Humanoid Robot Soccer Locomotion and Kick Dynamics: Open Loop Walking, Kicking and Morphing into Special Motions on the Nao Robot

Striker speed and accuracy in the RoboCup (SPL) international robot soccer league is becoming increasingly important as the level of play rises. Competition around the ball is now decided in a matter of seconds. Therefore, eliminating any wasted actions or motions is crucial when attempting to kick the ball. It is common to see a discontinuity between walking and kicking where a robot will return to an initial pose in preparation for the kick action. In this thesis we explore the removal of this behaviour by developing a transition gait that morphs the walk directly into the kick back swing pose. The solution presented here is targeted towards the use of the Aldebaran walk for the Nao robot. The solution we develop involves the design of a central pattern generator to allow for controlled steps with realtime accuracy, and a phase locked loop method to synchronise with the Aldebaran walk so that precise step length control can be activated when required. An open loop trajectory mapping approach is taken to the walk that is stabilized statically through the use of a phase varying joint holding torque technique. We also examine the basic princples of open loop walking, focussing on the commonly overlooked frontal plane motion. The act of kicking itself is explored both analytically and empirically, and solutions are provided that are versatile and powerful. Included as an appendix, the broader matter of striker behaviour (process of goal scoring) is reviewed and we present a velocity control algorithm that is very accurate and efficient in terms of speed of execution

Genetically evolved dynamic control for quadruped walking

The aim of this dissertation is to show that dynamic control of quadruped locomotion is achievable through the use of genetically evolved central pattern generators. This strategy is tested both in simulation and on a walking robot. The design of the walker has been chosen to be statically unstable, so that during motion less than three supporting feet may be in contact with the ground. The control strategy adopted is capable of propelling the artificial walker at a forward locomotion speed of ~1.5 Km/h on rugged terrain and provides for stability of motion. The learning of walking, based on simulated genetic evolution, is carried out in simulation to speed up the process and reduce the amount of damage to the hardware of the walking robot. For this reason a general-purpose fast dynamic simulator has been developed, able to efficiently compute the forward dynamics of tree-like robotic mechanisms. An optimization process to select stable walking patterns is implemented through a purposely designed genetic algorithm, which implements stochastic mutation and cross-over operators. The algorithm has been tailored to address the high cost of evaluation of the optimization function, as well as the characteristics of the parameter space chosen to represent controllers. Experiments carried out on different conditions give clear indications on the potential of the approach adopted. A proof of concept is achieved, that stable dynamic walking can be obtained through a search process which identifies attractors in the dynamics of the motor-control system of an artificial walker

Metastable legged-robot locomotion

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 195-215).A variety of impressive approaches to legged locomotion exist; however, the science of legged robotics is still far from demonstrating a solution which performs with a level of flexibility, reliability and careful foot placement that would enable practical locomotion on the variety of rough and intermittent terrain humans negotiate with ease on a regular basis. In this thesis, we strive toward this particular goal by developing a methodology for designing control algorithms for moving a legged robot across such terrain in a qualitatively satisfying manner, without falling down very often. We feel the definition of a meaningful metric for legged locomotion is a useful goal in and of itself. Specifically, the mean first-passage time (MFPT), also called the mean time to failure (MTTF), is an intuitively practical cost function to optimize for a legged robot, and we present the reader with a systematic, mathematical process for obtaining estimates of this MFPT metric. Of particular significance, our models of walking on stochastically rough terrain generally result in dynamics with a fast mixing time, where initial conditions are largely "forgotten" within 1 to 3 steps. Additionally, we can often find a near-optimal solution for motion planning using only a short time-horizon look-ahead. Although we openly recognize that there are important classes of optimization problems for which long-term planning is required to avoid "running into a dead end" (or off of a cliff!), we demonstrate that many classes of rough terrain can in fact be successfully negotiated with a surprisingly high level of long-term reliability by selecting the short-sighted motion with the greatest probability of success. The methods used throughout have direct relevance to machine learning, providing a physics-based approach to reduce state space dimensionality and mathematical tools to obtain a scalar metric quantifying performance of the resulting reduced-order system.by Katie Byl.Ph.D

Biomechanics and Energetics of Bipedal Locomotion on Uneven Terrain.

Humans navigate uneven terrain in their everyday lives. From trails, grass, and uneven sidewalks, we constantly adapt to various surfaces in our environment. Past research has shown that walking on natural terrain, compared to walking on smooth flat surfaces, results in increased energy expenditure during locomotion. However, the biomechanical adaptations responsible for this energetic increase are unclear, since locomotion research is often conducted either on short walkways or in an outdoor setting, thus limiting data collections. To further our understanding of human locomotion on uneven terrain, I focused on quantifying the biomechanical and energetic changes due to increased terrain variability during walking and running. First, this thesis presents modifications to a regular exercise treadmill to allow for attachment of a separate uneven surface. Using this treadmill, I collected kinetic, kinematic, electromyographic, and energy expenditure data during continuous human walking and running. I showed that humans walking at 1.0m/s on an uneven surface, with a 2.5cm height variability, increased energy expenditure by 0.73W/kg (approx. 28%) compared to walking on smooth terrain. Greater energy expenditure was primarily caused by increased positive work at the hip and knee, with minor contributions from increased muscle activity and step parameter adaptations. I then showed that running at 2.3m/s on the same surface resulted in an energetic increase of 0.48W/kg (approx. 5%) compared to running on even terrain. In contrast to walking, humans compensated for uneven terrain during running by reducing positive work produced by the ankle and adapting a more crouched leg posture. The similar absolute increases in energetic cost between walking and running implied that much of this increase is likely due to surface height variability and changes in mechanical work. Finally, this work presents analytical and simulated analyses for the rimless wheel and simplest walker models. These analyses explored the relationship between gait dynamics, energy input strategies, surface unevenness and the energetic cost of walking. Together, these studies advance our understanding of the relationship between mechanics and energetics of human walking on uneven surfaces and could potentially lead to more robust and energetically efficient legged robots, prostheses and more effective clinical rehabilitation interventions.PhDKinesiology and Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111616/1/voloshis_1.pd

Applied optimal control for dynamically stable legged locomotion

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 79-84).Online learning and controller adaptation will be an essential component for legged robots in the next few years as they begin to leave the laboratory setting and join our world. I present the first example of a learning system which is able to quickly and reliably acquire a robust feedback control policy for 3D dynamic bipedal walking from a blank slate using only trials implemented on the physical robot. The robot begins walking within a minute and learning converges in approximately 20 minutes. The learning works quickly enough that the robot is able to continually adapt to the terrain as it walks. This success can be attributed in part to the mechanics of our robot, which is capable of stable walking down a small ramp even when the computer is turned off. In this thesis, I analyze the dynamics of passive dynamic walking, starting with reduced planar models and working up to experiments on our real robot. I describe, in detail, the actor-critic reinforcement learning algorithm that is implemented on the return map dynamics of the biped. Finally, I address issues of scaling and controller augmentation using tools from optimal control theory and a simulation of a planar one-leg hopping robot. These learning results provide a starting point for the production of robust and energy efficient walking and running robots that work well initially, and continue to improve with experience.by Russell L. Tedrake.Ph.D

Reimagining Robotic Walkers For Real-World Outdoor Play Environments With Insights From Legged Robots: A Scoping Review

PURPOSE For children with mobility impairments, without cognitive delays, who want to participate in outdoor activities, existing assistive technology (AT) to support their needs is limited. In this review, we investigate the control and design of a selection of robotic walkers while exploring a selection of legged robots to develop solutions that address this gap in robotic AT. METHOD We performed a comprehensive literature search from four main databases: PubMed, Google Scholar, Scopus, and IEEE Xplore. The keywords used in the search were the following: “walker”, “rollator”, “smart walker”, “robotic walker”, “robotic rollator”. Studies were required to discuss the control or design of robotic walkers to be considered. A total of 159 papers were analyzed. RESULTS From the 159 papers, 127 were excluded since they failed to meet our inclusion criteria. The total number of papers analyzed included publications that utilized the same device, therefore we classified the remaining 32 studies into groups based on the type of robotic walker used. This paper reviewed 15 different types of robotic walkers. CONCLUSIONS The ability of many-legged robots to negotiate and transition between a range of unstructured substrates suggests several avenues of future consideration whose pursuit could benefit robotic AT, particularly regarding the present limitations of wheeled paediatric robotic walkers for children’s daily outside use. For more information: Kod*lab (link to kodlab.seas.upenn.edu

Advancing Human Lower-limb Kinematic Estimation Using Inertial Measurement Units

The study of human biomechanics has broad applications in human health, worker safety, warfighter performance, athlete performance, injury prevention, and related fields. Historically, research in all of these fields is frequently limited by measurements of human kinematics being restricted to laboratory environments. Wearable sensors, in the form of body-worn inertial measurement units (IMUs), show great promise in extending the validity of research conclusions by enabling measurements in non-laboratory environments such as the workplace, home, clinic, and training facility. However, to accurately estimate human kinematics from body-worn IMUs, advancements must be made in signal processing methods to correct integration drift errors caused by the integration of noisy sensor data. This dissertation addresses this need by contributing a novel error-state Kalman filter (ErKF) method for estimating the kinematics of the human lower limbs in broad contexts. The lower limbs are chosen due to their paramount importance in the applications articulated above. This research achievement follows the systematic progression of three studies that advance IMU-based kinematic estimation for: 1) a single foot-mounted IMU, 2) an array of three body-worn IMUs in a mechanical “walker” (an approximation to the human lower limbs), and 3) an array of seven body-worn IMUs in a full representation of the human lower limbs. The major findings and contributions of each study are summarized below. The first study lays a critical foundation for the full lower-limb model by exploring the limiting case of deploying a single foot-mounted IMU to estimate foot trajectories. This study contributes criteria for selecting IMU sensor hardware to achieve accurate estimates of stride parameters (e.g., stride length, stride angle) and reveals that prior zero-velocity drift corrections developed for normal walking remain applicable for highly dynamic gaits, including fast walking and running. The second study builds from the first by considering three IMUs attached to the three segments of a mechanical “walker” (composed of a pelvis and two straight legs) which serves as an approximation to the human lower limbs. The study contributes a novel ErKF method to estimate the kinematics of the coupled, three-body walker model. Importantly, the method uses kinematic constraints to reduce integration drift errors without reliance on magnetometers or common assumptions (e.g., level-ground). The method successfully estimates the kinematics of a mechanical walker which replicate closely those obtained via simulation and experimental motion capture (MOCAP). For instance, the (hip) joint angles achieve RMS differences below 1.5 degrees compared to MOCAP. The success of the ErKF method on the three-body walker model motivates its extension to a full, seven-body model of the human lower limbs in the third study. This study contributes novel joint axis corrections within the ErKF for the hip and knee to reduce joint angle drift errors and to account for the additional complexities of human anatomy (e.g., soft tissue, biological joints). The resulting full model is evaluated on human subjects performing six different types of gait and compared to results from MOCAP. This comparison reveals RMS differences in joint angle estimates generally below 5 degrees when compared to MOCAP employing reflective markers attached to the IMUs. Similarly, small differences in the estimated joint angle ranges of motion, stride length, and step width confirm the significant promise of this novel ErKF method as a research strategy for non-laboratory based biomechanical studies of the human lower limbs and in broad contexts.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168067/1/mvpotter_1.pd

Principles of energy optimization underlying human walking gait adaptations

Learning to move in novel situations is a complex process. We need to continually learn the changing situations and determine the best way to move. Optimization is a widely accepted framework for this process. However, little is known about algorithms used by the nervous system to perform this optimization. Our lab recently found evidence that people can continuously optimize energy during walking. My goal in this thesis is to identify principles of optimization, particularly energy optimization in walking, that govern our choice of movement in novel situations. I used two novel walking tasks for this purpose. For the first task, I designed, built, and tested a mechatronic system that can quickly, accurately, and precisely apply forces to a user’s torso. It changes the relationship between a walking gait and its associated energetic cost—cost landscape—to shift the energy optimal walking gait. Participants shift their gait towards the new optimum in these landscapes. In my second project, I aimed to understand how the nervous system identifies when to initiate optimization. I used my system to create cost landscapes of three different cost gradients. I found that experiencing a steeper cost gradient through natural variability is not sufficient to cue the nervous system to initiate optimization. For my third and fourth projects, I used the task of split-belt walking. I collaborated with another research group to analyse the mechanics and energetics of walking with different step lengths on a split-belt treadmill. I found that people can harness energy from a split-belt treadmill by placing their leading leg further forward on the fast belt, and that there may be an energy optimal gait. In my fourth project, I used computer modelling to identify that there may exist an energy optimal gait due to the trade-off between the cost of swinging the leg and the cost of redirecting the body center of mass when transitioning from step to step. Together, these projects develop a new system and a new approach to understand energy optimization in walking. They uncover principles governing the initiation of this process and our ability to benefit from it

An admittance shaping controller for exoskeleton assistance of the lower extremities

We present a method for lower-limb exoskeleton control that defines assistance as a desired dynamic response for the human leg. Wearing the exoskeleton can be seen as replacing the leg's natural admittance with the equivalent admittance of the coupled system. The control goal is to make the leg obey an admittance model defined by target values of natural frequency, peak magnitude and zero-frequency response. No estimation of muscle torques or motion intent is necessary. Instead, the controller scales up the coupled system's sensitivity transfer function by means of a compensator employing positive feedback. This approach increases the leg's mobility and makes the exoskeleton an active device capable of performing net positive work on the limb. Although positive feedback is usually considered destabilizing, here performance and robust stability are successfully achieved through a constrained optimization that maximizes the system's gain margins while ensuring the desired location of its dominant poles

Biomechanical models and stability analysis of bipedal running = Biomechanische Modelle und Stabilitätsanalyse des zweibeinigen Rennens

Humans and birds both walk and run bipedally on compliant legs. However, differences in leg architecture may result in species-specific leg control strategies as indicated by the observed gait patterns. In this work, control strategies for stable running are derived based on a conceptual model and compared with experimental data on running humans and pheasants (Phasianus colchicus). From a model perspective, running with compliant legs can be represented by the planar spring mass model. However, to compare experimental data to simulated spring mass running, an effective leg stiffness has to be defined. In chapter 2, different methods of estimating a leg stiffness during running are compared to running patterns predicted by the spring mass model, and a new method only relying on temporal parameters is proposed and used in the further course of this work. It has been shown that spring mass running is self-stabilizing for sufficiently high running speeds. However, to provide stability over a broader range of running, control strategies can be applied and swing leg control is one elegant approach to stabilize the running pattern, while maintaining the system energy conservative. Here, linear adaptations of the swing leg parameters, leg angle, leg length and leg stiffness, are assumed. Experimentally observed kinematic control parameters (leg rotation and leg length change) of running humans (chapter 3 and 4) and pheasants (chapter 4) are compared, and interpreted within the context of this model, with specific focus on stability and robustness characteristics