109 research outputs found

    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

    Fast biped walking with a neuronal controller and physical computation

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    Biped walking remains a difficult problem and robot models can greatly {facilitate} our understanding of the underlying biomechanical principles as well as their neuronal control. The goal of this study is to specifically demonstrate that stable biped walking can be achieved by combining the physical properties of the walking robot with a small, reflex-based neuronal network, which is governed mainly by local sensor signals. This study shows that human-like gaits emerge without {specific} position or trajectory control and that the walker is able to compensate small disturbances through its own dynamical properties. The reflexive controller used here has the following characteristics, which are different from earlier approaches: (1) Control is mainly local. Hence, it uses only two signals (AEA=Anterior Extreme Angle and GC=Ground Contact) which operate at the inter-joint level. All other signals operate only at single joints. (2) Neither position control nor trajectory tracking control is used. Instead, the approximate nature of the local reflexes on each joint allows the robot mechanics itself (e.g., its passive dynamics) to contribute substantially to the overall gait trajectory computation. (3) The motor control scheme used in the local reflexes of our robot is more straightforward and has more biological plausibility than that of other robots, because the outputs of the motorneurons in our reflexive controller are directly driving the motors of the joints, rather than working as references for position or velocity control. As a consequence, the neural controller and the robot mechanics are closely coupled as a neuro-mechanical system and this study emphasises that dynamically stable biped walking gaits emerge from the coupling between neural computation and physical computation. This is demonstrated by different walking experiments using two real robot as well as by a Poincar\'{e} map analysis applied on a model of the robot in order to assess its stability. In addition, this neuronal control structure allows the use of a policy gradient reinforcement learning algorithm to tune the parameters of the neurons in real-time, during walking. This way the robot can reach a record-breaking walking speed of 3.5 leg-lengths per second after only a few minutes of online learning, which is even comparable to the fastest relative speed of human walking

    Simulation and Framework for the Humanoid Robot TigerBot

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    Walking humanoid robotics is a developing field. Different humanoid robots allow for different kinds of testing. TigerBot is a new full-scale humanoid robot with seven degrees-of-freedom legs and with its specifications, it can serve as a platform for humanoid robotics research. Currently TigerBot has encoders set up on each joint, allowing for position control, and its sensors and joints connect to Teensy microcontrollers and the ODroid XU4 single-board computer central control unit. The components’ communication system used the Robot Operating System (ROS). This allows the user to control TigerBot with ROS. It’s important to have a simulation setup so a user can test TigerBot’s capabilities on a model before using the real robot. A working walking gait in the simulation serves as a test of the simulator, proves TigerBot’s capability to walk, and opens further development on other walking gaits. A model of TigerBot was set up using the simulator Gazebo, which allowed testing different walking gaits with TigerBot. The gaits were generated by following the linear inverse pendulum model and the basic zero-moment point (ZMP) concept. The gaits consisted of center of mass trajectories converted to joint angles through inverse kinematics. In simulation while the robot follows the predetermined joint angles, a proportional-integral controller keeps the model upright by modifying the flex joint angle of the ankles. The real robot can also run the gaits while suspended in the air. The model has shown the walking gait based off the ZMP concept to be stable, if slow, and the actual robot has been shown to air walk following the gait. The simulation and the framework on the robot can be used to continue work with this walking gait or they can be expanded on for different methods and applications such as navigation, computer vision, and walking on uneven terrain with disturbances

    The Runbot: engineering control applied to rehabilitation in spinal cord injury patients

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    Human walking is a complicated interaction among the musculoskeletal system, nervous system and the environment. An injury affecting the neurological system, such as a spinal cord injury (SCI) can cause sensor and motor deficits, and can result in a partial or complete loss of their ambulatory functions. Functional electrical stimulation (FES), a technique to generate artificial muscle contractions with the application of electrical current, has been shown to improve the ambulatory ability of patients with an SCI. FES walking systems have been used as a neural prosthesis to assist patients walking, but further work is needed to establish a system with reduced engineering complexity which more closely resembles the pattern of natural walking. The aim of this thesis was to develop a new FES gait assistance system with a simple and efficient FES control based on insights from robotic walking models, which can be used in patients with neuromuscular dysfunction, for example in SCI. The understanding of human walking is fundamental to develop suitable control strategies. Limit cycle walkers are capable of walking with reduced mechanical complexity and simple control. Walking robots based on this principle allow bio-inspired mechanisms to be analysed and validated in a real environment. The Runbot is a bipedal walker which has been developed based on models of reflexes in the human central nervous system, without the need for a precise trajectory algorithm. Instead, the timing of the control pattern is based on ground contact information. Taking the inspiration of bio-inspired robotic control, two primary objectives were addressed. Firstly, the development of a new reflexive controller with the addition of ankle control. Secondly, the development of a new FES walking system with an FES control model derived from the principles of the robotic control system. The control model of the original Runbot utilized a model of neuronal firing processes based on the complexity of the central neural system. As a causal relationship between foot contact information and muscle activity during human walking has been established, the control model was simplified using filter functions that transfer the sensory inputs into motor outputs, based on experimental observations in humans. The transfer functions were applied to the RunBot II to generate a stable walking pattern. A control system for walking was created, based on linear transfer functions and ground reaction information. The new control system also includes ankle control, which has not been considered before. The controller was validated in experiments with the new RunBot III. The successful generation of stable walking with the implementation of the novel reflexive robotic controller indicates that the control system has the potential to be used in controlling the strategies in neural prosthesis for the retraining of an efficient and effective gait. To aid of the development of the FES walking system, a reliable and practical gait phase detection system was firstly developed to provide correct ground contact information and trigger timing for the control. The reliability of the system was investigated in experiments with ten able-bodied subjects. Secondly, an automatic FES walking system was implemented, which can apply stimulation to eight muscles (four in each leg) in synchrony with the user’s walking activity. The feasibility and effectiveness of this system for gait assistance was demonstrated with an experiment in seven able-bodied participants. This thesis addresses the feasibility and effectiveness of applying biomimetic robotic control principles to FES control. The interaction among robotic control, biology and FES control in assistive neural prosthesis provides a novel framework to developing an efficient and effective control system that can be applied in various control applications

    Bipedal robotic walking control derived from analysis of human locomotion

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    This paper presents a human-inspired approach to the design of bipedal robotic walking control, using information that appears to be intrinsic to human walking. We first investigated the correlation between ground contact information from the feet and leg muscle activity (EMG) in human walking. From this relationship filter functions were created which relate the sensory input to motor actions producing a minimal, nonlinear and robust robotic controller which incorporates hip, knee and ankle control. The developed control system was subsequently analysed by applying it to our bipedal robot "RunBot III", a minimalistic robotic walker designed without any central pattern generators (CPGs) or precise trajectory control. Our results demonstrated that this controller, which regards the function between the sensory input and motor output as a black box derived from human data, can generate stable robotic walking. This indicates that complex locomotion patterns can result from a simple model based on reflexes and supports the premise that human-inspired methods have the potential for use in the control of robotics or in the development of assistive devices for gait
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