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Dissertations / Theses on the topic 'Biologically-inspired robots'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Schubert, Oliver John. "Distributed control of a segmented and shape memory alloy actuated biologically inspired robot." Thesis, Montana State University, 2005. http://etd.lib.montana.edu/etd/2005/schubert/SchubertO0805.pdf.

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12

Dunker, Philip A. "A Biologically Inspired Robot for Lunar Exploration and Regolith Excavation." Case Western Reserve University School of Graduate Studies / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=case1219803272.

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13

Baisch, Andrew Thomas. "Design, Manufacturing, and Locomotion Studies of Ambulatory Micro-Robots." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:10907.

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Biological research over the past several decades has elucidated some of the mechanisms behind highly mobile, efficient, and robust locomotion in insects such as the cockroach. Roboticists have used this information to create biologically-inspired machines capable of running, jumping, and climbing robustly over a variety of terrains. To date, little work has been done to develop an at-scale insect-inspired robot capable of similar feats, due to limitations in fabrication, actuation, and electronics integration at small scales. This thesis addresses these challenges, focusing on the mechanical design and fabrication of a sub-2g walking robot, the Harvard Ambulatory MicroRobot (HAMR). The development of HAMR includes modeling and parameter selection for a two degree of freedom leg powertrain that enables locomotion. In addition, a design inspired by pop-up books that enables fast and repeatable assembly of the miniature walking robot is presented. Finally, a method to drive HAMR resulting in speeds up to 37cm/s is presented, along with simple control schemes.<br>Engineering and Applied Sciences

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14

Randall, Mark James. "Stable adaptive neural control systems with closed kinematic chains applied to biologically-inspired walking robots." Thesis, University of the West of England, Bristol, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.300916.

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15

Egerton, Simon John. "From mammals to machines : towards a biologically inspired spatial integration system for autonomous mobile robots." Thesis, University of Essex, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.413641.

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16

Mirletz, Brian Tietz. "Adaptive Central Pattern Generators for Control of Tensegrity Spines with Many Degrees of Freedom." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1438865567.

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17

Taylor, Brian Kyle. "Implementation and Benchmarking of a Whegs Robot in the USARSim Environment." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1215620284.

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18

Leibach, Ronald. "Development of a Tunable Compliance Energy Return Actuator." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1586543212369862.

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19

Daltorio, Kathryn A. "Obstacle Navigation Decision-Making: Modeling Insect Behavior for Robot Autonomy." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1365157897.

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20

Nassour, John [Verfasser], Rüdiger [Gutachter] Dillmann, Patrick [Gutachter] Henaff, Fred [Gutachter] Hamker, and Fred [Akademischer Betreuer] Hamker. "Biologically inspired action representation on humanoids with a perspective for soft wearable robots / John Nassour ; Gutachter: Rüdiger Dillmann, Patrick Henaff, Fred Hamker ; Betreuer: Fred Hamker." Chemnitz : Technische Universität Chemnitz, 2021. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa2-757871.

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21

Lewinger, William Anthony. "Neurobiologically-based Control System for an Adaptively Walking Hexapod." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1295655329.

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22

Holzbach, Andreas [Verfasser], Gordon [Akademischer Betreuer] [Gutachter] Cheng, and Aleš [Gutachter] Ude. "Enabling Scalable and Efficient Visual Attention, Object-Based Attention and Object Recognition for Humanoid Robots - a Biologically-Inspired Approach. / Andreas Holzbach. Betreuer: Gordon Cheng. Gutachter: Ales Ude ; Gordon Cheng." München : Universitätsbibliothek der TU München, 2016. http://d-nb.info/1104933667/34.

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23

au, shiqi peng@woodside com, and Shiqi Peng. "A Biologically Inspired Four Legged Walking Robot." Murdoch University, 2006. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20070115.113710.

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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 robots 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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24

Armour, Rhodri H. "A biologically inspired jumping and rolling robot." Thesis, University of Bath, 2010. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.518126.

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Mobile robots for rough terrain are of interest to researchers as their range of possible uses is large, including exploration activities for inhospitable areas on Earth and on other planets and bodies in the solar system, searching in disaster sites for survivors, and performing surveillance for military applications. Nature generally achieves land movement by walking using legs, but additional modes such as climbing, jumping and rolling are all produced from legs as well. Robotics tends not to use this integrated approach and adds additional mechanisms to achieve additional movements. The spherical device described within this thesis, called Jollbot, integrated a rolling motion for faster movement over smoother terrain, with a jumping movement for rougher environments. Jollbot was developed over three prototypes. The first achieved pause-and-leap style jumps by slowly storing strain energy within the metal elements of a spherical structure using an internal mechanism to deform the sphere. A jump was produced when this stored energy was rapidly released. The second prototype achieved greater jump heights using a similar structure, and added direction control to each jump by moving its centre of gravity around the polar axis of the sphere. The final prototype successfully combined rolling (at a speed of 0.7 m/s, up 4° slopes, and over 44 mm obstacles) and jumping (0.5 m cleared height), both with direction control, using a 0.6 m spherical spring steel structure. Rolling was achieved by moving the centre of gravity outside of the sphere’s contact area with the ground. Jumping was achieved by deflecting the sphere in a similar method to the first and second prototypes, but through a larger percentage deflection. An evaluation of existing rough terrain robots is made possible through the development of a five-step scoring system that produces a single numerical performance score. The system is used to evaluate the performance of Jollbot.

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Livingston, Nicholas B. "AN EXPLORATION OF BIOLOGICALLY-INSPIRED ROBOT INTELLIGENCE." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1189180311.

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Nichols, Eric James. "A biologically inspired neural network for robot navigation." Thesis, Ulster University, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.540224.

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This research implements two spiking neural network (SNN) based robot navigation systems inspired by the sensory fusion and control structures of biological systems. The work builds on an in-depth review of robotic and SNN systems. The robotic review points to perception, cognition and reasoning as intelligent attributes that are lacking in modern robotic systems but are performed effortlessly by the nervous system within biologic organisms. The SNN review focusses on models of synapses and neurons and the associated architectures that use long and short term plasticity rules as mechanisms for learning. This review also focusses on self-organisation and there is a brief description of the peripheral and central nervous systems. Two self-organising SNN architectures are presented and their performances are verified on wall following tasks experimentally. Input is obtained from infrared sensors in the first SNN. Structured learning maps the input to appropriate output by self-organising the SNN architecture as the robot experiences novel environments. Information is routed through the SNN with biological inspiration from synapses with short term plasticity. Working memory is implemented by fusing prior and current conditions to provide a richer sense of the environment. Learning occurs online in a supervised manner using hand-crafted rules. The second SNN fuses inputs from laser and sonar sensors. The self-organising structure from the first SNN is maintained and the biological precision of short term plasticity is increased using a facilitating synaptic model at every synapse until the final layer where depressing synapses are used. Long-term synaptic plasticity is implemented online using the temporal difference learning rule to enable the robot to learn to associate the correct movement with the appropriate input conditions. Results of experiments on each SNN are presented and conclusions are drawn. vii

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Fisher, Paul Conway. "Biologically inspired robotic search strategies in chemical fields." Thesis, University of Portsmouth, 2008. https://researchportal.port.ac.uk/portal/en/theses/biologically-inspired-robotic-search-strategies-in-chemical-fields(918d7741-f414-443e-8cea-91177fccb5aa).html.

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28

Li, Wei. "Biologically Inspired Neural Control Network for A Bipedal Walking Model." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1481161796893903.

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Adams, Samantha. "The development of bio-inspired cortical feature maps for robot sensorimotor controllers." Thesis, University of Plymouth, 2013. http://hdl.handle.net/10026.1/1464.

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This project applies principles from the field of Computational Neuroscience to Robotics research, in particular to develop systems inspired by how nature manages to solve sensorimotor coordination tasks. The overall aim has been to build a self-organising sensorimotor system using biologically inspired techniques based upon human cortical development which can in the future be implemented in neuromorphic hardware. This can then deliver the benefits of low power consumption and real time operation but with flexible learning onboard autonomous robots. A core principle is the Self-Organising Feature Map which is based upon the theory of how 2D maps develop in real cortex to represent complex information from the environment. A framework for developing feature maps for both motor and visual directional selectivity representing eight different directions of motion is described as well as how they can be coupled together to make a basic visuomotor system. In contrast to many previous works which use artificially generated visual inputs (for example, image sequences of oriented moving bars or mathematically generated Gaussian bars) a novel feature of the current work is that the visual input is generated by a DVS 128 silicon retina camera which is a neuromorphic device and produces spike events in a frame-free way. One of the main contributions of this work has been to develop a method of autonomous regulation of the map development process which adapts the learning dependent upon input activity. The main results show that distinct directionally selective maps for both the motor and visual modalities are produced under a range of experimental scenarios. The adaptive learning process successfully controls the rate of learning in both motor and visual map development and is used to indicate when sufficient patterns have been presented, thus avoiding the need to define in advance the quantity and range of training data. The coupling training experiments show that the visual input learns to modulate the original motor map response, creating a new visual-motor topological map.

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30

Moffat, Shannon Marija. "Biologically Inspired Legs and Novel Flow Control Valve Toward a New Approach for Accessible Wearable Robotics." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-theses/1279.

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The Humanoid Walking Robot (HWR) is a research platform for the study of legged and wearable robots actuated with Hydro Muscles. The fluid operated HWR is representative of a class of biologically inspired, and in some aspects highly biomimetic robotic musculoskeletal appendages showing certain advantages in comparison to more conventional artificial limbs and braces for physical therapy/rehabilitation, assistance of daily living, and augmentation. The HWR closely mimics the human body structure and function, including the skeleton, ligaments, tendons, and muscles. The HWR can emulate close to human-like movements even when subjected to simplified control laws. One of the main drawbacks of this approach is the inaccessibility of an appropriate fluid flow management support system, in the form of affordable, lightweight, compact, and good quality valves suitable for robotics applications. To resolve this shortcoming, the Compact Robotic Flow Control Valve (CRFC Valve) is introduced and successfully proof-of-concept tested. The HWR added with the CRFC Valve has potential to be a highly energy efficient, lightweight, controllable, affordable, and customizable solution that can resolve single muscle action.

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Hunt, Alexander. "A Biologically Inspired Robot for Assistance in Urban Search and Rescue." Cleveland, Ohio : Case Western Reserve University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1270137669.

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Thesis (Master of Sciences (Engineering))--Case Western Reserve University, 2010<br>Department of EMC - Mechanical Engineering Title from PDF (viewed on 2010-05-25) Includes abstract Includes bibliographical references and appendices Available online via the OhioLINK ETD Center

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Isava, Monica. "Exploring the timescale limitations of RoboClam : a biologically inspired burrowing robot." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/83721.

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Thesis (S.B.)--Massachusetts Institute of Technology, Department of Mechanical Engineering, 2013.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (page 27).<br>The Atlantic razor clam (Ensis directus) burrows into soil by contracting its valves in a pattern that fluidizes the particles around it. In this way, it uses an order of magnitude less energy to dig to its burrowing depth than would be expected if it were moving through static soil. This technology is a mechanically simple solution to reduce energy requirements in applications such as anchoring and underwater pipe installation. RoboClam is a robot that imitates the movements of Ensis and has achieved localized fluidization in environments similar to that of the animal. This paper tests the theoretical timescale limits for running RoboClam while still achieving the soil fluidization that Ensis achieves. Needle valves were used on the robot's pneumatic control system to vary its expansion and contraction times in a series of tests, then each test was analyzed to determine to what extent soil fluidization occurred. It was found that the theoretical minimum contraction time is an appropriate boundary and the theoretical maximum contraction time is a loose boundary on tests that will result in soil fluidization. However, these conclusions came from a limited number of tests, so further testing is necessary to confirm these results.<br>by Monica Isava.<br>S.B.

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Woodward, Matthew A. "MultiMo-Bat: Biologically Inspired Integrated Multi-Modal Locomotion." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1093.

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The combination or integration of locomotion modes, is analyzed through the design, development, and verification of a miniature integrated jumping and gliding robot, the MultiMo-Bat, which is inspired by the locomotion strategies of vampire bats, locusts, and pelicans. This robot has a mass of between 100 and 162 grams and exhibits high jumping and gliding performance, reaching heights of over 4.5 meters, to overcome obstacles in the environment. Integration results in a smaller, lighter robot with high cooperation between the modes. This thesis presents a previously unstudied robot design concept and highlights the understudied evolutionary concept within organism mobility of integration of locomotion modes. High performance locomotion modes also require high energy density actuators. To this end, a design methodology is developed for tailoring magnetic springs to the characteristics of shape memory alloy-actuated mechanisms, which allow the MultiMo-Bat to reach jumping heights of 3.5 m with active wing deployment and full controller. Through a combinations of permanent magnets, a magnetic spring can be customized to desired characteristics; theoretically any welldefined function of force vs. displacement can be created. The methodology is not limited to SMA but can be adapted to any smart actuator, joint, or situation which requires a fixed complex force-displacement relationship with extension other interactions and magnetic field design. Robotic locomotion is also much more idealized than that of their biological counter parts. This thesis serves to highlight just how non-ideal, yet robust, biological locomotion can inspire concepts for enhancing the robustness of robot locomotion. We studied the desert locust (Schistocerca gregaria), which is adapted for jumping at the extreme limits of its surface friction, as evident by its morphological adaptations for not only jumping, but slipping. Analysis of both foot morphology and jumping behavior are used to understand how the feet interact with different surfaces, including hydrophobic glass, hydrophilic glass, wood, sandstone, and mesh. The results demonstrate a complex interplay of embodied mechanical intelligence, allowing the foot to interact and adapt passively to different surfaces without burdening the organism with additional tasks. The key morphological and dynamical features are extracted to create a concept for developing multi-Surface Locust Inspired Passively-adaptable (SLIP) feet. A simple interpretation of the concepts are then used to construct a SLIP foot for the MultiMo-Bat. These feet allow the MultiMo-Bat to reach jumping heights of well over 4 m, greater than any other electrically powered robot, and this is achieved on a 45 degree angled surface while slipping. The SLIP foot concept can be directly applied to a wide range of robot size scales, thus enhancing their dynamic terrestrial locomotion on variable surfaces.

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Espenschied, Kenneth Scot. "Biologically-inspired control of an insect-like hexapod robot on rough terrain." Case Western Reserve University School of Graduate Studies / OhioLINK, 1994. http://rave.ohiolink.edu/etdc/view?acc_num=case1061220984.

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Guitron, Steven Paul. "Parameters that affect the digging of a biologically-inspired underwater borrowing robot." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/98962.

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Thesis: S.B., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (page 29).<br>RoboClam 2 is a device that burrows based on the movement of the Atlantic razor clam. A functional RoboClam 2 has been built. Testing was conducted in a controlled laboratory environment to determine what parameters of the device and its operation affect its ability to dig both speedily, deeply, and efficiently. Smaller contraction and dilation volume, heavier device weight, and longer contractions above a theoretically calculated minimum fluidizing velocity were all found to correlate with faster digging speed. Future work will involve experimentally determining the minimum fluidizing velocity and the effect of contraction speed on digging ability.<br>by Steven Paul Guitron.<br>S.B.

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Dorsch, Daniel Scott. "Design of a biologically-inspired underwater burrowing robot with on-board actuation." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/97849.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 67-68).<br>The Atlantic razor clam (Ensis directus) burrows by contracting its valves, fluidizing the surrounding soil and reducing burrowing drag. Moving through a fluidized, rather than static, soil requires energy that scales linearly with depth, rather than depth squared. In addition to providing an advantage for the animal, localized fluidization may provide significant value to engineering applications such as vehicle anchoring and underwater pipe installation. This thesis presents the design of RoboClam 2, a self-actuated, radially expanding burrowing mechanism that utilizes E. directus burrowing methods. The device is sized to be a platform for an anchoring system for autonomous underwater vehicles. The scaling relationships necessary for the creation of this internally actuated burrowing robot are presented. These relationships allow for designing devices of different sizes for other applications, and describe optimal sizing and power needs for various size subsea burrowing systems. RoboClam 2 is a proof of concept iteration of a digging mechanism that utilizes localized fluidization. It will be used for testing digging parameters in a laboratory setting and validating the theory presented.<br>by Daniel S. Dorsch.<br>S.M.

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Price, Aaron David. "Biologically inspired dexterous robot hand actuated by smart material based artificial muscles." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27409.

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Modern externally powered upper-body prostheses are conventionally actuated by electric servomotors. Although these motors achieve reasonable kinematic performance, they are voluminous and heavy. Deterring factors such as these lead to a substantial proportion of upper extremity amputees avoiding the use of their prostheses. Therefore, it is apparent that there exists a need for functional prosthetic devices that are compact and light-weight. The realization of such a device requires an alternative actuation technology, and biological inspiration suggests that tendon based systems are advantageous. Shape memory alloys are a type of smart material that exhibit an actuation mechanism resembling the biological equivalent. As such, shape memory alloy enabled devices promise to be of major importance in the future of dexterous robotics, and to prosthetics in particular. This thesis investigates the issues surrounding the practical application of shape memory alloys as artificial muscles in a three fingered robot hand. First the function of the human hand and the kinematic requirements for manipulation are reviewed. An overview of artificial hands is provided, followed by a discussion on shape memory alloys focused on the unique phenomena of the shape memory effect. Second, the forward and inverse kinematics of the artificial finger are established in order to relate the desired finger tip contact point to the required joint angles. This is followed by the design of the requisite instrumentation and control systems. Due to the highly nonlinear nature of both the SMA and the robot hand, alternative control approaches such as neural networks are reviewed. Finally, a large-strain SMA actuator is proposed and the concepts explored herein are applied to the design, manufacture, and evaluation of an SMA actuated robotic hand.

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Lock, Richard J. "A biologically-inspired multi-modal wing for aerial-aquatic robotic vehicles." Thesis, University of Bristol, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.556709.

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The majority of robotic vehicles that can be found in the engineering community today are bound to operations within a single medium. This dissertation begins to lay the foundation for a generation of vehicles capable of multi-modal locomotion, allowing ambulatory abilities in more than one media, specifically focussing on a vehicle with both aerial and aquatic modes of locomotion. The flapping mechanism under investigation is used in both air and water, driving a wing capable of morphing its shape dependant on the medium of operation. Through a combi- nation of numerical analysis along with empirical analysis (gathered using a purpose built mechatronic system), the feasibility of using the dual-use mechanism has been demon- strated, along with quantifying projected performance. A detailed numerical model of the morphing wing supporting the development of the multi- modal vehicle has been formulated that combines blade element analysis (BEA), which models the hydrodynamics, along with additional inertial dynamics during the aquatic phase. The aerial phase of the model was developed based on established formuli relating to flapping aerial flight. The initial numerical model demonstrated the importance of the ability to operate with under-constrained kinematics in order to provide the desired highly manoeuvrable platform. Referring to the developed specification, the numerical model showed that in order to be able to achieve the vehicles minimum power velocity, i.e. enabling loitering capabilities, the wing semi-span would need to be ~ 0.35m and the chord would need to be ~ 0.08m. The mechatronic testing platform, consisting of a 2 degree-of-freedom (dof') flapping mech- anism, demonstrated that the required power to flap the retracted foil in water greatly reduces compared to the extended, with an observed reduction of ~ 75% in required me- chanical power into the system. Through standardisation of the results by means of the non-dimensionalised St number, the observed performance measures of the propulsive ef- ficiency and thrust coefficient have been shown to be similar for both the extended and retracted foils. Mechanical propulsive efficiencies of 0.65 were found for both foil shapes (i.e. outstretched ana' retracted) provided parameters were standardised during compar- isons, specifically the Strouhal number and the effective angle of attack, demonstrating the feasibility of using a retracted foil on future robotic vehicles using a flapping wing in both aerial and aquatic substrates.

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Sachinis, Michael 1977. "The design and testing of a biologically inspired underwater robotic mechanism." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/89282.

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Wei, Terence E. "A Robot Designed for Walking and Climbing Based on Abstracted Cockroach Locomotion Mechanisms." Case Western Reserve University School of Graduate Studies / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=case1131722937.

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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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Kingsley, Daniel A. "A COCKROACH INSPIRED ROBOT WITH ARTIFICIAL MUSCLES." Case Western Reserve University School of Graduate Studies / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=case1094932214.

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Spiers, Adam James. "Robust and intelligent control approaches for biologically inspired motion generation with an anthropomorphic robot arm." Thesis, University of Bristol, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.540872.

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Sedlackova, Anna. "Replicating Motion Vision and Response in Insects Using a Synthetic Nervous System." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1593309220545937.

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McMillen, David. "Effects of spike-frequency adaptation on neural models, with applications to biologically inspired robotics." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape2/PQDD_0020/NQ53651.pdf.

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Mehringer, Anna G. "FabricWorm: A Biologically-Inspired Robot That Demonstrates Structural Advantages of a Soft Exterior for Peristaltic Locomotion." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1493900162956628.

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Peniak, Martin. "GPU computing for cognitive robotics." Thesis, University of Plymouth, 2014. http://hdl.handle.net/10026.1/3052.

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This thesis presents the first investigation of the impact of GPU computing on cognitive robotics by providing a series of novel experiments in the area of action and language acquisition in humanoid robots and computer vision. Cognitive robotics is concerned with endowing robots with high-level cognitive capabilities to enable the achievement of complex goals in complex environments. Reaching the ultimate goal of developing cognitive robots will require tremendous amounts of computational power, which was until recently provided mostly by standard CPU processors. CPU cores are optimised for serial code execution at the expense of parallel execution, which renders them relatively inefficient when it comes to high-performance computing applications. The ever-increasing market demand for high-performance, real-time 3D graphics has evolved the GPU into a highly parallel, multithreaded, many-core processor extraordinary computational power and very high memory bandwidth. These vast computational resources of modern GPUs can now be used by the most of the cognitive robotics models as they tend to be inherently parallel. Various interesting and insightful cognitive models were developed and addressed important scientific questions concerning action-language acquisition and computer vision. While they have provided us with important scientific insights, their complexity and application has not improved much over the last years. The experimental tasks as well as the scale of these models are often minimised to avoid excessive training times that grow exponentially with the number of neurons and the training data. This impedes further progress and development of complex neurocontrollers that would be able to take the cognitive robotics research a step closer to reaching the ultimate goal of creating intelligent machines. This thesis presents several cases where the application of the GPU computing on cognitive robotics algorithms resulted in the development of large-scale neurocontrollers of previously unseen complexity enabling the conducting of the novel experiments described herein.

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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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Kent, Benjamin A. "Biologically Inspired Control Mechanisms with Application to Anthropomorphic Control of Myoelectric Upper-Limb Prostheses." University of Akron / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=akron1505091317897427.

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Rutkowski, Adam J. "A BIOLOGICALLY-INSPIRED SENSOR FUSION APPROACH TO TRACKING A WIND-BORNE ODOR IN THREE DIMENSIONS." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1196447143.

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