Relevant bibliographies by topics / Biologically-inspired robots

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Biologically-inspired robots'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Biologically-inspired robots"

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Holland, Owen. "The first biologically inspired robots." Robotica 21, no. 4 (2003): 351–63. http://dx.doi.org/10.1017/s0263574703004971.

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Thie first biologically inspired robots, the famous electromechanical tortoises, were designed and built in 1949 by W. Grey Walter. This paper reviews their origins in Walter's theories of the brain and the nature of life, and uses contemporary unpublished notes and photographs to assess their significance then and now.
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Bekey, George A. "Biologically inspired control of autonomous robots." Robotics and Autonomous Systems 18, no. 1-2 (1996): 21–31. http://dx.doi.org/10.1016/0921-8890(96)00022-x.

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Wang, Hongqiang, Peter York, Yufeng Chen, et al. "Biologically inspired electrostatic artificial muscles for insect-sized robots." International Journal of Robotics Research 40, no. 6-7 (2021): 895–922. http://dx.doi.org/10.1177/02783649211002545.

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Millimeter-sized electrostatic film actuators, inspired by the efficient spatial arrangement of insect muscles, achieve a muscle-like power density (61 W kg−1) and enable robotic applications in which agility is needed in confined spaces. Like biological muscles, these actuators incorporate a hierarchical structure, in this case building from electrodes to arrays to laminates, and are composed primarily of flexible materials. So comprised, these actuators can be designed for a wide range of manipulation and locomotion tasks, similar to natural muscle, while being robust and compact. A typical actuator can achieve 85 mN of force with a 15 mm stroke, with a size of [Formula: see text] mm3 and mass of 92 mg. Two millimeter-sized robots, an ultra-thin earthworm-inspired robot and an intestinal-muscle-inspired endoscopic tool for tissue resection, demonstrate the utility of these actuators. The earthworm robot undertakes inspection tasks: the navigation of a 5 mm channel and a 19 mm square tube while carrying an on-board camera. The surgical tool, which conforms to the surface of the distal end of an endoscope, similar to the thin, smooth muscle that covers the intestine, completes tissue cutting and penetrating tasks. Beyond these devices, we anticipate widespread use of these actuators in soft robots, medical robots, wearable robots, and miniature autonomous systems.
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Kelasidi, Eleni, Pal Liljeback, Kristin Y. Pettersen, and Jan Tommy Gravdahl. "Innovation in Underwater Robots: Biologically Inspired Swimming Snake Robots." IEEE Robotics & Automation Magazine 23, no. 1 (2016): 44–62. http://dx.doi.org/10.1109/mra.2015.2506121.

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Belter, Dominik, and Piotr Skrzypczyński. "A biologically inspired approach to feasible gait learning for a hexapod robot." International Journal of Applied Mathematics and Computer Science 20, no. 1 (2010): 69–84. http://dx.doi.org/10.2478/v10006-010-0005-7.

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A biologically inspired approach to feasible gait learning for a hexapod robotThe objective of this paper is to develop feasible gait patterns that could be used to control a real hexapod walking robot. These gaits should enable the fastest movement that is possible with the given robot's mechanics and drives on a flat terrain. Biological inspirations are commonly used in the design of walking robots and their control algorithms. However, legged robots differ significantly from their biological counterparts. Hence we believe that gait patterns should be learned using the robot or its simulation model rather than copied from insect behaviour. However, as we have foundtahula rasalearning ineffective in this case due to the large and complicated search space, we adopt a different strategy: in a series of simulations we show how a progressive reduction of the permissible search space for the leg movements leads to the evolution of effective gait patterns. This strategy enables the evolutionary algorithm to discover proper leg co-ordination rules for a hexapod robot, using only simple dependencies between the states of the legs and a simple fitness function. The dependencies used are inspired by typical insect behaviour, although we show that all the introduced rules emerge also naturally in the evolved gait patterns. Finally, the gaits evolved in simulations are shown to be effective in experiments on a real walking robot.
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Zhang, Chi, Wei Zou, Liping Ma, and Zhiqing Wang. "Biologically inspired jumping robots: A comprehensive review." Robotics and Autonomous Systems 124 (February 2020): 103362. http://dx.doi.org/10.1016/j.robot.2019.103362.

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Shahbazi, Hamed, Kamal Jamshidi, Amir Hasan Monadjemi, and Hafez Eslami. "Biologically inspired layered learning in humanoid robots." Knowledge-Based Systems 57 (February 2014): 8–27. http://dx.doi.org/10.1016/j.knosys.2013.12.003.

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Bar-Cohen, Y. "Biologically Inspired Intelligent Robots using Artificial Muscles." Strain 41, no. 1 (2005): 19–24. http://dx.doi.org/10.1111/j.1475-1305.2004.00161.x.

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Masár, Marek, and Ivana Budinská. "Robot Coordination Based on Biologically Inspired Methods." Advanced Materials Research 664 (February 2013): 891–96. http://dx.doi.org/10.4028/www.scientific.net/amr.664.891.

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An embedded particle swarm optimization (PSO) technique combined with virtual pheromones deposition and rules for artificial bird flocking is proposed to handle an area coverage problem using a swarm of mobile robots. A simulation tool VERA that was developed to simulate a swarm behavior of a group of mobile agents is described. Results of simulation experiments and tests on Lego robots that prove the concept are presented. Results are discussed and future development is suggested in the end of the paper.
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Parker, Chris A. C., and Hong Zhang. "Biologically inspired collective comparisons by robotic swarms." International Journal of Robotics Research 30, no. 5 (2011): 524–35. http://dx.doi.org/10.1177/0278364910397621.

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Intelligent entities must often make decisions by comparing several candidate alternatives and selecting the best one. This is just as true for autonomous swarms as it is for solitary robots, but to date there has been little work to propose efficient comparison behaviors for autonomous robotic swarms that are not tied to specific environments. In this work, we examine an elegant collective comparison strategy that is used by at least three different species of social insect and adapt it for artificial systems. The behavior is particularly attractive for robotic implementations because it relies only on short range explicit peer-to-peer communication, eliminating the need for chemical trails or other forms of stigmergy. The proposed comparison strategy is proven to converge, and a series of experiments using real robots with noisy sensors is presented that validates our theoretical analysis. Using the proposed behavior, a robotic swarm is able to compare alternatives collectively more accurately than its member robots would be able to individually.
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Dissertations / Theses on the topic "Biologically-inspired robots"

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Peng, Shiqi. "A biologically inspired four legged walking robot." Peng, Shiqi (2006) A biologically inspired four legged walking robot. PhD thesis, Murdoch University, 2006. http://researchrepository.murdoch.edu.au/255/.

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This Ph.D. thesis presents the design and implementation of a biologically inspired four-phase walking strategy using behaviours for a four legged walking robot. In particular, the walking strategy addresses the balance issue, including both static and dynamic balance that were triggered non-deterministically based on the robot's realtime interaction with the environment. Four parallel Subsumption Architectures (SA) and a simple Central Pattern Producer (CPP) are employed in the physical implementation of the walking strategy. An implementation framework for such a parallel Subsumption Architecture is also proposed to facilitate the reusability of the system. A Reinforcement Learning (RL) method was integrated into the CPP to allow the robot to learn the optimal walking cycle interval (OWCI), appropriate for the robot walking on various terrain conditions. Experimental results demonstrate that the robot employs the proposed walking strategy and can successfully carry out its walking behaviours under various experimental terrain conditions, such as flat ground, incline, decline and uneven ground. Interactions of all the behaviours of the robot enable it to exhibit a combination of both preset and emergent walking behaviours.
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Mamrak, Justin. "MARK II a biologically-inspired walking robot /." Ohio : Ohio University, 2008. http://www.ohiolink.edu/etd/view.cgi?ohiou1226694264.

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Dong, Wei S. M. Massachusetts Institute of Technology. "Biologically-inspired robots for stage performance." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62126.

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Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2010.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 46-47).<br>Stage performances present many challenges and opportunities in the field of robotics. Onstage robots not only have to function flawlessly, they must interact convincingly with their human counterparts and adhere to a rigid timeline. The scope of this work is to create set pieces that look and behave like organic entities for the production of Tod Machover's new opera, Death and the Powers. With a set of design rules and techniques, I have developed the mechanical and control systems, including their interactive behavior, for several performance-ready robots. A six-legged walking robot and transformable robot were first built to verify the adopted design methodology prior to the prototyping of onstage robots. In addition, the robots were certified as performance-ready according to four criteria: the visual appearance, the overall functionality, the quality of movement, and the fluency of human-robot interaction. Two robots were successfully built and tested for use in the opera of Death and the Powers.<br>by Wei Dong.<br>S.M.
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Garratt, Matthew A. "Biologically inspired vision and control for an autonomous flying vehicle /." View thesis entry in Australian Digital Theses Program, 2007. http://thesis.anu.edu.au/public/adt-ANU20090116.154822/index.html.

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Stowers, John Ross. "Biologically Inspired Visual Control of Flying Robots." Thesis, University of Canterbury. Electrical and Computer Engineering, 2013. http://hdl.handle.net/10092/8729.

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Insects posses an incredible ability to navigate their environment at high speed, despite having small brains and limited visual acuity. Through selective pressure they have evolved computationally efficient means for simultaneously performing navigation tasks and instantaneous control responses. The insect’s main source of information is visual, and through a hierarchy of processes this information is used for perception; at the lowest level are local neurons for detecting image motion and edges, at the higher level are interneurons to spatially integrate the output of previous stages. These higher level processes could be considered as models of the insect's environment, reducing the amount of information to only that which evolution has determined relevant. The scope of this thesis is experimenting with biologically inspired visual control of flying robots through information processing, models of the environment, and flight behaviour. In order to test these ideas I developed a custom quadrotor robot and experimental platform; the 'wasp' system. All algorithms ran on the robot, in real-time or better, and hypotheses were always verified with flight experiments. I developed a new optical flow algorithm that is computationally efficient, and able to be applied in a regular pattern to the image. This technique is used later in my work when considering patterns in the image motion field. Using optical flow in the log-polar coordinate system I developed attitude estimation and time-to-contact algorithms. I find that the log-polar domain is useful for analysing global image motion; and in many ways equivalent to the retinotopic arrange- ment of neurons in the optic lobe of insects, used for the same task. I investigated the role of depth in insect flight using two experiments. In the first experiment, to study how concurrent visual control processes might be combined, I developed a control system using the combined output of two algorithms. The first algorithm was a wide-field optical flow balance strategy and the second an obstacle avoidance strategy which used inertial information to estimate the depth to objects in the environment - objects whose depth was significantly different to their surround- ings. In the second experiment I created an altitude control system which used a model of the environment in the Hough space, and a biologically inspired sampling strategy, to efficiently detect the ground. Both control systems were used to control the flight of a quadrotor in an indoor environment. The methods that insects use to perceive edges and control their flight in response had not been applied to artificial systems before. I developed a quadrotor control system that used the distribution of edges in the environment to regulate the robot height and avoid obstacles. I also developed a model that predicted the distribution of edges in a static scene, and using this prediction was able to estimate the quadrotor altitude.
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Yau, Chi-Yung. "A biologically inspired neural architecture for emotional robots." Thesis, University of Sunderland, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.529272.

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Amayo, Paul Omondi. "Biologically inspired goal directed navigation for mobile robots." Master's thesis, University of Cape Town, 2016. http://hdl.handle.net/11427/20512.

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This project involved an investigation into low-cost navigation of mobile robots with the aim of creating and adaptive navigation system inspired by behaviour seen in animals. The navigation module developed here would need to be able to successfully localise a robot and navigate it to a defined target. A critical literature review was carried out of current localisation and path-planning architectures and a bio-inspired approach using an Echo State Network and Liquid State Machine architecture was chosen as the base for the navigation modules. The navigation module implemented in this work is trained to navigate and localise itself in different environments drawing its inspiration from the behaviour of small rodents. These architectures were adapted for use by a robot with a view on the physical implementation of these architectures on an embedded low-cost robot using a Raspberry Pi computer. This robot was then built using low-cost, noisy proximity sensors which formed the inputs to the navigation modules. Before the deployment on the embedded robot the system was tested and validated in a full physics simulator. While the training of the Echo State Networks and Liquid State Machine has been carried out in the literature by the offline method of linear regression, in this work we introduce a novel way of training these networks that is online using concepts from adaptive filters. This online method increases the adaptability of this system while significantly decreasing its memory requirements making it very attractive for low-cost embedded robots. The end result from the project was a functioning navigation module using an Echo State Network that was able to navigate the robot to a target position as well as learn new paths, either using offline or online methods. The results showed that the Echo State Network approach was valid both in simulation and practically as a base for creating navigation modules for low-cost robots and could also lead to more efficient and adaptable robots being developed if the training was carried out in an online manner. The increased computational complexity of implementing the liquid State machine on analytical machines however made it unsuitable for deployment on robots using embedded micro-controllers.
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Diller, Eric David. "Design of a Biologically-Inspired Climbing Hexapod Robot for Complex Maneuvers." Cleveland, Ohio : Case Western Reserve University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1259960651.

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Thesis(M.S.)--Case Western Reserve University, 2010<br>Title from PDF (viewed on 2010-01-28) Department of EMC - Mechanical Engineering Includes abstract Includes bibliographical references and appendices Available online via the OhioLINK ETD Center
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McBride, Michael F. "Biologically inspired sensory processing for mobile robots using Spiking Neural Networks." Thesis, Ulster University, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.538953.

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This thesis is focused on research into biologically inspired sensory fusion for a mobile robot. The approach is based upon a bio-inspired Liquid State Machine (Reservoir Computing) paradigm, utilising Spiking Neural Networks in the reservoir as the core of the sensory fusion system, with a conventional classical artificial neural network in the readout phase. The connectivity and structure of the LSM is inspired by the biological example of the mammalian brain and in particular by the connectivity of the somatosensory cortex. The use of the reservoir computing paradigm allows for effective integration of data from different sensory modalities within the reservoir and permits snapshots of the internal state to be captured for subsequent processing. The use of such an approach provides a novel method for autonomous systems to combine information, in a method which is more closely inspired by nature. The experimental analysis of this research investigates a robot traversing an environment using multiple sensory inputs from multiple sensor types and experiencing varying sensory conditions. The research investigates parameters for sensor data coding and creating a LSM for processing sensor information. An LSM structure is presented to combine the sensor information within its structure. The empirical assessment of the LSM sensor fusion experiments of the robot obstacle avoidance is presented. The experiments demonstrate how the fusing of separate sensor data in the LSM improves the performance of the robot over the performance of processing a single sensor type in the LSM
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Cristiano, Rodríguez Julián Efrén. "Generation and control of locomotion for biped robots based on biologically inspired approaches." Doctoral thesis, Universitat Rovira i Virgili, 2016. http://hdl.handle.net/10803/348879.

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Aquesta tesi proposa l'ús d'aproximacions de control inspirades biològicament per a generar i controlar el patró de locomoció omnidireccional de robots humanoides, adaptant el seu moviment a diversos tipus de terreny pla usant realimentació multisensorial. Els sistemes de control de locomoció proposats van ser implementats usant xarxes de Generadors Centrals de Patrons (CPG) basades en el model de neurona de Matsuoka. Els CPGs són xarxes neuronals biològiques situades en el sistema nerviós central dels vertebrats o en els ganglis principals d'invertebrats, les quals poden controlar moviments coordinats. El fet que, a la natura, la locomoció humana i animal sigui controlada mitjançant xarxes CPG ha inspirat la teoria en la qual es basa la present tesi. En particular, la tesi proposa dues arquitectures de control en llaç tancat basades en mètodes de control CPG-espai-articulacions, les quals han estat validades mitjançant un robot simulat i un robot humanoide NAO real. La primera arquitectura de control va identificar algunes característiques importants que un esquema de control CPG-espai-articulacions ha de tenir si es vol descriure un patró de locomoció útil. A partir d'aquesta anàlisi, la segona arquitectura de control va ser proposada per descriure patrons de locomoció ben caracteritzats. Per a millorar el comportament del sistema en llaç tancat, s’ha proposat un mecanisme de reinicialització de fase per a xarxes CPG basades en el model de neurona de Matsuoka. Aquest mecanisme fa possible dissenyar i estudiar controladors de realimentació que poden modificar ràpidament els patrons de locomoció generats. Els resultats obtinguts mostren que els esquemes de control proposats poden produir patrons de locomoció ben caracteritzats amb una resposta ràpida adequada per a robots humanoides amb una capacitat de processament reduïda. Els experiments també indiquen que el sistema de control proposat habilita el robot a respondre ràpida i robustament, i poder fer front a situacions complexes.<br>Esta tesis propone el uso de aproximaciones de control inspiradas biológicamente para generar y controlar el modo de caminar omnidireccional de robots humanoides, adaptando su movimiento a varios tipos de terreno plano usando realimentación multisensorial. Los sistemas de control de locomoción propuestos fueron implementados usando redes de Generadores Centrales de Patrones (CPG) basadas en el modelo de neurona de Matsuoka. Los CPGs son redes neuronales biológicas ubicadas en el sistema nervioso central de vertebrados o en los ganglios principales de invertebrados, las cuales pueden controlar movimientos coordinados. El hecho de que, en la naturaleza, la locomoción humana y animal sea controlada mediante redes CPG ha inspirado la teoría en la cual se basa la presente tesis. En particular, dos arquitecturas de control en lazo cerrado basadas en métodos de control CPG-espacio-articulaciones han sido propuestas y probadas mediante ambos un robot simulado y un robot humanoide NAO real. La primera arquitectura de control identificó algunas características importantes que un esquema de control CPG-espacio-articulaciones debe tener si se quiere describir un patrón de locomoción útil. A partir de este análisis, la segunda arquitectura de control fue propuesta para describir patrones de locomoción bien caracterizados. Para mejorar cómo se comporta el sistema en lazo cerrado, un mecanismo de reseteo de fase para redes CPG basadas en el modelo de neurona de Matsuoka ha sido propuesto. Este mecanismo hace posible diseñar y estudiar controladores de realimentación que pueden modificar rápidamente los patrones de locomoción generados. Los resultados obtenidos muestran que los esquemas de control propuestos pueden producir patrones de locomoción bien caracterizados con una respuesta rápida adecuada para robots humanoides con una capacidad de procesamiento reducida. Estos experimentos también indican que el sistema de control propuesto habilita al robot a responder rápida y robustamente, y poder hacer frente a situaciones complejas.<br>This thesis proposes the use of biologically inspired control approaches to generate and control the omnidirectional gait of humanoid robots, adapting their movement to various types of flat terrain using multi-sensory feedback. The proposed locomotion control systems were implemented using Central Pattern Generator (CPG) networks based on Matsuoka’s neuron model. CPGs are biological neural networks located in the central nervous system of vertebrates or in the main ganglia of invertebrates, which can control coordinated movements, such as those involved in locomotion, respiration, chewing or swallowing. The fact that, in nature, human and animal locomotion is controlled by CPG networks has inspired the theory on which the present thesis is based. In particular, two closed-loop control architectures based on CPG-joint-space control methods have been proposed and tested by using both a simulated and a real NAO humanoid robot. The first control architecture identified some important features that a CPG-joint-space control scheme must have if a useful locomotion pattern is to be described. On the basis of this analysis, the second control architecture was proposed to describe well-characterized locomotion patterns. The new system, characterized by optimized parameters obtained with a genetic algorithm (GA), effectively generated and controlled locomotion patterns for biped robots on flat and sloped terrain. To improve how the system behaves in closed loop, a phase resetting mechanism for CPG networks based on Matsuoka’s neuron model has been proposed. It makes it possible to design and study feedback controllers that can quickly modify the locomotion pattern generated. The results obtained show that the proposed control schemes can yield well-characterized locomotion patterns with a fast response suitable for humanoid robots with a reduced processing capability. These experiments also indicate that the proposed system enables the robot to respond quickly and robustly, and to cope with complex situations.
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Books on the topic "Biologically-inspired robots"

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L, Breazeal Cynthia, ed. Biologically inspired intelligent robots. SPIE Press, 2003.

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Hirose, Shigeo. Biologically inspired robots: Snake-like locomotors and manipulators. Oxford University Press, 1993.

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Biologically inspired robots: Snake-like locomotors and manipulators. Oxford University Press, 1993.

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service), SpringerLink (Online, ed. Biologically Inspired Approaches for Locomotion, Anomaly Detection and Reconfiguration for Walking Robots. Springer-Verlag GmbH Berlin Heidelberg, 2011.

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Jakimovski, Bojan. Biologically Inspired Approaches for Locomotion, Anomaly Detection and Reconfiguration for Walking Robots. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22505-5.

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Claudio, Mattiussi, ed. Bio-inspired artificial intelligence: Theories, methods, and technologies. MIT Press, 2009.

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Duro, Richard J., José Santos, and Manuel Graña, eds. Biologically Inspired Robot Behavior Engineering. Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-7908-1775-1.

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Biologically inspired robotics. Taylor & Francis/CRC Press, 2011.

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Liu, Yunhui, and Dong Sun. Biologically inspired robotics. Taylor & Francis/CRC Press, 2011.

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Spiers, Adam, Said Ghani Khan, and Guido Herrmann. Biologically Inspired Control of Humanoid Robot Arms. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30160-0.

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Book chapters on the topic "Biologically-inspired robots"

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Meyer, Jean-Arcady, and Agnès Guillot. "Biologically Inspired Robots." In Springer Handbook of Robotics. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-30301-5_61.

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Schulz, S., C. Pylatiuk, A. Kargov, et al. "Fluidically Driven Robots with Biologically Inspired Actuators." In Climbing and Walking Robots. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-26415-9_11.

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Hennion, B., J. Pill, and J. C. Guinot. "A Biologically Inspired Model For Quadruped Locomotion." In Climbing and Walking Robots. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-26415-9_5.

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Jakimovski, Bojan. "Biologically Inspired Robot Control Architecture." In Biologically Inspired Approaches for Locomotion, Anomaly Detection and Reconfiguration for Walking Robots. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22505-5_4.

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De Santis, Dalia, Vishwanathan Mohan, Pietro Morasso, and Jacopo Zenzeri. "Do Humanoid Robots Need a Body Schema?" In Biologically Inspired Cognitive Architectures 2012. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34274-5_23.

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Haikonen, Pentti O. A. "Consciousness and the Quest for Sentient Robots." In Biologically Inspired Cognitive Architectures 2012. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34274-5_4.

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Salomon, Ralf. "Self-Adapting Neural Networks for Mobile Robots." In Biologically Inspired Robot Behavior Engineering. Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-7908-1775-1_6.

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Spiers, Adam, Said Ghani Khan, and Guido Herrmann. "Humanoid Robots and Control." In Biologically Inspired Control of Humanoid Robot Arms. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30160-0_2.

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Nolfi, Stefano, and Davide Marocco. "Evolving Robots Able to Integrate Sensory-Motor Information over Time." In Biologically Inspired Robot Behavior Engineering. Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-7908-1775-1_7.

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Ishiguro, Hiroshi. "Biological Fluctuation “Yuragi” as the Principle of Bio-inspired Robots." In Biologically Inspired Cognitive Architectures 2012. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34274-5_5.

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Conference papers on the topic "Biologically-inspired robots"

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Melhuish, Chris, Andrew Adamatzky, and Brett A. Kennedy. "Biologically inspired robots." In SPIE's 8th Annual International Symposium on Smart Structures and Materials, edited by Yoseph Bar-Cohen. SPIE, 2001. http://dx.doi.org/10.1117/12.432659.

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Bar-Cohen, Yoseph, and Cynthia Breazeal. "Biologically inspired intelligent robots." In Smart Structures and Materials, edited by Yoseph Bar-Cohen. SPIE, 2003. http://dx.doi.org/10.1117/12.484379.

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Bar-Cohen, Yoseph. "Biologically inspired robots as artificial inspectors." In NDE For Health Monitoring and Diagnostics, edited by Tribikram Kundu. SPIE, 2002. http://dx.doi.org/10.1117/12.469902.

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Je-sung Koh, Seung-won Kim, Min-kyun Noh, and Kyu-jin Cho. "Biologically inspired robots using Smart Composite Microstructures." In 2011 8th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI 2011). IEEE, 2011. http://dx.doi.org/10.1109/urai.2011.6145989.

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Polverino, Giovanni, and Maurizio Porfiri. "Controlling invasive species with biologically-inspired robots." In The 2021 Conference on Artificial Life. MIT Press, 2021. http://dx.doi.org/10.1162/isal_a_00373.

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Berkvens, Rafael, Adam Jacobson, Michael Milford, Herbert Peremans, and Maarten Weyn. "Biologically inspired SLAM using Wi-Fi." In 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014). IEEE, 2014. http://dx.doi.org/10.1109/iros.2014.6942799.

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Ho, Thanhtam, and Sangyoon Lee. "Two Types of Biologically-Inspired Mesoscale Quadruped Robots." In 2008 IEEE Conference on Robotics, Automation and Mechatronics (RAM). IEEE, 2008. http://dx.doi.org/10.1109/ramech.2008.4681384.

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Smith, Beatrice G. R., Chakravarthini M. Saaj, and Elie Allouis. "Evolving legged robots using biologically inspired optimization strategies." In 2010 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2010. http://dx.doi.org/10.1109/robio.2010.5723523.

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Kim, Hyun K., Woong Kwon, and Kyung Shik Roh. "Biologically Inspired Energy Efficient Walking for Biped Robots." In 2006 IEEE International Conference on Robotics and Biomimetics. IEEE, 2006. http://dx.doi.org/10.1109/robio.2006.340273.

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Hecker, Joshua, Karl Stolleis, Bjorn Swenson, Kenneth Letendre, and Melanie Moses. "Evolving Error Tolerance in Biologically-Inspired iAnt Robots." In European Conference on Artificial Life 2013. MIT Press, 2013. http://dx.doi.org/10.7551/978-0-262-31709-2-ch153.

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Reports on the topic "Biologically-inspired robots"

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Quinn, Roger, Roy Ritzmann, Stephen Phillips, Randall Beer, Steven Garverick, and Matthew Birch. Biologically-Inspired Micro-Robots. Volume 1. Robots Based on Crickets. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada434047.

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Beer, Randall D. A Biologically-Inspired Autonomous Robot. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada273909.

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Shim, Jaeeun, and Ronald C. Arkin. Biologically-Inspired Deceptive Behavior for a Robot. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada563086.

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Rego, Jocelyn, Edward Kim, and Garrett Kenyon. Biologically Inspired Robust Perception Approximating Sparse Coding. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1820067.

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