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Relevant bibliographies by topics / Biologically inspired robotics

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Academic literature on the topic 'Biologically inspired robotics'

Author: Grafiati

Published: 4 June 2021

Last updated: 31 January 2023

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Journal articles on the topic "Biologically inspired robotics"

1

Beer, Randall. "Biologically inspired robotics." Scholarpedia 4, no. 4 (2009): 1531. http://dx.doi.org/10.4249/scholarpedia.1531.

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Shi, Liwei, Maki K. Habib, Nan Xiao, and Huosheng Hu. "Biologically Inspired Robotics." Journal of Robotics 2015 (2015): 1–2. http://dx.doi.org/10.1155/2015/894394.

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Shi, Liwei, Maki Habib, Nan Xiao, and Huosheng Hu. "Biologically Inspired Robotics 2016." Journal of Robotics 2018 (August 1, 2018): 1–2. http://dx.doi.org/10.1155/2018/7514069.

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Beer, Randall D., Roger D. Quinn, Hillel J. Chiel, and Roy E. Ritzmann. "Biologically inspired approaches to robotics." Communications of the ACM 40, no. 3 (1997): 30–38. http://dx.doi.org/10.1145/245108.245118.

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Shi, Liwei, Yong Yu, Nan Xiao, and Dongming Gan. "Biologically Inspired and Rehabilitation Robotics." Applied Bionics and Biomechanics 2019 (March 13, 2019): 1–2. http://dx.doi.org/10.1155/2019/2428707.

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Shi, Liwei, Yong Yu, Nan Xiao, Dongming Gan, and Wei Wei. "Biologically Inspired and Rehabilitation Robotics 2020." Applied Bionics and Biomechanics 2022 (January 22, 2022): 1–2. http://dx.doi.org/10.1155/2022/9852938.

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Hu, Huosheng, Shugen Ma, and Hong Zhang. "Special issue on biologically inspired robotics." Advanced Robotics 28, no. 5 (2014): 269–70. http://dx.doi.org/10.1080/01691864.2014.885483.

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Fernández-Caballero, Antonio, and José Manuel Ferrández. "Biologically inspired vision systems in robotics." International Journal of Advanced Robotic Systems 14, no. 6 (2017): 172988141774594. http://dx.doi.org/10.1177/1729881417745947.

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Higgins, Charles M. "Sensory Architectures for Biologically Inspired Autonomous Robotics." Biological Bulletin 200, no. 2 (2001): 235–42. http://dx.doi.org/10.2307/1543322.

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Kim, S. H., S. Hashi, and K. Ishiyama. "Magnetic Robotics: A Biologically Inspired Walking Robot." Journal of the Magnetics Society of Japan 35, no. 2 (2011): 149–56. http://dx.doi.org/10.3379/msjmag.10r060zh.

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Dissertations / Theses on the topic "Biologically inspired robotics"

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Peng, Shiqi. "A biologically inspired four legged walking robot." Thesis, Peng, Shiqi (2006) A biologically inspired four legged walking robot. PhD thesis, Murdoch University, 2006. https://researchrepository.murdoch.edu.au/id/eprint/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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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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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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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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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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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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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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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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Chen, Zetao. "Biologically-inspired place recognition with neural networks." Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/98550/1/Zetao_Chen_Thesis.pdf.

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This thesis explores two aspects of biologically inspired methods for place recognition, a key component of navigation. The first key theme is to explore the multi-scale mapping principles inspired by the recent discovery of overlapping, multi-scale spatial maps in the rodent brain, while the second develops biologically inspired Convolutional Neural Networks (CNNs) for place recognition. We presented a series of studies comprehensively demonstrating for the first time how both a rodent brain-inspired multi-scale mapping system and CNN-based techniques enable state of the art place recognition performance.

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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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Books on the topic "Biologically inspired robotics"

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

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

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International Workshop on Biologically Inspired Robotics (2002 Bristol, England). Biologically inspired robotics: Papers of a theme issue. The Royal Society, 2003.

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King, Ralf Simon. BiLBIQ: A Biologically Inspired Robot with Walking and Rolling Locomotion. Springer Berlin Heidelberg, 2013.

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Chella, Antonio. Biologically Inspired Cognitive Architectures 2012: Proceedings of the Third Annual Meeting of the BICA Society. Springer Berlin Heidelberg, 2013.

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Machado, Penousal. Evolutionary and Biologically Inspired Music, Sound, Art and Design: Second International Conference, EvoMUSART 2013, Vienna, Austria, April 3-5, 2013. Proceedings. Springer Berlin Heidelberg, 2013.

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

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1966-, Arena Paolo, and International Centre for Mechanical Sciences., eds. Dynamical systems, wave-based computation and neuro-inspired robots. Springer, 2008.

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Book chapters on the topic "Biologically inspired robotics"

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Iida, Fumiya, and Auke Jan Ijspeert. "Biologically Inspired Robotics." In Springer Handbook of Robotics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32552-1_75.

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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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Yuta, Shin’ichi. "Biologically Inspired Approach to Autonomous Systems." In Robotics Research. Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-1580-9_42.

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Gupta, Saytandra K., Wojciech Bejgerowski, John Gerdes, et al. "An Engineering Approach to Utilizing Bio-Inspiration in Robotics Applications." In Biologically Inspired Design. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5248-4_10.

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Buessler, J. L., and J. P. Urban. "Modular Neural Architectures for Robotics." In Biologically Inspired Robot Behavior Engineering. Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-7908-1775-1_10.

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Hoffmann, Matej. "Biologically Inspired Robot Body Models and Self-Calibration." In Encyclopedia of Robotics. Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-642-41610-1_201-1.

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Caci, Barbara, Antonella D’Amico, and Giuseppe Chiazzese. "Robotics and Virtual Worlds: An Experiential Learning Lab." In Biologically Inspired Cognitive Architectures 2012. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34274-5_19.

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Yan, Lingyun, Lei Ren, and Guowu Wei. "A Biologically Inspired Lower Limb Cable Driven Exoskeleton." In Intelligent Robotics and Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13841-6_58.

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Massone, Lina L. E. "A Biologically-Inspired Architecture for Reactive Motor Control." In Neural Networks in Robotics. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3180-7_24.

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Kimura, Hiroshi, Yasuhiro Fukuoka, and Hiroyuki Nakamura. "Biologically Inspired Adaptive Dynamic Walking of the Quadruped on Irregular Terrain." In Robotics Research. Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0765-1_40.

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Conference papers on the topic "Biologically inspired robotics"

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Asbeck, Alan T., Robert J. Dyer, Arnar F. Larusson, and Conor J. Walsh. "Biologically-inspired soft exosuit." In 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR 2013). IEEE, 2013. http://dx.doi.org/10.1109/icorr.2013.6650455.

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H. Suhr, Steve, Yun Seong Song, Sang Jun Lee, and Metin Sitti. "Biologically Inspired Miniature Water Strider Robot." In Robotics: Science and Systems 2005. Robotics: Science and Systems Foundation, 2005. http://dx.doi.org/10.15607/rss.2005.i.042.

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Maufroy, Christophe, Hiroshi Kimura, and Kunikatsu Takase. "Biologically inspired neural controller for quadruped." In 2007 IEEE International Conference on Robotics and biomimetics (ROBIO). IEEE, 2007. http://dx.doi.org/10.1109/robio.2007.4522337.

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Weixing Lin, Rong Liu, Peter X. Liu, and Max Q. H. Meng. "Parameter estimation using biologically inspired methods." In 2007 IEEE International Conference on Robotics and biomimetics (ROBIO). IEEE, 2007. http://dx.doi.org/10.1109/robio.2007.4522358.

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Conn, Andrew T., and Jonathan Rossiter. "Antagonistic dielectric elastomer actuator for biologically-inspired robotics." In SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, edited by Yoseph Bar-Cohen and Federico Carpi. SPIE, 2011. http://dx.doi.org/10.1117/12.880438.

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Asif, U., and J. Iqbal. "Design and Simulation of a Biologically Inspired Hexapod Robot using SimMechanics." In Robotics. ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.703-044.

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Papauschek, Christian, and Michael Zillich. "Biologically inspired navigation on a mobile robot." In 2010 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2010. http://dx.doi.org/10.1109/robio.2010.5723380.

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Karpelson, Michael, Benjamin H. Waters, Benjamin Goldberg, et al. "A wirelessly powered, biologically inspired ambulatory microrobot." In 2014 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2014. http://dx.doi.org/10.1109/icra.2014.6907190.

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Barker, Steve, Luis A. Fuente, Khaled Hayatleh, Neil Fellows, Jochen J. Steil, and Nigel T. Crook. "Design of a biologically inspired humanoid neck." In 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2015. http://dx.doi.org/10.1109/robio.2015.7407034.

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Dai, Yu, Yuan Xue, Jianxun Zhang, and Jianmin Li. "Biologically-inspired auditory perception during robotic bone milling." In 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017. http://dx.doi.org/10.1109/icra.2017.7989132.

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Reports on the topic "Biologically inspired robotics"

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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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