10 research outputs found
Harmonic Versus Chaos Controlled Oscillators in Hexapedal Locomotion
The behavioural diversity of chaotic oscillator can be controlled into periodic dynamics and used to model locomotion using central pattern generators. This paper shows how controlled chaotic oscillators may improve the adaptation of the robot locomotion behaviour to terrain uncertainties when compared to nonlinear harmonic oscillators. This is quantitatively assesses by the stability, changes of direction and steadiness of the robotic movements. Our results show that the controlled Wu oscillator promotes the emergence of adaptive locomotion when deterministic sensory feedback is used. They also suggest that the chaotic nature of chaos controlled oscillators increases the expressiveness of pattern generators to explore new locomotion gaits
Variability, Symmetry, and Dynamics in Human Rhythmic Motor Control
How humans and other animals control rhythmic behaviors, and locomotion in particular, is one of the grand challenges of neuroscience and biomechanics. And yet remarkably few studies address the fundamental control-systems modeling of locomotor control. This thesis attempts to address several pieces of this grand challenge through the development of experimental, theoretical, and computational tools. Specifically, we focus our attention on three key features of human rhythmic motor control, namely
variability, symmetry, and dynamics.
Variability: Little is known about how haptic sensing of discrete events, such as heel-strike in walking, in rhythmic dynamic tasks enhances behavior and performance. In order to discover the role of discrete haptic cues on rhythmic motor control performance, we study a virtual paddle juggling behavior. We show that haptic sensing of a force impulse to the hand at the moment of ball-paddle collision categorically improves performance over visual feedback alone, not by regulating the rate of convergence to steady state, but rather by reducing cycle-to-cycle variability.
Symmetry: Neglecting evident characteristics of a system can certainly be a modeling convenience, but it may also produce a better statistical model. For example, the dynamics of human locomotion is frequently treated as symmetric about the sagittal plane for modeling convenience. In this work, we test this assumption by examining the statistical consequences of neglecting (or not) bilateral asymmetries in the dynamics of human walking. Indeed, we show that there are statistically significant asymmetries in the walking dynamics of healthy participants (N=8), but that by ignoring these asymmetries and fitting a symmetric model to the data, we arrive at a more consistent and predictive model of human walking.
Dynamics: Rhythmic hybrid dynamic behaviors can be observed in a wide variety of biological and robotic systems. Analytic (white-box) modeling tools of such systems are limited to the case when we have a full (and preferably simple) mathematical model that can accurately describe the system dynamics. In contrast, data-driven (block-box) system identification methods have the potential to overcome this fundamental limitation and could play a critical role in describing and analyzing the dynamics of rhythmic behaviors based on experimental data. And yet few tools exist for identifying the dynamics of rhythmic systems from input--output data. In this context, we propose a new formulation for identifying the dynamics of rhythmic hybrid dynamical systems around their limit-cycles by using discrete-time harmonic transfer functions
18th IEEE Workshop on Nonlinear Dynamics of Electronic Systems: Proceedings
Proceedings of the 18th IEEE Workshop on Nonlinear Dynamics of Electronic Systems, which took place in Dresden, Germany, 26 â 28 May 2010.:Welcome Address ........................ Page I
Table of Contents ........................ Page III
Symposium Committees .............. Page IV
Special Thanks ............................. Page V
Conference program (incl. page numbers of papers)
................... Page VI
Conference papers
Invited talks ................................ Page 1
Regular Papers ........................... Page 14
Wednesday, May 26th, 2010 ......... Page 15
Thursday, May 27th, 2010 .......... Page 110
Friday, May 28th, 2010 ............... Page 210
Author index ............................... Page XII
Bio-Inspired Robotics
Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensoryâmotor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field
Intelligent System Synthesis for Dynamic Locomotion Behavior in Multi-legged Robots
Robot technology has been implemented in many fields of our life, such as entertainment, security, rescue, rehabilitation, social life, the military, and etc. Multi-legged robot always exist in many fields, therefore it is important to be developed. Motion capabilities of the robot will be a main focus to be developed. Current development or conventional model of motion capabilities have several issues in saturation of development. There are some limitation in dynamic factors such as, locomotion generator, flexibility of motion planning, and smoothness of movement. Therefore, in this research, natural based computation are implemented as the basic model. There are three subsystems to be developed and integrated, (1) locomotion behavior model, (2) stability behavior model, and (3) motion planning model. Since individual people has different walking behavior in each walking direction and walking speed, locomotion behavior learning model of omni-directional bio-inspired locomotion which is generating different walking behavior in different walking provision are required to be developed. Step length in sagital and coronal direction, and degree of turning are considered parameters in walking provision. In proposed omni-directional walking model, interconnection structures composed by 16 neurons where 1 leg is represented by 4 joints and 1 joint is represented by 2 motor neurons. In order to acquire walking behavior in certain walking provision, the interconnection structure is optimized by multi-objectives evolutionary algorithm. For acquiring the diversity of references, several optimized interconnection structures are generated in optimization processes in different walking provisions. Learning models are proposed for solving non-linearity of relationship between walking input and walking output representing the synaptic weight of interconnection structure, where one learning model representing one walking parameter. Furthermore, by using optimized model, walking behavior can be generated with unsealed walking provision. Smooth walking transition with low error of desired walking provision was proved based on several numerical experiments in physical computer simulation. In stability behavior model, neuro-based push recovery controller is applied in multi-legged robot in order to keep the stability with minimum energy required. There are three motion patterns in individual people behavior when it gets external perturbation, those are ankle behavior, hip behavior, and step behavior. We propose a new model of Modular Recurrent Neural Network (MRNN) for performing online learning system in each motion behavior. MRNN consists of several recurrent neural networks (RNNs) working alternatively depending on the condition. MRNN performs online learning process of each motion behavior controller independently. The aim of push recovery controller is to manage the motion behavior controller by minimizing the energy required for responding to the external perturbation. This controller selects the appropriate motion behavior and adjusts the gain that represent the influence of the motion behavior to certain push disturbance based on behavior graphs which is generated by adaptive regression spline. We applied the proposed controller to the humanoid robot that has small footprint in open dynamics engine. Experimental result shows the effectiveness of the push controller stabilizing the external perturbation with minimum energy required. Proposed motion planning model presents a natural mechanism of the human brain for generating a dynamic path planning in 3-D rough terrain. The proposed model not only emphasizes the inner state process of the neuron but also the development process of the neurons in the brain. There are two information transmission processes in this proposed model, the forward transmission activity for constructing the neuron connections to find the possible way and the synaptic pruning activity with backward neuron transmission for finding the best pathway from current position to target position and reducing inefficient neuron with its synaptic connections. In order to respond and avoid the unpredictable obstacle, dynamic path planning is also considered in this proposed model. An integrated system for applying the proposed model in the actual experiments is also presented. In order to confirm the effectiveness of the proposed model, we applied the integrated system in the pathway of a four-legged robot on rough terrain in computer simulation. For analyzing and proving the flexibility of proposed model, unpredictable collision is also performed in those experiments. The model can find the best pathway and facilitate the safe movement of the robot. When the robot found an unpredictable collision, the path planner dynamically changed the pathway. The proposed path planning model is capable to be applied in further advance implementation. In order to implement the motion capabilities in real cases, all subsystem should be integrated into one interconnected motion capabilities model. We applied small quadruped robot equipped with IMU, touch sensor, and dual ultrasonic sensor for performing motion planning in real terrain from starting point to goal point. Before implemented, topological map is generated by Kinect camera. In this implementation, all subsystem were analyzed and performed well and the robot able to stop in the goal point. These implementation proved the effectiveness of the system integration, the motion planning model is able to generate safe path planning, the locomotion model is able to generate flexible movement depending on the walking provision from motion planning model, and the stability model can stabilize the robot on rough terrain. Generally, the proposed model can be expected to bring a great contribution to the motion capabilities development and can be used as alternative model for acquiring the dynamism and efficient model in the future instead of conventional model usage. In the future, the proposed model can be applied into any legged robot as navigation, supporter, or rescue robot in unstable environmental condition. In addition, we will realize a cognitive locomotion that generates multiple gaits depending on the 3 aspects, embodiment, locomotion generator, and cognition model. A dynamic neuro-locomotion integrated with internal and external sensory information for correlating with the environmental condition will be designed.ããããæè¡ã¯ããšã³ã¿ãŒãã€ã¡ã³ããã»ãã¥ãªãã£ãæå©ããªãããªã瀟äŒç掻ãè»äºãªã©ã®æ§ã
ãªç掻åéã«å®çŸããŠãããå€èããããã¯åžžã«å€ãã®åéã«ååšããããéçºããããšãéèŠã§ãããããããã®éåèœåãéçºã®äž»èŠãšãªã£ãŠãããçŸç¶ã®éçºãããŠããåäœèœåã¯,飜åç¶æ
ã«ãããããã€ãã®åçãªèŠå ã«ãããæ©è¡çæåšãåäœèšç»ã®æè»æ§ãããã³åäœã®æ»ãããçã«å¶éããããããã§ãæ¬ç 究ã§ã¯ãåºæ¬çãªã¢ãã«ãšããŠèªç¶èšç®ã«åºã¥ãæ¹æ³è«ãå®è£
ããããŸããæ¬ç 究ã§ã¯ãæ©è¡åäœã¢ãã«ãå®å®åäœã¢ãã«ããéåèšç»ã¢ãã«ãããªã3ã€ã®ãµãã·ã¹ãã ãéçºãçµ±åããã人éã¯æ©è¡æ¹åãšé床ã«å¿ããŠæ©è¡åäœãç°ãªããããç°ãªãæ©è¡è»žã§ã¯ç°ãªãæ©è¡åäœãçæãããšããå
šæ¹äœçç©çãªéåã®æ©è¡åäœåŠç¿ã¢ãã«ãéçºã«ã¯èŠæ±ããããçæ¬ ããã³å¶åŸ¡æ¹åã®ã¹ãããé·ãæåã®åºŠåãã¯,æ©è¡è»žã®ãã©ã¡ãŒã¿ãšããŠèæ
®ããããææ¡ããå
šæ¹äœæ©è¡ã¢ãã«ã§ã¯,1è¢ã«ã€ã16åã®ãã¥ãŒãã³ã«ãã£ãŠæ§æãããçžäºæ¥ç¶æ§é ã4ã€ã®é¢ç¯ã«ãã£ãŠè¡šçŸããããŸãã1ã€ã®é¢ç¯ã¯,2åã®ã¢ãŒã¿ãã¥ãŒãã³ã«ãã£ãŠè¡šçŸãããäžå®ã®æ©è¡è»žã§ã®æ©è¡åäœãç²åŸããããã«,æ¬ç 究ã§ã¯,å€ç®çé²åã¢ã«ãŽãªãºã ã«ãã£ãŠæé©åãè¡ããææ¡ææ³ã§ã¯ãåç
§ç¹ã®å€æ§æ§ãç²åŸããããã«,ç°ãªãæ©è¡è»žã«ãããŠããã€ãã®æé©ãªçžäºæ¥ç¶æ§é ãçæããããçžäºæ¥ç¶æ§é ã®ã·ããã¹éã¿ãè¡šçŸããŠããæ©è¡å
¥åãšåºåéã®éç·åœ¢ãªé¢ä¿ã解ãããã®åŠç¿ã¢ãã«ãæ§ç¯ãããæ¬ææ³ã§ã¯,1ã€ã®åŠç¿ã¢ãã«ã1ã€ã®æ©è¡ãã©ã¡ãŒã¿ã§è¡šçŸãããæé©åãããã¢ãã«ãçšããããšã«ãã,æ©è¡åäœã¯,ã¹ã±ãŒãªã³ã°ãããŠããªãæ©è¡è»žãçæããããšãå¯èœãšãªã,ç©çæŒç®ã·ãã¥ã¬ãŒã·ã§ã³ãçšããå®éšã«ãã,誀差ã®å°ãªãæ©è¡è»žã®æ»ãããªæ©è¡é·ç§»ãæ¬å®éšã§ã¯ç€ºããŠãããå®å®åäœã¢ãã«ã§ã¯ãå¿
èŠæå°éã®ãšãã«ã®ãŒã§å®å®æ§ãç¶æããããå€è¶³æ©è¡ããããã«ãã¥ãŒãããŒã¹ããã·ã¥ãªã«ããªå¶åŸ¡åšãé©çšãããå€åããåãããšã,人éã®è¡åã«ã¯è¶³éŠã®åäœã»è¡é¢ç¯ã®åäœã»èžã¿åäœã®3ã€ã®åäœãã¿ãŒã³ãååšãããæ¬ç 究ã§ã¯,åéååäœã«ããããªã³ã©ã€ã³åŠç¿ã·ã¹ãã ãå®çŸããããã«ãã¢ãžã¥ã©ãŒãªã«ã¬ã³ããã¥ãŒã©ã«ãããã¯ãŒã¯(MRNN)ãçšããæ°ããªåŠç¿ã¢ãã«ãææ¡ãããMRNNã¯ç¶æ³ã«å¿ããŠéžæãããè€æ°ã®ãªã«ã¬ã³ããã¥ãŒã©ã«ãããã¯ãŒã¯(RNN)ã«ãã£ãŠæ§æããããMRNNã¯åéååäœã³ã³ãããŒã©ã®ãªã³ã©ã€ã³åŠç¿ããã»ã¹ãç¬ç«ããŠå®è¡ãããããã·ã¥ãªã«ããªå¶åŸ¡åšã®ç®çã¯ãå€ä¹±ã«å¿ããŠãšãã«ã®ãŒæå°åãè¡ãããšã«ãã£ãŠéååäœå¶åŸ¡åšã管çããããšã§ããããã®å¶åŸ¡åšã¯é©åãªéååäœãéžæã,é©å¿ååž°ã¹ãã©ã€ã³ã«ããçæãããåäœã°ã©ãã«åºã¥ãæŒãåäœã«å¯ŸããŠæã圱é¿ãåãŒãéååäœã®ã²ã€ã³ã®èª¿æŽãè¡ããææ¡ããå¶åŸ¡åšãOpen Dynamics Engine(ODE)äžã§å°ããªè¶³ã®é·ããæã€ãã¥ãŒããã€ãããããã«é©çšã,å¿
èŠæå°éã®ãšãã«ã®ãŒã§å€åã«å¯ŸããŠå®å®ãããããã·ã¥ãªã«ããªå¶åŸ¡åšã®æå¹æ§ã瀺ããŠããã3次å
ã®äžæŽå°ã«ãããåçãªçµè·¯èšç»ãçæããããã«,人éã®èªç¶ãªè³æ©èœã«åºã¥ããåäœèšç»ææ³ãææ¡ãããæ¬ã¢ãã«ã¯ããã¥ãŒãã³ã®å
éšç¶æ
éçšã ãã§ãªããè³å
ã®ãã¥ãŒãã³ã®çºééçšãéèŠããŠãããæ¬ã¢ãã«ã¯äºã€ã®ã¢ã«ãŽãªãºã ã«æ§æãããã1ã€ã¯ãééå¯èœãªéãèŠã€ããããã«æ§ç¯ãããæ¥ç¶çãªãã¥ãŒãã³æŽ»åã§ããé æ¹åäŒé掻åã§ãã,ãã1ã€ã¯ãçŸåšäœçœ®ããæé©çµè·¯ãèŠã€ããããã«ãã·ããã¹çµåãçšããŠéå¹ççãªãã¥ãŒãã³ãæžå°ãããéæ¹åã«ãã¥ãŒãã³äŒéãè¡ãã·ããã¹ãã«ãŒãã³ã°æŽ»åã§ããããŸã,äºæž¬äžå¯èœãªè¡çªãåé¿ããããã«,åçãªçµè·¯èšç»ãå®è¡ããããããã«ãå®ç°å¢ã«ãããŠææ¡ãããã¢ãã«ãå®çŸããããã®çµ±åã·ã¹ãã ãæ瀺ããããææ¡ã¢ãã«ã®æå¹æ§ãæ€èšŒããããã«,ã³ã³ãã¥ãŒã¿ã·ãã¥ã¬ãŒã·ã§ã³äžã§ãäžæŽå°ç°å¢ã®4足æ©è¡ããããã«é¢ããã·ãã¥ã¬ãŒã·ã§ã³ç°å¢ãå®è£
ããããããã®å®éšã§ã¯,äºæž¬äžèœãªè¡çªã«é¢ããå®éšãè¡ã£ããæ¬ã¢ãã«ã¯ãæé©çµè·¯ãèŠã€ãåºãããããã®å®å
šãªç§»åãå®çŸã§ãããããã«ããããããäºæž¬ã§ããªãè¡çªãæ€åºããå Žå,çµè·¯èšç»ã¢ã«ãŽãªãºã ãçµè·¯ãåçã«å€æŽå¯èœã§ããããšã瀺ããŠããããããã®ããšãããææ¡ãããçµè·¯èšç»ã¢ãã«ã¯ãããªãå
é²çãªå±éãå®çŸå¯èœã§ãããšèãããããå®ç°å¢ã«ãããéåèœåãå®è£
ããããã«ã¯ããã¹ãŠã®ãµãã·ã¹ãã ã1ã€ã®éåèœåã¢ãã«ã«çµ±åããå¿
èŠããããããã§æ¬ç 究ã§ã¯ãIMUãã¿ããã»ã³ãµã2ã€ã®è¶
é³æ³¢ã»ã³ãµãæèŒããå°åã®4足æ©è¡ãããããçšããå®ç°å¢ã«ãããŠåºçºå°ç¹ããç®çå°ç¹ãŸã§ã®éåèšç»ãè¡ã£ããæ¬å®è£
ã§ã¯ã3次å
è·é¢èšæž¬ã»ã³ãµã§ããKinecãçšã3次å
空éã®äœçžæ§é ãçæããããŸããæ¬å®è£
ã§ã¯ããã¹ãŠã®ãµãã·ã¹ãã ãåæãããããããã¯ç®çå°ç¹ã§åæ¢ããããšãã§ãããããã«ãå®å
šãªçµè·¯èšç»ãçæããããšãã§ããããšããã·ã¹ãã çµ±åã®æå¹æ§ã確èªã§ããããŸããæ©è¡ã¢ãã«ã«ããæ©è¡è»žã«å¿ããæè»ãªåããçæãããããšã§ããã®å®å®æ§ã¢ãã«ã¯äžæŽå°ç°æ¹ã§ãããããã®æ©è¡ãå®å®ãããããšãã§ããããããã®ããšãããæ¬ææ¡ã¢ãã«ã¯éåèœåãžã®å€å€§ãªè²¢ç®ãæåŸ
ããããã€ããã¯ã¹ãç²åŸããããã®ä»£æ¿ã¢ãã«ãšããŠäœ¿çšããããšãã§ã,çŸåšãã䜿çšãããŠããã¢ãã«ã«ä»£ããå¹ççãªã¢ãã«ãšãªãããšãèãããããä»åŸã®èª²é¡ãšããŠã¯,äžå®å®ãªç°å¢äžã«ãããããã²ãŒã·ã§ã³ã»æ¯æŽã»ã¬ã¹ãã¥ãŒãããããšãã£ãä»»æã®è¢ã®æ°ãæã€å€è¶³æ©è¡ãããããžã®æ¬ææ¡ã¢ãã«ã®é©çšãããããããããã«,身äœæ§,æ©è¡çæ,èªç¥ã¢ãã«ã®3ã€ã®èŠ³ç¹ããè€æ°ã®æ©å®¹ãçæããèªç¥çæ©è¡ãå®çŸããããšãèããŠãããç°å¢ãšçžäºäœçšããããã®ã¢ãã«ãšããŠãå
çã»ã³ãµãšå€çã»ã³ãµæ
å ±ãçµ±åããåçãã¥ãŒãæ©è¡ãå®çŸããäºå®ã§ãããéŠéœå€§åŠæ±äº¬, 2018-03-25, 修士ïŒå·¥åŠïŒéŠéœå€§åŠæ±
Manipulador aéreo con brazos antropomórficos de articulaciones flexibles
[Resumen] Este artÃculo presenta el primer robot manipulador aéreo con dos brazos antropomórficos diseñado para aplicarse en tareas de inspección y mantenimiento en entornos industriales de difÃcil acceso para operarios humanos. El robot consiste en una plataforma aérea multirrotor equipada con dos brazos antropomórficos ultraligeros, asà como el sistema de control integrado de la plataforma y los brazos. Una de las principales caracterÃsticas del manipulador es la flexibilidad mecánica proporcionada en todas las articulaciones, lo que aumenta la seguridad en las interacciones fÃsicas con el entorno y la protección del propio robot. Para ello se ha introducido un compacto y simple mecanismo de transmisión por muelle entre el eje del servo y el enlace de salida. La estructura en aluminio de los brazos ha sido cuidadosamente diseñada de forma que los actuadores estén aislados frente a cargas radiales y axiales que los puedan dañar. El manipulador desarrollado ha sido validado a través de experimentos en base fija y en pruebas de vuelo en exteriores.Ministerio de EconomÃa y Competitividad; DPI2014-5983-C2-1-