bring collective excavation into the realm of human infrastructure. This thesis explores a decentralized approach to excavation processes, where traffic laws are borrowed from swarms of fire ants (Solenopsis invicta) or termites (Coptotermes formosanus) to create decision rules for a swarm of robots working together and organizing effectively to create a desired final excavated pattern. First, a literature review of the behavioral rules of different types of insect colonies and the resulting structural patterns over the course of excavation was conducted. After identifying pertinent excavation laws, three different finite state machines were generated that relate to construction, search and rescue operations, and extraterrestrial exploration. After analyzing these finite state machines, it became apparent that they all shared a common controller. Then, agent-based NetLogo software was used to simulate a swarm of agents that run this controller, and a model for excavating behaviors and patterns was fit to the simulation data. This model predicts the tunnel shapes formed in the simulation as a function of the swarm size and a time delay, called the critical waiting period, in one of the state transitions. Thus, by controlling the individual agents' behavior, it was possible to control the structural outcomes of collective excavation in simulation. To create an experimental testbed that could be used to physically implement the controller, a small foldable robotic platform was developed, and it's capabilities were tested in granular media. In order to characterize the granular media, force experiments were conducted and parameters were measured for resistive forces during an excavation cycle. The final experiment verified the robot's ability to engage in excavation and deposition, and to determine whether or not to begin the critical waiting period. This testbed can be expanded with multiple robots to conduct small-scale experiments on collective excavation, such as further exploring the effects of the critical waiting period on the resulting excavation pattern. In addition, investigating other factors like tuning digging efficiency or deposition proximity could help to transition the proposed bio-inspired swarm excavation controllers to implementation in real-world applications. Dissertation/Thesis Masters Thesis Mechanical Engineering 2018

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Książki na temat "Robotics-inspired algorithms"

Carlos, Martín Vide, Truthe Bianca, and SpringerLink (Online service), eds. Theory and Practice of Natural Computing: First International Conference, TPNC 2012, Tarragona, Spain, October 2-4, 2012. Proceedings. Springer Berlin Heidelberg, 2012.

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Bhowmick. Bio-Inspired Swarm Robotics and Control: Algorithms, Mechanisms, and Strategies. IGI Global, 2024.

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Bhowmick. Bio-Inspired Swarm Robotics and Control: Algorithms, Mechanisms, and Strategies. IGI Global, 2024.

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Bhowmick. Bio-Inspired Swarm Robotics and Control: Algorithms, Mechanisms, and Strategies. IGI Global, 2024.

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Bhowmick. Bio-Inspired Swarm Robotics and Control: Algorithms, Mechanisms, and Strategies. IGI Global, 2024.

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Dediu, Adrian-Horia, Carlos Martín-Vide, Bianca Truthe, and Miguel A. Vega-Rodriguez. Theory and Practice of Natural Computing: Second International Conference, TPNC 2013, Cáceres, Spain, December 2013. Proceedings. Springer Berlin / Heidelberg, 2013.

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Magdalena, Luis, Adrian-Horia Dediu, and Carlos Martín-Vide. Theory and Practice of Natural Computing: Fourth International Conference, TPNC 2015, Mieres, Spain, December 15-16, 2015. Proceedings. Springer London, Limited, 2015.

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Dediu, Adrian-Horia, Carlos Martín-Vide, and Manuel Lozano. Theory and Practice of Natural Computing: Third International Conference, TPNC 2014, Granada, Spain, December 9-11, 2014. Proceedings. Springer London, Limited, 2014.

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Dediu, Adrian-Horia, Carlos Martín-Vide, and Bianca Truthe. Theory and Practice of Natural Computing: First International Conference, TPNC 2012, Tarragona, Spain, October 2-4, 2012. Proceedings. Springer, 2012.

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Części książek na temat "Robotics-inspired algorithms"

Abbott, Jade, and Andries P. Engelbrecht. "Nature-Inspired Swarm Robotics Algorithms for Prioritized Foraging." In Lecture Notes in Computer Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09952-1_23.

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Belghiti Alaoui, Soukayna, Badr El Kari, Yassine Chaibi, Zakaria Chalh, and Mohamed Benslimane. "Bio-Inspired Algorithms in Robotics Systems: An Overview." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-74491-4_38.

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Karpenko, Anatoliy P., and Ilia A. Leshchev. "Nature-Inspired Algorithms for Global Optimization in Group Robotics Problems." In Studies in Systems, Decision and Control. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99759-9_8.

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Barredo, Pablo, and Jorge Puente. "Robust Makespan Optimization via Genetic Algorithms on the Scientific Workflow Scheduling Problem." In Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06527-9_8.

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Lochmatter, Thomas, and Alcherio Martinoli. "Tracking Odor Plumes in a Laminar Wind Field with Bio-inspired Algorithms." In Experimental Robotics. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00196-3_54.

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Peldszus, Sven, Henriette Knopp, Yorick Sens, and Thorsten Berger. "Towards ML-Integration and Training Patterns for AI-Enabled Systems." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-73741-1_26.

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AbstractMachine learning (ML) has improved dramatically over the last decade. ML models have become a fundamental part of intelligent software systems, many of which are safety-critical. Since ML models have complex lifecycles, they require dedicated methods and tools, such as pipeline automation or experiment management. Unfortunately, the current state of the art is model-centric, disregarding the challenges of engineering systems with multiple ML models that need to interact to realize complex functionality. Consider, for instance, robotics or autonomous driving systems, where perception architectures can easily incorporate more than 30 ML models. Developing such multi-ML model systems requires architectures that can integrate and chain ML components. Maintaining and evolving them requires tackling the combinatorial explosion when re-training ML components, often exploring different (hyper-)parameters, features, training algorithms, or other ML artifacts. Addressing these problems requires systems-centric methods and tools. In this work, we discuss characteristics of multi-ML-model systems and challenges of engineering them. Inspired by such systems in the autonomous driving domain, our focus is on experiment-management tooling, which supports tracking and reasoning about the training process for ML models. Our analysis reveals their concepts, but also their limitations when engineering multi-ML-model systems, especially due to their model-centric focus. We discuss possible integration patterns and ML training to facilitate the effective and efficient development, maintenance, and evolution of multi-ML-model systems. Furthermore, we describe real-world multi-ML-model systems, providing early results from identifying and analyzing open-source systems from GitHub.

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Singh, Aditya Narayan, Yagyesh Godiyal, Vibha Saroha, Yash Vardhan Singh, and Nripendra Kumar Singh. "Artificial Bee Colony Algorithms in Control Systems, Robotics, and Automation." In Springer Tracts in Nature-Inspired Computing. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-7344-2_4.

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Marín García, Francisco Antonio, José Manuel Cuadra Troncoso, Félix de la Paz López, and José Ramón Álvarez-Sánchez. "Autonomous Robot Navigation by Area Centroid Algorithm Using Depth Cameras." In Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06527-9_26.

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Arufe, Lis, Riccardo Rasconi, Angelo Oddi, Ramiro Varela, and Miguel Ángel González. "Compiling Single Round QCCP-X Quantum Circuits by Genetic Algorithm." In Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06527-9_9.

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Firos, A. "Fuzzy logic and bio-inspired Ant Colony Algorithm-based technique to find relative desirability in IoT-based healthcare system." In Bio-Inspired Data-driven Distributed Energy in Robotics and Enabling Technologies. CRC Press, 2024. http://dx.doi.org/10.1201/9781003530077-10.

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Streszczenia konferencji na temat "Robotics-inspired algorithms"

Ansary, Jamal, Jacob O’Donnell, Nashiyat Fyza, and Brian Trease. "Swarms of Aquatic Unmanned Surface Vehicles (USV), a Review From Simulation to Field Implementation." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22702.

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Abstract Swarm robotic is a field of multi-robotics in which the robot’s behavior is inspired from nature. With rapid development in the field of the multi-robotics and the lack of efficacy in traditional centralized controls method, decentralized nature inspired swarm algorithms were introduced to control the swarm behavior. Unmanned surface vehicles (USVs) are marine crafts that they can operate autonomously. Due to their potential in operating in different areas, these vehicles have been used for variety of reason including patrolling, border protection, environmental monitoring and oil spill confrontation. This paper provides a review of the Swarm of USVs, their application, simulation environments and the algorithms that has been used in the past and current projects.

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Saicharan, Bandari, Ritu Tiwari, and Nirmal Roberts. "Multi Objective optimization based Path Planning in robotics using nature inspired algorithms: A survey." In 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES). IEEE, 2016. http://dx.doi.org/10.1109/icpeices.2016.7853442.

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Millidge, Beren, Tommaso Salvatori, Yuhang Song, Rafal Bogacz, and Thomas Lukasiewicz. "Predictive Coding: Towards a Future of Deep Learning beyond Backpropagation?" In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/774.

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The backpropagation of error algorithm (BP) used to train deep neural networks has been fundamental to the successes of deep learning. However, it requires sequential backwards updates and non-local computations which make it challenging to parallelize at scale and is unlike how learning works in the brain. Neuroscience-inspired learning algorithms, however, such as \emph{predictive coding} which utilize local learning have the potential to overcome these limitations and advance beyond deep learning technologies in the future. While predictive coding originated in theoretical neuroscience as a model of information processing in the cortex, recent work has developed the idea into a general-purpose algorithm able to train neural networks using only local computations. In this survey, we review works that have contributed to this perspective and demonstrate the close connection between predictive coding and backpropagation in terms of generalization quality, as well as works that highlight the multiple advantages of using predictive coding models over backprop-trained neural networks. Specifically, we show the substantially greater flexibility of predictive coding networks against equivalent deep neural networks, which can function as classifiers, generators, and associative memories simultaneously, and can be defined on arbitrary graph topologies. Finally, we review direct benchmarks of predictive coding networks on machine learning classification tasks, as well as its close connections to control theory and applications in robotics.

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Lochmatter, T., and A. Martinoli. "Theoretical analysis of three bio-inspired plume tracking algorithms." In 2009 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2009. http://dx.doi.org/10.1109/robot.2009.5152686.

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Li, Junfei, Simon X. Yang, and Zhe Xu. "A Survey on Robot Path Planning using Bio-inspired Algorithms." In 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2019. http://dx.doi.org/10.1109/robio49542.2019.8961498.

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Herrera, Rosana Matuk. "Bio-inspired algorithms for tactile control of dexterous manipulation." In 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob 2012). IEEE, 2012. http://dx.doi.org/10.1109/biorob.2012.6290889.

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Mundhenk, Terrell N., Nitin Dhavale, Salvador Marmol, et al. "Utilization and viability of biologically-inspired algorithms in a dynamic multiagent camera surveillance system." In Photonics Technologies for Robotics, Automation, and Manufacturing, edited by David P. Casasent, Ernest L. Hall, and Juha Roning. SPIE, 2003. http://dx.doi.org/10.1117/12.515176.

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Guth, Felipe, Luan Silveira, Silvia Botelho, Paulo Drews, and Pedro Ballester. "Underwater SLAM: Challenges, state of the art, algorithms and a new biologically-inspired approach." In 2014 5th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob). IEEE, 2014. http://dx.doi.org/10.1109/biorob.2014.6913908.

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Rojas, Salvador, He Shen, Holly Griffiths, Ni Li, and Lanchun Zhang. "Motion and Gesture Compliance Control for High Performance of a Wheeled Humanoid Robot." In ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-72337.

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Humanoid robots have the potential to help or even take the place of humans working in extreme or undesirable environments. Wheeled humanoids are robots that combine the mobility of mobile platforms, and the dexterity of an articulated body with two robotic arms. To perform like a human being, these robots normally are designed with a high center of mass, which makes it challenging to maintain stability while achieving high performance on complex and unpredictable terrain. Inspired from how humans react to balance themselves, a compliance control method is studied to help the wheeled humanoid robot developed at the Robotics Laboratory at Cal State LA achieve high dynamic performance while scouting over uneven terrain. Lagrange-Euler method is used to obtain the dynamic model of the humanoid robot. Then a nonlinear sliding mode compliance controller is derived and proven to ensure asymptotic stability of the humanoid robot while tracking desired reference trajectories. Finally, the performance of the proposed compliance control system is demonstrated using simulation. The results show that the robot successfully tracks a given input while maintaining balance based on the proposed tip-over avoidance algorithm.

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"Development of a Modern Simulation Software and Analysis of its Performance Using a Swarm System Architecture Driven by Nature-Inspired Algorithms." In 2021 IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things (RAAICON). IEEE, 2021. http://dx.doi.org/10.1109/raaicon54709.2021.9929707.

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Raporty organizacyjne na temat "Robotics-inspired algorithms"

Pasupuleti, Murali Krishna. Quantum-Enhanced Machine Learning: Harnessing Quantum Computing for Next-Generation AI Systems. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv125.

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Abstract Quantum-enhanced machine learning (QML) represents a paradigm shift in artificial intelligence by integrating quantum computing principles to solve complex computational problems more efficiently than classical methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the potential to accelerate deep learning training, optimize combinatorial problems, and enhance feature selection in high-dimensional spaces. This research explores foundational quantum computing concepts relevant to AI, including quantum circuits, variational quantum algorithms, and quantum kernel methods, while analyzing their impact on neural networks, generative models, and reinforcement learning. Hybrid quantum-classical AI architectures, which combine quantum subroutines with classical deep learning models, are examined for their ability to provide computational advantages in optimization and large-scale data processing. Despite the promise of quantum AI, challenges such as qubit noise, error correction, and hardware scalability remain barriers to full-scale implementation. This study provides an in-depth evaluation of quantum-enhanced AI, highlighting existing applications, ongoing research, and future directions in quantum deep learning, autonomous systems, and scientific computing. The findings contribute to the development of scalable quantum machine learning frameworks, offering novel solutions for next-generation AI systems across finance, healthcare, cybersecurity, and robotics. Keywords Quantum machine learning, quantum computing, artificial intelligence, quantum neural networks, quantum kernel methods, hybrid quantum-classical AI, variational quantum algorithms, quantum generative models, reinforcement learning, quantum optimization, quantum advantage, deep learning, quantum circuits, quantum-enhanced AI, quantum deep learning, error correction, quantum-inspired algorithms, quantum annealing, probabilistic computing.

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