Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion

Novel Locomotion Methods in Magnetic Actuation and Pipe Inspection

There is much room for improvement in tube network inspections of jet aircraft. Often, these inspections are incomplete and inconsistent. In this paper, we develop a Modular Robotic Inspection System (MoRIS) for jet aircraft tube networks and a corresponding kinematic model. MoRIS consists of a Base Station for user control and communication, and robotic Vertebrae for accessing and inspecting the network. The presented and tested design of MoRIS can travel up to 9 feet in a tube network. The Vertebrae can navigate in all orientations, including smooth vertical tubes. The design is optimized for nominal 1.5 outside diameter tubes. We developed a model of the Locomotion Vertebra in a tube. We defined the model\u27s coordinate system and its generalized coordinates. We studied the configuration space of the robot, which includes all possible orientations of the Locomotion Vertebra. We derived the expression for the elastic potential energy of the Vertebra\u27s suspensions and minimized it to find the natural settling orientation of the robot. We further explore the effect of the tractive wheel\u27s velocity constraint on locomotion dynamics. Finally, we develop a general model for aircraft tube networks and for a taut tether. Stabilizing bipedal walkers is a engineering target throughout the research community. In this paper, we develop an impulsively actuated walking robot. Through the use of magnetic actuation, for the first time, pure impulsive actuation has been achieved in bipedal walkers. In studying this locomotion technique, we built the world\u27s smallest walker: Big Foot. A dynamical model was developed for Big Foot. A Heel Strike and a Constant Pulse Wave Actuation Schemes were selected for testing. The schemes were validated through simulations and experiments. We showed that there exists two regimes for impulsive actuation. There is a regime for impact-like actuation and a regime for longer duration impulsive actuation

In Vivo Neuromechanics: Decoding Causal Motor Neuron Behavior with Resulting Musculoskeletal Function.

Human motor function emerges from the interaction between the neuromuscular and the musculoskeletal systems. Despite the knowledge of the mechanisms underlying neural and mechanical functions, there is no relevant understanding of the neuro-mechanical interplay in the neuro-musculo-skeletal system. This currently represents the major challenge to the understanding of human movement. We address this challenge by proposing a paradigm for investigating spinal motor neuron contribution to skeletal joint mechanical function in the intact human in vivo. We employ multi-muscle spatial sampling and deconvolution of high-density fiber electrical activity to decode accurate α-motor neuron discharges across five lumbosacral segments in the human spinal cord. We use complete α-motor neuron discharge series to drive forward subject-specific models of the musculoskeletal system in open-loop with no corrective feedback. We perform validation tests where mechanical moments are estimated with no knowledge of reference data over unseen conditions. This enables accurate blinded estimation of ankle function purely from motor neuron information. Remarkably, this enables observing causal associations between spinal motor neuron activity and joint moment control. We provide a new class of neural data-driven musculoskeletal modeling formulations for bridging between movement neural and mechanical levels in vivo with implications for understanding motor physiology, pathology, and recovery

Control Implementation of Dynamic Locomotion on Compliant, Underactuated, Force-Controlled Legged Robots with Non-Anthropomorphic Design

The control of locomotion on legged robots traditionally involves a robot that takes a standard legged form, such as the anthropomorphic humanoid, the dog-like quadruped, or the bird-like biped. Additionally, these systems will often be actuated with position-controlled servos or series-elastic actuators that are connected through rigid links. This work investigates the control implementation of dynamic, force-controlled locomotion on a family of legged systems that significantly deviate from these classic paradigms by incorporating modern, state-of-the-art proprioceptive actuators on uniquely configured compliant legs that do not closely resemble those found in nature. The results of this work can be used to better inform how to implement controllers on legged systems without stiff, position-controlled actuators, and also provide insight on how intelligently designed mechanical features can potentially simplify the control of complex, nonlinear dynamical systems like legged robots. To this end, this work presents the approach to control for a family of non-anthropomorphic bipedal robotic systems which are developed both in simulation and with physical hardware. The first is the Non-Anthropomorphic Biped, Version 1 (NABi-1) that features position-controlled joints along with a compliant foot element on a minimally actuated leg, and is controlled using simple open-loop trajectories based on the Zero Moment Point. The second system is the second version of the non-anthropomorphic biped (NABi-2) which utilizes the proprioceptive Back-drivable Electromagnetic Actuator for Robotics (BEAR) modules for actuation and fully realizes feedback-based force controlled locomotion. These systems are used to highlight both the strengths and weaknesses of utilizing proprioceptive actuation in systems, and suggest the tradeoffs that are made when using force control for dynamic locomotion. These systems also present case studies for different approaches to system design when it comes to bipedal legged robots

Visual servo control on a humanoid robot

Includes bibliographical referencesThis thesis deals with the control of a humanoid robot based on visual servoing. It seeks to confer a degree of autonomy to the robot in the achievement of tasks such as reaching a desired position, tracking or/and grasping an object. The autonomy of humanoid robots is considered as crucial for the success of the numerous services that this kind of robots can render with their ability to associate dexterity and mobility in structured, unstructured or even hazardous environments. To achieve this objective, a humanoid robot is fully modeled and the control of its locomotion, conditioned by postural balance and gait stability, is studied. The presented approach is formulated to account for all the joints of the biped robot. As a way to conform the reference commands from visual servoing to the discrete locomotion mode of the robot, this study exploits a reactive omnidirectional walking pattern generator and a visual task Jacobian redefined with respect to a floating base on the humanoid robot, instead of the stance foot. The redundancy problem stemming from the high number of degrees of freedom coupled with the omnidirectional mobility of the robot is handled within the task priority framework, allowing thus to achieve con- figuration dependent sub-objectives such as improving the reachability, the manipulability and avoiding joint limits. Beyond a kinematic formulation of visual servoing, this thesis explores a dynamic visual approach and proposes two new visual servoing laws. Lyapunov theory is used first to prove the stability and convergence of the visual closed loop, then to derive a robust adaptive controller for the combined robot-vision dynamics, yielding thus an ultimate uniform bounded solution. Finally, all proposed schemes are validated in simulation and experimentally on the humanoid robot NAO

Hibrit artık robot kolu kullanarak yüksek performanslı taşlama işlemi geliştirmesi.

Automatic grinding using robot manipulators, requires simultaneous control of the robot endpoint and force interaction between the robot and the constraint surface. In robotic grinding, surface quality can be increased by accurate estimation of grinding forces where significant tool and workpiece deflection occurs. Tool deflection during robotic grinding operation causes geometrical errors in the workpiece cross-section. Also, it makes controlling the grinding cutting depth difficult. Moreover small diameter of the tool in robotic grinding causes different behavior in the grinding process in comparison with the tools that are used by universal grinding machines. In this study, a robotic surface grinding force model is developed in order to predict the normal and tangential grinding forces. A physical model is used based on chip formation energy and sliding energy. To improve the model for robotic grinding operations, a refining term is added. In order to include the stiffness of the tool and setup in the force model, penetration tests are implemented and their results are used in refining term of the force model. The model coefficients are estimated using a linear regression technique. The proposed model is validated by comparing model outputs with experimentally obtained data. Evaluation of the test results demonstrates the effectiveness of the proposed model in predicting surface grinding forces. In this thesis, a method is proposed for calculation of the tool deflection in normal and tangential directions based on grinding force feedback in these directions. Based on calculated values, a real-time tool deflection compensation algorithm is developed and implemented. Implementing surface grinding with constant normal force is a well-known approach for improving surface quality. Tool deflection in the robotic grinding causes orientation between the force sensor reference frame and tool reference frame. This means that the measured normal and tangential forces by the sensor are not actual normal and tangential interaction forces between the tool and workpiece. In order to eliminate this problem, a resultant grinding force control strategy is designed and implemented for a parallel hexapod-robotic light abrasive surface grinding operation. Due to the nonlinear nature of the grinding operation, a supervised fuzzy controller is designed where the reference input is identified by the proposed grinding force model. Evaluation of the experimental results demonstrates significant improvement in grinding operation accuracy using the proposed resultant force control strategy in parallel with a real-time tool deflection compensation algorithm. The final aim of this thesis is to develop a posture optimization strategy for robotic grinding operation using 12 DOF hybrid redundant manipulator. The 12 DOF redundant hybrid manipulator of present study is composed of a 6 DOF serial ABB IRB2000 robot and a 6 DOF PI H-824 hexapod where the parallel hexapod is connected to the end of the serial ABB manipulator. Here the fifth joint (wrist) of the ABB serial manipulator is the weakest joint in the robot, so the computed torque of this joint is selected as the cost function. The aim is to minimize this factor by finding the best configuration of the hybrid manipulator using genetic algorithm approach. For such a purpose, a complete kinematic and dynamic model of the 12 DOF manipulator is developed where the output of the grinding force model is fed into the dynamic model as external reaction forces. The computed torque of the wrist joint is given to the optimization module and new configuration is generated by the module and is given to the dynamic model. This process continues until converge to the minimum computed torque value. Then the optimal configuration is chosen for the grinding operation. The evaluation of this posture optimization approach shows its great ability to decrease the necessary actuating torques of the redundant manipulator joints.Ph.D. - Doctoral Progra

7th International Conference on Nonlinear Vibrations, Localization and Energy Transfer: Extended Abstracts

International audienceThe purpose of our conference is more than ever to promote exchange and discussions between scientists from all around the world about the latest research developments in the area of nonlinear vibrations, with a particular emphasis on the concept of nonlinear normal modes and targeted energytransfer

Artificial neural networks as emerging tools for earthquake detection

As seismic networks continue to spread and monitoring sensors become more ef¿cient, the abundance of data highly surpasses the processing capabilities of earthquake interpretation analysts. Earthquake catalogs are fundamental for fault system studies, event modellings, seismic hazard assessment, forecasting, and ultimately, for mitigating the seismic risk. These have fueled the research for the automation of interpretation tasks such as event detection, event identi¿cation, hypocenter location, and source mechanism analysis. Over the last forty years, traditional algorithms based on quantitative analyses of seismic traces in the time or frequency domain, have been developed to assist interpretation. Alternatively, recentadvancesarerelatedtotheapplicationofArti¿cial Neural Networks (ANNs), a subset of machine learning techniques that is pushing the state-of-the-art forward in many areas. Appropriated trained ANN can mimic the interpretation abilities of best human analysts, avoiding the individual weaknesses of most traditional algorithms, and spending modest computational resources at the operational stage. In this paper, we will survey the latest ANN applications to the automatic interpretation of seismic data, with a special focus on earthquake detection, and the estimation of onset times. For a comparative framework, we give an insight into the labor of human interpreters, who may face uncertainties in the case of small magnitude earthquakes.Peer ReviewedPostprint (published version