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Journal articles on the topic 'Robot dog'

Author: Grafiati

Published: 2 September 2021

Last updated: 2 February 2022

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1

Adachi, Yoshinobu, and Masayoshi Kakikura. "Research on the Sheepdog Problem Using Cellular Automata." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 9 (November 20, 2007): 1099–106. http://dx.doi.org/10.20965/jaciii.2007.p1099.

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The simulation framework we propose for complex path planning problems with multiagent systems focuses on the sheepdog problem for handling distributed autonomous robot systems – an extension of the pursuit problem for handling one prey robot and multiple predator robot. The sheepdog problem involves a more complex issue in which multiple dog robot chase and herd multiple sheep robot. We use the Boids model and cellular automata to model sheep flocking and chase and herd behavior for dog robots. We conduct experiments using a Sheepdog problem simulator and study cooperative behavior.

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2

Morooka, Yukio, and Ikuo Mizuuchi. "Gravity Compensation Modular Robot: Proposal and Prototyping." Journal of Robotics and Mechatronics 31, no. 5 (October 20, 2019): 697–706. http://dx.doi.org/10.20965/jrm.2019.p0697.

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If a robot system can take various shapes, then it can play various roles, such as humanoid, dog robot, and robot arm. A modular robot is a robot system in which robots are configured using multiple modules, and it is possible to configure robots of other shapes by varying the combinations of the modules. In conventional modular robots, the shape is restricted by gravity, and configurable shapes are limited. In this study, we propose a gravity compensation modular robot to solve this problem. This paper describes the design and prototyping of the gravity compensation modular robot, and provides examples of robot shapes configured using the gravity compensation modules and motion experiments of the robots. In the experiments, there were motions that the robots could perform and could not perform. We considered the lack in the gravity compensation level and module rigidity as the main factor of the failures. This paper also discusses the solutions to these problems.

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Doi, Toshi T. "Dog-oid Robot AIBO and New Robot Industry." Journal of the Robotics Society of Japan 30, no. 10 (2012): 1000–1001. http://dx.doi.org/10.7210/jrsj.30.1000.

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4

Huynh, Phu Duc, and Tuong Quan Vo. "An application of genetic algorithm to optimize the 3-Joint carangiform fish robot’ s links to get the desired straight velocity." Science and Technology Development Journal 18, no. 1 (March 31, 2015): 27–36. http://dx.doi.org/10.32508/stdj.v18i1.920.

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Biomimetic robot is a new branch of researched field which is developing quickly in recent years. Some of the popular biomimetic robots are fish robot, snake robot, dog robot, dragonfly robot, etc. Among the biomimetic underwater robots, fish robot and snake robot are mostly concerned. In this paper, we study about an optimization method to find the design parameters of fish robot. First, we analyze the dynamic model of the 3-joint Carangiform fish robot by using Lagrange method. Then the Genetic Algorithm (GA) is used to find the optimal lengths’ values of fish robot’s links. The constraint of this optimization problem is that the values of fish robot’s links are chosen that they can make fish robot swim with the desired straight velocity. Finally, some simulation results are presented to prove the effectiveness of the proposed method

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Tholley, Ibrahim S., Qing Gang Meng, and Paul W. H. Chung. "Robot Dancing: What Makes a Dance?" Advanced Materials Research 403-408 (November 2011): 4901–9. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.4901.

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In this paper, we investigate the mechanics of dance for humans that can be applied to robots, in an attempt to make dancing robots learn the fundamentals of dance, and improve their dancing. We provide a conceptual definition of ‘dance’ and ‘movement’ to make robot dancers form their own movements to music. We used a virtual robot dog to experiment on our conceptual definitions, and human subjects to give their feedback on the robot’s dancing. Experimental results show that the robot learns (using reinforcement learning) our conceptual definition of ‘dance’ and that a dance that has structure and fundamental joint movements, improves the dancing.

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Wang, Jun, Chuan Hu, and You Tong Zhang. "Energy Saving Matching of Power Equipment for Four-Footed Robot Dog." Advanced Materials Research 986-987 (July 2014): 1222–25. http://dx.doi.org/10.4028/www.scientific.net/amr.986-987.1222.

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To increase the flexibility performance of robot dog, dynamic performance matching of power equipment in four-footed robot dog is made. Demand for power and configure in robot dog power equipment is briefly analyzed, improved backward power match between gasoline and pump is presented, power and flow rate match between gasoline and pump is calculated, control strategy based on pump pressure feedback is applied into gasoline engine control in order to save energy. With experimental bench test, experimental results showed that energy saving matching of power equipment can meet the requirement of fast dynamic meditation for robot dog .

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Wang, Jun, Lian He, and You Tong Zhang. "Power Equipment Control in Four-Footed Bearing-Burden Robot Dog." Applied Mechanics and Materials 513-517 (February 2014): 3328–31. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3328.

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To meet the control demand of power equipment in robot dog, development on control system for robot dog is made. Based on the brief analysis of demand in power and configure in power assembly of robot dog, whole design sketch of control system for power is presented, electronic control system based on MC9S12DP512 microprocessor and neural control algorithm for network structure PID is found. Control algorithm and control effect of control system is verified in experimental bench, experimental results showed that designed control system control effect is stable and it can meet the design requirement in transient speed adjusting and stable speed fixing.

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8

Han, Congcong, Ming Yu, Hengyuan Pan, and Jinhao Yu. "About the design of a new bionic robot with four legs for competition." MATEC Web of Conferences 336 (2021): 03003. http://dx.doi.org/10.1051/matecconf/202133603003.

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In order to solve the stable walking and tracking of the bionic mammalian four-legged mechanical dog, the paper introduces the mechanical structure and robot control, adopts the tandem rod structure and the new vision processing algorithm, and realizes the stable movement of the robot. The recognition and processing technology of track information image is improved, so that the four-legged bionic robot moves forward accurately along the given track, and the tracking function of the four-legged bionic mechanical dog is realized.

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9

Doi, Satoshi, and Manabu KOSAKA. "1403 Locomotion control for guide dog robot." Proceedings of Conference of Kansai Branch 2007.82 (2007): _14–3_. http://dx.doi.org/10.1299/jsmekansai.2007.82._14-3_.

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10

Sun, Qun, Chong Wang, Dongjie Zhao, and Cuihua Zhang. "Cam Drive Step Mechanism of a Quadruped Robot." Journal of Robotics 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/851680.

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Bionic quadruped robots received considerable worldwide research attention. For a quadruped robot walking with steady paces on a flat terrain, using a cam drive control mechanism instead of servomotors provides theoretical and practical benefits as it reduces the system weight, cost, and control complexities; thus it may be more cost beneficial for some recreational or household applications. This study explores the robot step mechanism including the leg and cam drive control systems based on studying the bone structure and the kinematic step sequences of dog. The design requirements for the cam drive robot legs have been raised, and the mechanical principles of the leg operating mechanism as well as the control parameters have been analyzed. A cam drive control system was constructed using three cams to control each leg. Finally, a four-leg demo robot was manufactured for experiments and it showed stable walking patterns on a flat floor.

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11

Faragó, Tamás, Márta Gácsi, Beáta Korcsok, and Ádám Miklósi. "Why is a dog-behaviour-inspired social robot not a doggy-robot?" Interaction Studies 15, no. 2 (August 20, 2014): 224–32. http://dx.doi.org/10.1075/is.15.2.11far.

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12

Sinatra, Anne M., Valerie K. Sims, Matthew G. Chin, and Heather C. Lum. "If it looks like a dog." Interaction Studies 13, no. 2 (May 7, 2012): 235–62. http://dx.doi.org/10.1075/is.13.2.04sin.

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This study was designed to compare the natural free form communication that takes place when a person interacts with robotic entities versus live animals. One hundred and eleven participants interacted with one of four entities: an AIBO robotic dog, Legobot, Dog or Cat. It was found that participants tended to rate the Dog as more capable than the other entities, and often spoke to it more than the robotic entities. However, participants were not positively biased toward live entities, as the Cat often was thought of and spoken to similarly to the AIBO robot. Results are consistent with a model in which both appearance and interactivity lead to the development of beliefs about a live or robotic entity in an interaction. Keywords: Human-robot interaction; human-animal interaction; AIBO; free form communication; attributions; human-entity interaction

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13

JIANG, Ming. "Mechanism Architecture of Hybrid Serial-parallel Robot Dog." Journal of Mechanical Engineering 48, no. 01 (2012): 19. http://dx.doi.org/10.3901/jme.2012.01.019.

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14

Rutkin, Aviva. "Robot sniffer dog goes hunting for gas leaks." New Scientist 222, no. 2972 (June 2014): 21. http://dx.doi.org/10.1016/s0262-4079(14)61104-0.

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15

Al-Raziqi, Ali, Mahesh Venkata Krishna, and J. Denzler. "Detection of dog-robot interactions in video sequences." Pattern Recognition and Image Analysis 26, no. 1 (January 2016): 45–53. http://dx.doi.org/10.1134/s1054661816010028.

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16

Tachi, Susumu, Kazuo Tanie, Kiyoshi Komoriya, and Minoru Abe. "Electrocutaneous Communication in a Guide Dog Robot (MELDOG)." IEEE Transactions on Biomedical Engineering BME-32, no. 7 (July 1985): 461–69. http://dx.doi.org/10.1109/tbme.1985.325561.

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17

Tang, Zhao, Peng Qi, and Jian Dai. "Mechanism design of a biomimetic quadruped robot." Industrial Robot: An International Journal 44, no. 4 (June 19, 2017): 512–20. http://dx.doi.org/10.1108/ir-11-2016-0310.

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Purpose This paper aims to introduce a novel design of the biomimetic quadruped robot, including its body structure, three structural modes and respective workspace. Design/methodology/approach By taking a metamorphic 8-bar linkage as the body of a quadruped robot, the authors propose a reconfigurable walking robot that can imitate three kinds of animals: mammals (e.g. dog), arthropods (e.g. stick insect) and reptiles (e.g. lizard). Furthermore, to analyze the three structural modes of this quadruped robot, the workspace is calculated and studied. Findings Based on experimental data analyses, it is revealed that the metamorphic quadruped robot can walk in all its three structural modes and adapt to different terrains. Research limitations/implications Because the body of the quadruped robot is deformable and reconfigurable, the location of payload is not considered in the current stage. Practical implications The relative positions and postures of legs of the metamorphic robot can be rearranged during its body reconfiguration in such a way to combine all the features of locomotion of the three kinds of animals into one robot. So, the metamorphic quadruped robot is capable of maintaining wider stability margins than conventional rigid-body quadruped robots and conducting operations in different environments, particularly the extreme and restricted occasions due to the changeable and adaptable trunk. Originality/value The main contribution is the development of a reconfigurable biomimetic quadruped robot, which uses the metamorphic 8-bar linkage. This robot can easily reshape to three different structural modes and mimic the walking patterns of all mammals, arthropods and reptiles.

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18

황근영, 윤현달, and KIM GUN IN. "A Sensitivity Analysis of Dog-Horse Robot Using AWAM." Korean Journal of Military Art and Science 67, no. 2 (August 2011): 345–58. http://dx.doi.org/10.31066/kjmas.2011.67.2.014.

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19

Sparkes, Matthew. "Robot guide dog could help people who are blind." New Scientist 250, no. 3329 (April 2021): 17. http://dx.doi.org/10.1016/s0262-4079(21)00596-0.

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20

Fukuoka, Y., and H. Kimura. "Dynamic Locomotion of a Biomorphic Quadruped ‘Tekken’ Robot Using Various Gaits: Walk, Trot, Free-Gait and Bound." Applied Bionics and Biomechanics 6, no. 1 (2009): 63–71. http://dx.doi.org/10.1155/2009/743713.

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Numerous quadruped walking and running robots have been developed to date. Each robot walks by means of a crawl, walk, trot or pace gait, or runs by means of a bound and/or gallop gait. However, it is very difficult to design a single robot that can both walk and run because of problems related to mechanisms and control. In response to this, we adapted a biological control method for legged locomotion in order to develop a dog-like quadruped robot we have named ‘Tekken’. Tekken has a control system that incorporates central pattern generators, reflexes and responses as well as a mechanism that makes the most of the control system. Tekken, which is equipped with a single mechanism, an unchangeable control method, and modifiable parameters, is capable of achieving walking and trotting on flat terrain, can walk using a free gait on irregular terrain, and is capable of running on flat terrain using a bounding gait. In this paper, we describe the mechanism, the control method and the experimental results of our new development.

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21

Zamansky, Anna, Stephane Bleuer-Elsner, Sylvia Masson, Shir Amir, and Ofer Magen. "Effects of anxiety on canine movement in dog-robot interactions." Animal Behavior and Cognition 5, no. 4 (November 1, 2018): 380–87. http://dx.doi.org/10.26451/abc.05.04.05.2018.

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22

TAKEZAWA, Hayato, and Hideo NAKAGAWA. "S151013 Door opening operation of service dog robot moving room." Proceedings of Mechanical Engineering Congress, Japan 2011 (2011): _S151013–1—_S151013–3. http://dx.doi.org/10.1299/jsmemecj.2011._s151013-1.

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23

Henkel, Zachary, Kenna Baugus, Cindy L. Bethel, and David C. May. "User expectations of privacy in robot assisted therapy." Paladyn, Journal of Behavioral Robotics 10, no. 1 (March 26, 2019): 140–59. http://dx.doi.org/10.1515/pjbr-2019-0010.

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AbstractThis article describes ethical issues related to the design and use of social robots in sensitive contexts like psychological interventions and provides insights from one user design study and two controlled experiments with adults and children. User expectations regarding privacy with a therapeutic robotic dog, Therabot, gathered from a 16 participant design study are presented. Furthermore, results from 142 forensic interviews about bullying experiences conducted with children (ages 8 to 17) using three different social robots (Nao, Female RoboKind, Male RoboKind) and humans (female and male) as forensic interviewers are examined to provide insights into child beliefs about privacy and social judgment in sensitive interactions with social robots. The data collected indicates that adult participants felt a therapeutic robotic dog would be most useful for children in comparison to other age groups, and should include privacy safeguards. Data obtained from children after a forensic interview about their bullying experiences shows that they perceive social robots as providing significantly more socially protective factors than adult humans. These findings provide insight into how children perceive social robots and illustrate the need for careful considerationwhen designing social robots that will be used in sensitive contexts with vulnerable users like children.

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24

Jacobs, Trent. "What To Expect When You’re Expecting Robots." Journal of Petroleum Technology 73, no. 08 (August 1, 2021): 22–29. http://dx.doi.org/10.2118/0821-0022-jpt.

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The market turmoil of 2020 left the upstream industry with diminished ranks, palpable concerns over long-term demand, and mounting pressure to reduce its carbon footprint. This made for what many consider a bullish case, as JPT has reported, for robotics uptake over the course of the decade. But there are reasons to temper expectations. After all, this is the oil and gas industry. The upstream land-scape is as vast as it is specialized. Each silo is a fortress of status quo to which robot developers must dedicate significant time and fortune in conquering. Some are worth the battle, especially in the offshore arena where factors of cost and safety have made this the most active corner of oil and gas robotics. Many other use cases may be worth bypassing. About 70% of the world’s oil and gas supply is produced onshore which, of course, is much more accessible to human operators. That means a robot dog in the Permian Basin has to jump over a much higher bar in order to create value than a robot dog tasked with inspecting a platform in the middle of the Norwegian Sea. Speaking of inspection, this is both the chief strength and upper limit for much of the current generation of robots. The next generation will be asked to fix things. And whatever they can’t do - or are just not the right tool for - look for it to be covered by industrial automation. As a new class of oil and gas robots finds its niche, and fights for investment dollars along the way, here are a few developments to track and points to consider. This Time It’s Different, Right Boss? The upstream sector pulled back from exploring the frontier of robotics and drone technologies in the last decade relative to other industries, but it is now being pulled forward by societal and technological shifts, according to Ed Tovar who runs an Austin-based consulting company, InTechSys, that serves the defense and energy industry.

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25

Noda, Shintaro, Fumihito Sugai, Kunio Kojima, Kim-Ngoc-Khanh Nguyen, Yohei Kakiuchi, Kei Okada, and Masayuki Inaba. "Semi-Passive Walk and Active Walk by One Bipedal Robot: Mechanism, Control and Parameter Identification." International Journal of Humanoid Robotics 17, no. 02 (February 18, 2020): 2050012. http://dx.doi.org/10.1142/s0219843620500127.

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We developed a bipedal robot equipped with brake and clutch mechanisms to change the number of active and passive joints, thereby enabling various types of movements including normal active walking using 12-dof joints, under-actuated walking using brake, and passive-based walking using clutch and passive joints. In this paper, we describe three technologies to achieve the proposed system and show experimental results on active and semi-passive walking. The first technology comprises a small and high-strength clutch mechanism to sustain the massive weight of life-sized robots using actuators for joint and dog clutch control. The second technology comprises a walking controller using a simulation-based optimization technique to consider passive joint dynamics instead of depending on the inverse kinematics problem, thereby enabling the control of the under-actuated leg. The last technology is model parameter identification to achieve unstable passive-based walking in real-world considering the body as well as environmental parameters such as ground slope. To the best of our knowledge, the proposed robot is the first to achieve both active and passive-based walking using a bipedal body. This enables the implementation of the passive-walking technology to active-joint robots and expands the application possibility of passive joint for bipedal robots.

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MATSUDA, Masashi, Takuma ARAGI, Katsuyosi TSUJITA, and Tatsuya MASUDA. "1A1-D18 A guidance locomotion with harnessing of a quadruped robot towards seeing-eye dog robot." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2008 (2008): _1A1—D18_1—_1A1—D18_4. http://dx.doi.org/10.1299/jsmermd.2008._1a1-d18_1.

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27

Saegusa, Shozo, Yuya Yasuda, Yoshitaka Uratani, Eiichirou Tanaka, Toshiaki Makino, and Jen-Yuan Chang. "Development of a guide-dog robot: human–robot interface considering walking conditions for a visually handicapped person." Microsystem Technologies 17, no. 5-7 (January 12, 2011): 1169–74. http://dx.doi.org/10.1007/s00542-010-1219-1.

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28

SAKAI, Seiya, Shotaro FURUTA, Tsuyoshi NAKAMURA, Masayoshi KANOH, Koji YAMADA, Yuji IWAHORI, and Shinji FUKUI. "Efficient Hearing-Dog-Robot Searching for User Using Life Pattern Clustering." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 32, no. 5 (October 15, 2020): 860–65. http://dx.doi.org/10.3156/jsoft.32.5_860.

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29

Chen, Qiang, Yinong Chen, Jinhui Zhu, Gennaro De Luca, Mei Zhang, and Ying Guo. "Traffic light and moving object detection for a guide-dog robot." Journal of Engineering 2020, no. 13 (July 1, 2020): 675–78. http://dx.doi.org/10.1049/joe.2019.1137.

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30

TAJIRI, Tomoki, Rei OGAMI, Yogo TAKADA, Atushi IMADU, and Tadao KAWAI. "211 The 3D map for obstacles recognition by guide dog robot." Proceedings of the Symposium on Evaluation and Diagnosis 2010.9 (2010): 106–8. http://dx.doi.org/10.1299/jsmesed.2010.9.106.

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31

Saegusa, Shozo, Goki Fujimoto, Yuya Yashuda, Yoshitaka Uratani, and Eiichiro Tanaka. "1305 Detection of Bar-Shape Obstacles for a Guide-Dog Robot." Proceedings of the Conference on Information, Intelligence and Precision Equipment : IIP 2008 (2008): 90–94. http://dx.doi.org/10.1299/jsmeiip.2008.90.

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32

Kerepesi, A., E. Kubinyi, G. K. Jonsson, M. S. Magnusson, and Á. Miklósi. "Behavioural comparison of human–animal (dog) and human–robot (AIBO) interactions." Behavioural Processes 73, no. 1 (July 2006): 92–99. http://dx.doi.org/10.1016/j.beproc.2006.04.001.

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33

Yamanobe, Natsuki, Ee Sian Neo, Eiichi Yoshida, Nobuyuki Kita, Kazuyuki Nagata, Kazuhito Yokoi, and Yosuke Takano. "Integration of Manipulation, Locomotion, and Communication Intelligent RT Software Components for Mobile Manipulator System Using Scenario Tools in OpenRT Platform." Journal of Robotics and Mechatronics 22, no. 3 (June 20, 2010): 322–32. http://dx.doi.org/10.20965/jrm.2010.p0322.

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The OpenRT Platform, an integrated development environment for component-based robot system development, is being constructed in order to enhance intelligent robot research and development efficiency. In this paper, a mobile manipulator system that can bring human indicated objects like a service dog is developed based on the OpenRT Platform. The system works with several components providing manipulation, locomotion, and communication functions developed as examples modularizing intelligent robotic functions. These components are integrated using scenario tools in the OpenRT Platform for achieving target tasks.

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34

Onishi, Tomohiko. "POO--CHI." Journal of Robotics and Mechatronics 14, no. 1 (February 20, 2002): 76–77. http://dx.doi.org/10.20965/jrm.2002.p0076.

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POO-CHI of the KOKO-ROBO Series is a dog-shaped toy robot targeting girls of about 10 years old. To date, it has recorded sales of about 19,000,000. POO--CHI sings, moves its legs, and changes the expression of its eyes based on different input to its sensors. Its safety features prevent fingers of children from being caught in the legs of the robot by accident. The main challenge in its development was to reconcile lower cost with functions, especially those that make POO--CHI appear smarter.

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Kondo, Hayato, Tamaki Ura, and Yoshiaki Nose. "Development of an Autonomous Underwater Vehicle ""Tri-Dog"" Toward Practical Use in Shallow Water." Journal of Robotics and Mechatronics 13, no. 2 (April 20, 2001): 205–11. http://dx.doi.org/10.20965/jrm.2001.p0205.

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To develop robots used in an underwater environment, it is necessary to cope with restrictions such as high pressure, low visibility caused by bad transmissivity of light in water, and the fact that electromagnetic wave cannot be used for communications and positioning. Moreover, they operate in oceans and lakes, which involve complicate fluid disturbance and unknown obstacles that are difficult to predict. Therefore, it is important to build full-scale ocean-going vehicles and testbed vehicles, and do experiments in real environments. Sophisticated testbed vehicles are essential facilities for development of highly intelligent systems to be converted to ocean-going vehicles. This paper describes the design of ""Tri-Dog 1"", specifications, tank tests for system identification, and supposed mission. The robot has functions that are higher level than that of practical vehicles, considering experiments in shallow water such as lakes, marshes and shallow sea, and in test tanks. It is possible to apply the developed software system to practical vehicles immediately.

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36

François, Dorothée, Stuart Powell, and Kerstin Dautenhahn. "A long-term study of children with autism playing with a robotic pet." Interaction Studies 10, no. 3 (December 10, 2009): 324–73. http://dx.doi.org/10.1075/is.10.3.04fra.

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This paper presents a novel methodological approach of how to design, conduct and analyse robot-assisted play. This approach is inspired by nondirective play therapy. The experimenter participates in the experiments, but the child remains the main leader for play. Besides, beyond inspiration from non-directive play therapy, this approach enables the experimenter to regulate the interaction under specific conditions in order to guide the child or ask her questions about reasoning or affect related to the robot. This approach has been tested in a long-term study with six children with autism in a school setting. An autonomous robot with zoomorphic, dog-like appearance was used in the studies. The children’s progress was analyzed according to three dimensions, namely, Play, Reasoning and Affect. Results from the case-study evaluations have shown the capability of the method to meet each child’s needs and abilities. Children who mainly played solitarily progressively experienced basic imitation games with the experimenter. Children who proactively played socially progressively experienced higher levels of play and constructed more reasoning related to the robot. They also expressed some interest in the robot, including, on occasion, affect. Keywords: Human–Robot Interaction, Robot-Mediated Therapy, Robot-Assisted Play, Non-Directive Play Therapy, Assistive Technology, Autism, Children

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37

OGAWA, Hironori, Katsuyuki SAGAYAMA, and Kazuteru TOBITA. "1P1-L12 Development of the Guide Dog Robot using a Force Sensor." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2009 (2009): _1P1—L12_1—_1P1—L12_3. http://dx.doi.org/10.1299/jsmermd.2009._1p1-l12_1.

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38

Dahl, Torbjørn S. "Problems with using a human-dog interaction model for human-robot interaction?" Interaction Studies 15, no. 2 (August 20, 2014): 190–94. http://dx.doi.org/10.1075/is.15.2.05dah.

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39

Uratani, Yoshitaka, Shozo Saegusa, Goki Fujimoto, Yuya Yashuda, Eiichiro Tanaka, and Toshiaki Makino. "3718 Detection and avoidance of Bar-Shape Obstacles for Assistance-Dog Robot." Proceedings of the JSME annual meeting 2008.7 (2008): 227–28. http://dx.doi.org/10.1299/jsmemecjo.2008.7.0_227.

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40

Zhang, Jiaqi, Xiaolei Han, and Xueying Han. "Walking quality guaranteed central pattern generator control method." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 228, no. 3 (May 8, 2013): 569–79. http://dx.doi.org/10.1177/0954406213488854.

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Creating effective locomotion for a legged robot is a challenging task. Central pattern generators have been widely used to control robot locomotion. However, one significant disadvantage of the central pattern generator method is its inability to design high-quality walks because it only produces sine or quasi-sine signals for motor control as compared to most cases in which the expected control signals are more advanced. Control accuracy is therefore diminished when traditional methods are replaced by central pattern generators resulting in unaesthetically pleasing walking robots. In this paper, we present a set of solutions, based on testings of Sony’s four-legged robotic dog (AIBO), which produces the same walking quality as traditional methods. First, we designed a method based on both evolution and learning to optimize the walking gait. Second, a central pattern generator model was put forth to enabled AIBO to learn from arbitrary periodic inputs, which resulted in the replication of the optimized gait to ensure high-quality walking. Lastly, an accelerator sensor feedback was introduced so that AIBO could detect uphill and downhill terrains and change its gait according to the surrounding environment. Simulations were performed to verify this method.

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Taghavi, Luecke, and Jeffery. "A Neuro-Prosthetic Device for Substituting Sensory Functions during Stance Phase of the Gait." Applied Sciences 9, no. 23 (November 27, 2019): 5144. http://dx.doi.org/10.3390/app9235144.

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In this study, we present the experimental results demonstrating the functionality of our recently developed “balancing device” for walking restoration in patients with spinal cord injuries. Since we are preparing this device for testing on dogs, we program the analytical core of the device to recognize both stance and swing phases of the dog gait, the direction that the dog is falling, as well as selecting a suitable balancing strategy to prevent falling. The analytical core of the device is a commercial microcontroller, the Teensy, which is able to provide suitable stimulation commands and intensities as a voltage for delivery to the stimulation circuit and target muscles. We show the functional schematic of the device along with experimental results obtained by testing the device in a simulated robotic dog. Results show that the sensory system of the animal lost by spinal cord injury can be replaced by the sensing core of the device and the analytical core can provide appropriate stimulation control to balance the body of a dog. All test results are obtained using our robot test-bed and living animals are not involved in this study.

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Kim, Young Jin, Samuel Jung, Tae Yun Kim, and Wan Suk Yoo. "Simplified Model of Wheel Type Dog-Horse Robot to Reduce Dynamic Analysis Time." Transactions of the Korean Society of Mechanical Engineers A 40, no. 2 (February 1, 2016): 157–65. http://dx.doi.org/10.3795/ksme-a.2016.40.2.157.

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ARAI, Junki, Tomoya SHIRAKI, and Atsuo YABU. "1P1-L11 Search and Conveyance Technique for Small Object by Partner Dog Robot." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2009 (2009): _1P1—L11_1—_1P1—L11_2. http://dx.doi.org/10.1299/jsmermd.2009._1p1-l11_1.

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Chernyak, Nadia, and Heather E. Gary. "Children’s Cognitive and Behavioral Reactions to an Autonomous Versus Controlled Social Robot Dog." Early Education and Development 27, no. 8 (April 7, 2016): 1175–89. http://dx.doi.org/10.1080/10409289.2016.1158611.

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Takezawa, Hayato, and Hideo Nakagawa. "720 Door opening and shutting operation by service dog robot using QR code." Proceedings of Conference of Kansai Branch 2011.86 (2011): _7–20_. http://dx.doi.org/10.1299/jsmekansai.2011.86._7-20_.

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KUBO, Seiji, Hideo NAKAGAWA, and Ichiro KITAYAMA. "J165031 Ma Collection movement of the falling object with the service dog robot." Proceedings of Mechanical Engineering Congress, Japan 2012 (2012): _J165031–1—_J165031–3. http://dx.doi.org/10.1299/jsmemecj.2012._j165031-1.

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Gaussier, P., C. Joulain, A. Revel, and J. P. Cocquerez. "How Acting Allows to Segregate Objects in a Visual Scene." Perception 25, no. 1_suppl (August 1996): 54. http://dx.doi.org/10.1068/v96l1110.

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Our purpose is to allow an autonomous robot to find and to categorise objects in a visual scene according to the actions it performs. The robot information comes from a CCD gray-level camera. The edges are extracted and a simple DOG filter is used to find ‘corner’-like forms in the image. These positions are used as possible focus points. The robot eye performs saccadic movements on the whole visual scene. A log-polar transform of the image is performed in the neighbourhood of the focus points to mimic the projection of the retina on the primary cortical areas. It simplifies object recognition by allowing size and rotation invariance. Those local views are learned on a self-organised topological map according to a vigilance level. At the same time, the robot tries to associate them with a particular action. For instance, we want the robot to learn to turn left when it sees a ‘turn-left’ arrow in the image. The problem is that the robot cannot see only a single object in the visual scene. There are many distractors such as doors, holes, and other objects not significant for the robot behaviour. At the beginning, a probabilistic conditioning rule allows the robot to associate all the seen objects to the performed movement. The robot repeatedly removes or creates new synaptic links to take into account only salient associations. As a result, object categorisation is not performed at the visual level (pure recognition of visual shape), but at the motor level (the action the robot has to perform). Our experiments show that learning and recognition of an object can be greatly simplified if we take into account the sensory-motor loop of the robot in its environment.

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RAZALI, SAZALINSYAH, QINGGANG MENG, and SHUANG-HUA YANG. "IMMUNE-INSPIRED COOPERATIVE MECHANISM WITH REFINED LOW-LEVEL BEHAVIORS FOR MULTI-ROBOT SHEPHERDING." International Journal of Computational Intelligence and Applications 11, no. 01 (March 2012): 1250007. http://dx.doi.org/10.1142/s1469026812500071.

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In this paper, immune systems and its relationships with multi-robot shepherding problems are discussed. The proposed algorithm is based on immune network theories that have many similarities with the multi-robot systems domain. The underlying immune-inspired cooperative mechanism of the algorithm is simulated and evaluated. The paper also describes a refinement of the memory-based immune network that enhances a robot's action-selection process. A refined model, which is based on the Immune Network T-cell-regulated — with Memory (INT-M) model, is applied to the dog–sheep scenario. The refinements involves the low-level behaviors of the robot dogs, namely shepherds' formation and shepherds' approach. These behaviors would make the shepherds form a line behind the group of sheep and also obey a safety zone of each flock, thus achieving better control of the flock and minimize flock separation occurrences. Simulation experiments are conducted on the Player/Stage robotics platform.

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Hwangbo, Jemin, Joonho Lee, Alexey Dosovitskiy, Dario Bellicoso, Vassilios Tsounis, Vladlen Koltun, and Marco Hutter. "Learning agile and dynamic motor skills for legged robots." Science Robotics 4, no. 26 (January 16, 2019): eaau5872. http://dx.doi.org/10.1126/scirobotics.aau5872.

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Legged robots pose one of the greatest challenges in robotics. Dynamic and agile maneuvers of animals cannot be imitated by existing methods that are crafted by humans. A compelling alternative is reinforcement learning, which requires minimal craftsmanship and promotes the natural evolution of a control policy. However, so far, reinforcement learning research for legged robots is mainly limited to simulation, and only few and comparably simple examples have been deployed on real systems. The primary reason is that training with real robots, particularly with dynamically balancing systems, is complicated and expensive. In the present work, we introduce a method for training a neural network policy in simulation and transferring it to a state-of-the-art legged system, thereby leveraging fast, automated, and cost-effective data generation schemes. The approach is applied to the ANYmal robot, a sophisticated medium-dog–sized quadrupedal system. Using policies trained in simulation, the quadrupedal machine achieves locomotion skills that go beyond what had been achieved with prior methods: ANYmal is capable of precisely and energy-efficiently following high-level body velocity commands, running faster than before, and recovering from falling even in complex configurations.

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Yasuda, Yuya, Shozo Saegusa, Goki Fujimoto, Yoshitaka Uratani, Eiichiro Tanaka, and Toshiaki Makino. "2P2-F09 Detection and Avoidance of Pole-Shape Obstacles for a Guide-Dog Robot." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2008 (2008): _2P2—F09_1—_2P2—F09_4. http://dx.doi.org/10.1299/jsmermd.2008._2p2-f09_1.

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