216 research outputs found

    Simulating a Flexible Robotic System based on Musculoskeletal Modeling

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    Humanoid robotics offers a unique research tool for understanding the human brain and body. The synthesis of human motion is a complex procedure that involves accurate reconstruction of movement sequences, modeling of musculoskeletal kinematics, dynamics and actuation, and characterization of reliable performance criteria. Many of these processes have much in common with the problems found in robotics research, with the recent advent of complex humanoid systems. This work presents the design and development of a new-generation bipedal robot. Its modeling and simulation has been realized by using an open-source software to create and analyze dynamic simulation of movement: OpenSim. Starting from a study by Fuben He, our model aims to be used as an innovative approach to the study of a such type of robot in which there are series elastic actuators represented by active and passive spring components in series with motors. It has provided of monoarticular and biarticular joint in a very similar manner to human musculoskeletal model. This thesis is only the starting point of a wide range of other possible future works: from the control structure completion and whole-body control application, to imitation learning and reinforcement learning for human locomotion, from motion test on at ground to motion test on rough ground, and obviously the transition from simulation to practice with a real elastic bipedal robot biologically-inspired that can move like a human bein

    Simulating Adaptive Human Bipedal Locomotion Based on Phase Resetting Using Foot-Contact Information

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    Humans generate bipedal walking by cooperatively manipulating their complicated and redundant musculoskeletal systems to produce adaptive behaviors in diverse environments. To elucidate the mechanisms that generate adaptive human bipedal locomotion, we conduct numerical simulations based on a musculoskeletal model and a locomotor controller constructed from anatomical and physiological findings. In particular, we focus on the adaptive mechanism using phase resetting based on the foot-contact information that modulates the walking behavior. For that purpose, we first reconstruct walking behavior from the measured kinematic data. Next, we examine the roles of phase resetting on the generation of stable locomotion by disturbing the walking model. Our results indicate that phase resetting increases the robustness of the walking behavior against perturbations, suggesting that this mechanism contributes to the generation of adaptive human bipedal locomotion

    OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotion

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    Muscle-actuated control is a research topic that spans multiple domains, including biomechanics, neuroscience, reinforcement learning, robotics, and graphics. This type of control is particularly challenging as bodies are often overactuated and dynamics are delayed and non-linear. It is however a very well tested and tuned actuation mechanism that has undergone millions of years of evolution with interesting properties exploiting passive forces and efficient energy storage of muscle-tendon units. To facilitate research on muscle-actuated simulation, we release a 3D musculoskeletal simulation of an ostrich based on the MuJoCo physics engine. The ostrich is one of the fastest bipeds on earth and therefore makes an excellent model for studying muscle-actuated bipedal locomotion. The model is based on CT scans and dissections used to collect actual muscle data, such as insertion sites, lengths, and pennation angles. Along with this model, we also provide a set of reinforcement learning tasks, including reference motion tracking, running, and neck control, used to infer muscle actuation patterns. The reference motion data is based on motion capture clips of various behaviors that we preprocessed and adapted to our model. This paper describes how the model was built and iteratively improved using the tasks. We also evaluate the accuracy of the muscle actuation patterns by comparing them to experimentally collected electromyographic data from locomoting birds. The results demonstrate the need for rich reward signals or regularization techniques to constrain muscle excitations and produce realistic movements. Overall, we believe that this work can provide a useful bridge between fields of research interested in muscle actuation.Comment: https://github.com/vittorione94/ostrichr

    Modeling and Control of Adjustable Articulated Parallel Compliant Actuation Arrangements in Articulated Robots

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    Considerable advances in robotic actuation technology have been made in recent years. Particularly the use of compliance has increased, both as series elastic elements as well as in parallel to the main actuation drives. This work focuses on the model formulation and control of compliant actuation structures including multiple branches and multi-articulation, and significantly contributes by proposing an elegant modular formulation that describes the energy exchange between the compliant elements and articulated multi-body robot dynamics using the concept of power flows, and a single matrix that describes the entire actuation topology. Using this formulation, a novel gradient descent based control law is derived for torque control of compliant actuation structures with adjustable pretension, with proven convexity for arbitrary actuation topologies. Extensions towards handling unidirectionality of elastic elements and joint motion compensation are also presented. A simulation study is performed on a 3-DoF leg model, where series-elastic main drives are augmented by parallel elastic tendons with adjustable pretension. Two actuation topologies are considered, one of which includes a biarticulated tendon. The data demonstrate the effectiveness of the proposed modelling and control methods. Furthermore, it is shown the biarticulated topology provides significant benefits over the monoarticulated arrangement

    Musculoskeletal Modeling of the Human Lower Limb Stiffness for Robotic Applications

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    This research work presents a physiologically accurate and novel computationally fast neuromusculoskeletal model of the human lower limb stiffness. The proposed computational framework uses electromyographic signals, motion capture data and ground reaction forces to predict the force developed by 43 musculotendon actuators. The estimated forces are then used to compute the musculotendon stiffness and the corresponding joint stiffness. The estimations at each musculotendon unit is constrained to simultaneously satisfy the joint angles and the joint moments of force generated with respect to five degrees of freedom, including: Hip Adduction-Abduction, Hip Flexion-Extension, Hip Internal-External Rotation, Knee Flexion-Extension, and Ankle Plantar-Dorsi Flexion. Advanced methods are used to perform accurate muscle-driven dynamic simulations and to guarantee the dynamic consistency between kinematic and kinetic data. This study presents also the design, simulation and prototyping of a small musculoskeletal humanoid made for replicating the human musculoskeletal structure in an artificial apparatus capable to maintain a quiet standing position using only a completely passive elastic actuation structure. The proposed prototype has a total mass of about 2 kg and its height is 40 cm. It comprises of four segments for each leg and six degrees of freedom, including: Hip Adduction-Abduction, Hip Flexion-Extension, Knee Flexion-Extension, Ankle Plantar-Dorsi Flexion, Ankle Inversion-Eversion, and Toe Flexion-Extension. In order to reconstruct the continuous state space parameters proper of the assembly's control of quiet standing, a hybrid non-linear Extended Kalman Filter based technique is proposed to combine a base-excited inverted pendulum kinematic model of the robot with the discrete-time position measurements. This research work provides effective solutions and readily available software tools to improve the human interaction with robotic assistive devices, advancing the research in neuromusculoskeletal modeling to better understand the mechanisms of actuation provided by human muscles and the rules that govern the lower limb joint stiffness regulation. The obtained results suggest that the neuromusculoskeletal modeling technology can be exploited to address the challenges on the development of musculoskeletal humanoids, new generation human-robot interfaces, motion control algorithms, and intelligent assistive wearable devices capable to effectively ensure a proper dynamic coupling between human and robot

    Advancing Musculoskeletal Robot Design for Dynamic and Energy-Efficient Bipedal Locomotion

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    Achieving bipedal robot locomotion performance that approaches human performance is a challenging research topic in the field of humanoid robotics, requiring interdisciplinary expertise from various disciplines, including neuroscience and biomechanics. Despite the remarkable results demonstrated by current humanoid robots---they can walk, stand, turn, climb stairs, carry a load, push a cart---the versatility, stability, and energy efficiency of humans have not yet been achieved. However, with robots entering our lives, whether in the workplace, in clinics, or in normal household environments, such improvements are increasingly important. The current state of research in bipedal robot locomotion reveals that several groups have continuously demonstrated enhanced locomotion performance of the developed robots. But each of these groups has taken a unilateral approach and placed the focus on only one aspect, in order to achieve enhanced movement abilities;---for instance, the motion control and postural stability or the mechanical design. The neural and mechanical systems in human and animal locomotion, however, are strongly coupled and should therefore not be treated separately. Human-inspired musculoskeletal design of bipedal robots offers great potential for enhanced dynamic and energy-efficient locomotion but also imposes major challenges for motion planning and control. In this thesis, we first present a detailed review of the problems related to achieving enhanced dynamic and energy-efficient bipedal locomotion, from various important perspectives, and examine the essential properties of the human locomotory apparatus. Subsequently, existing insights and approaches from biomechanics, to understand the neuromechanical motion apparatus, and from robotics, to develop more human-like robots that can move in our environment, are discussed in detail. These thorough investigations of the interrelated essential design decisions are used to develop a novel design for a musculoskeletal bipedal robot, BioBiped1, such that, in the long term, it is capable of realizing dynamic hopping, running, and walking motions. The BioBiped1 robot features a highly compliant tendon-driven actuation system that mimics key functionalities of the human lower limb system. In experiments, BioBiped1's locomotor function for the envisioned gaits is validated globally. It is shown that the robot is able to rebound passively, store and release energy, and actively push off from the ground. The proof of concept of BioBiped1's locomotor function, however, marks only the starting point for our investigations, since this novel design concept opens up a number of questions regarding the required design complexity for the envisioned motions and the appropriate motion generation and control concept. For this purpose, a simulator specifically designed for the requirements of musculoskeletally actuated robotic systems, including sufficiently realistic ground reaction forces, is developed. It relies on object-oriented design and is based on a numerical solver, without model switching, to enable the analysis of impact peak forces and the simulation of flight phases. The developed library also contains the models of the actuated and passive mono- and biarticular elastic tendons and a penalty-based compliant contact model with nonlinear damping, to incorporate the collision, friction, and stiction forces occurring during ground contact. Using these components, the full multibody system (MBS) dynamics model is developed. To ensure a sufficiently similar behavior of the simulated and the real musculoskeletal robot, various measurements and parameter identifications for sub-models are performed. Finally, it is shown that the simulation model behaves similarly to the real robot platform. The intelligent combination of actuated and passive mono- and biarticular tendons, imitating important human muscle groups, offers tremendous potential for improved locomotion performance but also requires a sophisticated concept for motion control of the robot. Therefore, a further contribution of this thesis is the development of a centralized, nonlinear model-based method for motion generation and control that utilizes the derived detailed dynamics models of the implemented actuators. The concept is used to realize both computer-generated hopping and human jogging motions. Additionally, the problem of appropriate motor-gear unit selection prior to the robot's construction is tackled, using this method. The thesis concludes with a number of simulation studies in which several leg actuation designs are examined for their optimality with regard to systematically selected performance criteria. Furthermore, earlier paradoxical biomechanical findings about biarticular muscles in running are presented and, for the first time, investigated by detailed simulation of the motion dynamics. Exploring the Lombard paradox, a novel reduced and energy-efficient locomotion model without knee extensor has been simulated successfully. The models and methods developed within this thesis, as well as the insights gained, are already being employed to develop future prototypes. In particular, the optimal dimensioning and setting of the actuators, including all mono- and biarticular muscle-tendon units, are based on the derived design guidelines and are extensively validated by means of the simulation models and the motion control method. These developments are expected to significantly enhance progress in the field of bipedal robot design and, in the long term, to drive improvements in rehabilitation for humans through an understanding of the neuromechanics underlying human walking and the application of this knowledge to the design of prosthetics

    A contact-implicit direct trajectory optimization scheme for the study of legged maneuverability

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    For legged robots to move safely in unpredictable environments, they need to be manoeuvrable, but transient motions such as acceleration, deceleration and turning have been the subject of little research compared to constant-speed gait. They are difficult to study for two reasons: firstly, the way they are executed is highly sensitive to factors such as morphology and traction, and secondly, they can potentially be dangerous, especially when executed rapidly, or from high speeds. These challenges make it an ideal topic for study by simulation, as this allows all variables to be precisely controlled, and puts no human, animal or robotic subjects at risk. Trajectory optimization is a promising method for simulating these manoeuvres, because it allows complete motion trajectories to be generated when neither the input actuation nor the output motion is known. Furthermore, it produces solutions that optimize a given objective, such as minimizing the distance required to stop, or the effort exerted by the actuators throughout the motion. It has consequently become a popular technique for high-level motion planning in robotics, and for studying locomotion in biomechanics. In this dissertation, we present a novel approach to studying motion with trajectory optimization, by viewing it more as “trajectory generation” – a means of generating large quantities of synthetic data that can illuminate the differences between successful and unsuccessful motion strategies when studied in aggregate. One distinctive feature of this approach is the focus on whole-body models, which capture the specific morphology of the subject, rather than the highly-simplified “template” models that are typically used. Another is the use of “contact-implicit” methods, which allow an appropriate footfall sequence to be discovered, rather than requiring that it be defined upfront. Although contact-implicit methods are not novel, they are not widely-used, as they are computationally demanding, and unnecessary when studying comparatively-predictable constant speed locomotion. The second section of this dissertation describes innovations in the formulation of these trajectory optimization problems as nonlinear programming problems (NLPs). This “direct” approach allows these problems to be solved by general-purpose, open-source algorithms, making it accessible to scientists without the specialized applied mathematics knowledge required to solve NLPs. The design of the NLP has a significant impact on the accuracy of the result, the quality of the solution (with respect to the final value of the objective function), and the time required to solve the proble
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