This paper discusses the design and implementation of a high performance, custom control solution for series elastic actuators (SEA) in a parallel or serial configuration. In many modern robotics applications, controlling actuator output force accurately and with high bandwidth is extremely important. The series elastic actuator has become popular in applications which require precise force control, however currently not many commercial options exist. Commonly, these actuators are custom designed and use electric motors, however most off-the-shelf electric motor drives are not designed for this specific application. In this paper, the hardware and software architecture of a control device designed specifically for force controlled series elastic actuators is described, along with test results on a novel SEA design. / Master of Science

Emergency first responders are the great heroes of our day, having to routinely risk their lives for the safety of others. Developing robotic technologies to aid in such emergencies could greatly reduce the risk these individuals must take, even going so far as to eliminate the need to risk one life for another. In this role, humanoid robots are a strong candidate, being able to take advantage of both the human engineered environment in which it will likely operate, but also make use of human engineered tools and equipment as it deals with a disaster relief effort. The work presented here aims to lessen the hurdles that stand in the way through the research and development of new humanoid robot technologies. To be successful in the role of an emergency first responder requires a fantastic array of skills. One of the most fundamental is the ability to just get to the scene. Unfortunately, it is at this level that humanoid robots currently struggle. This research focuses on the complementary development of physical hardware, digital controllers, and trajectory planning necessary to achieve the research goals of improving the locomotion capabilities of a humanoid robot. To improve the physical performance capabilities of the robot, this research will first focus on the interaction between the hip and knee actuators. It is shown that much like the human body, a biped greatly benefits from the use of biarticular actuation. Improvements in efficiency as much as 30% are possible by simply interconnecting the hip roll and knee pitch joints. Balancing and walking controllers are designed to take advantage of the new hardware capabilities and expand the terrain capabilities of bipedal walking robots to uneven and non-stationary ground. A hybrid position/force control based balancing controller stabilizes the robot's COM regardless of the terrain underfoot. In particular two feedback mechanisms are shown to greatly improve the stability of bipedal systems in response to unmodelled dynamics. The hybrid position/force approach is shown through experiments to greatly extend humanoid capabilities to many types of terrain. With robust balancing ensured, walking trajectories are defined using an improved linear inverted pendulum model that incorporates the swing leg dynamics. The proposed method is shown to significantly reduce the control authority (by 50%) required for satisfactory trajectory following. Three parameters are identified which provide for quick manual or numerical solutions to be found to the trajectory problem. The walking and balance controller were operated on four different terrains successfully, strewn plywood, gravel, and high pile synthetic grass. Furthermore, SAFFiR is believed to be the first bipedal robot to ever walk on sand. The hardware enabled force control architecture was very effective at modulating ground reaction torques no matter the ground conditions. This in combination with highly accurate state estimation provided a very stable balance controller on top of which successful walking was demonstrated. / Ph. D.

Bipedal robots offer a uniquely flexible platform capable of navigating complex, human-centric environments. This makes them ideally suited for a variety of missions, including disaster response and relief, emergency scenarios, or exoskeleton systems for individuals with disabilities. This, however, requires significant advances in humanoid locomotion and control, as they are still slow, unnatural, inefficient, and relatively unstable. The work of this dissertation the state of the art with the aim was of increasing the robustness and efficiency of these bipedal walking platforms. We present a series of control improvements to enable reliable, robust, natural bipedal locomotion that was validated on a variety of bipedal robots using both hardware and simulation experiments. A huge part of reliable walking involves maximizing the robot's control authority. We first present the development of a model predictive controller to both control the ground reaction forces and perform step adjustment for walking stabilization using a mixed-integer quadratic program. This represents the first model predictive controller to include step rotation in the optimization and leverage the capabilities of the time-varying divergent component of motion for navigating rough terrain. We also analyze the potential capabilities of model predictive controllers for the control of bipedal walking. As an alternative to standard trajectory optimization-based model predictive controls, we present several optimization-based control schemes that leverage more traditional bipedal walking control approaches by embedding a proportional feedback controller into a quadratic program. This controller is capable of combining multiple feedback mechanisms: ground reaction feedback (the "ankle strategy"), angular momentum (the "hip strategy"), swing foot speed up, and step adjustment. This allows the robot to effectively shift its weight, pitch its torso, and adjust its feet to retain balance, while considering environmental constraints, when available. To enable the robot to walk with straightened legs, we present a strategy that insures that the dynamic plans are kinematically and dynamically feasible to execute using straight legs. The effects of timing on dynamic plans are typically ignored, resulting in them potentially requiring significantly bending the legs during execution. This algorithm modifies the step timings to insure the plan can be executed without bending the legs beyond certain angle, while leaving the desired footsteps unmodified. To then achieve walking with straight legs we then presented a novel approach for indirectly controlling the center of mass height through the leg angles. This avoids complicated height planning techniques that are both computationally expensive and often not general enough to consider variable terrain by effectively biasing the solution of the whole-body controller towards using straighter legs. To incorporate the toe-off motion that is essential to both natural and straight leg walking, we also present a strategy for toe-off control that allows it to be an emergent behavior of the whole-body controller. The proposed approach was demonstrated through a series of simulation and experimental results on a variety of platforms. Model predictive control for step adjustment and rough terrain is illustrated in simulation, while the other step adjustment strategies and straight leg walking approaches are presented recovering from external disturbances and walking over a variety of terrains in hardware experiments. We discuss many of the practical considerations and limitations required when porting simulation-based controller development to hardware platforms. Using the presented approaches, we also demonstrated a important concept: using whole-body control frameworks, not every desired motion need be directly commanded. Many of these motions, such as toe-off, may simply be emergent behaviors that result by attempting to satisfy other objectives, such as desired reaction forces. We also showed that optimization is a very powerful tool for walking control, able to determine both stabilizing inputs and joint torques. / Ph. D.

Instability of humanoid robots is a common problem, especially given external disturbances or difficult terrain. Even with the robustness of most whole body controllers, instability is inevitable given the right conditions. When these unstable events occur they can result in costly damage to the robot potentially causing a cease of normal functionality. Therefore, it is important to study and develop methods to control a humanoid robot during a fall to reduce the chance of critical damage. This thesis proposes joint angular velocity strategies to reduce the impact velocity resulting from a lateral, backward, or forward fall. These strategies were used on two and three link reduced order models to simulate a fall from standing height of a humanoid robot. The results of these simulations were then used on a full degree of freedom robot, Viginia Tech's humanoid robot ESCHER, to validate the efficacy of these strategies. By using angular velocity strategies for the knee and waist joint, the reduced order models resulted in a decrease in impact velocity of the center of mass by 58%, 87%, and 74% for a lateral, backward, and forward fall respectively in comparison to a rigid fall using the same initial conditions. Best case angular velocity strategies were then developed for various initial conditions for each falling direction. Finally, these parameters were implemented on the full degree of freedom robot which showed results similar to those of the reduced order models. / Master of Science

Humanoid robots have captured the imagination of authors and researchers for years. Development of the bipedal walking necessary for humanoid robots began in earnest in the late 60's with research in Europe and Japan. The the unique challenges of a bipedal locomotion led to initial robots keeping power, computation, and perception systems off-board while developing the actuators and algorithms to enable locomotion. As technology has improved humanoid and exoskeleton systems have finally incorporated all the various subsytems to build a full independent system. Many of the groups building these platforms have developed them based on knowledge acquired through decades of prior development. For groups developing new humanoid systems little guidance on the pitfalls and challenges of humanoid design exist. Virginia Tech's robot ESCHER, developed for the DARPA Robotics Challenge (DRC), is the 4th generation full sized humanoid developed at the University. This paper attempts to quantify the design trades and techniques used to predict performance of ESCHER and how these trades specifically affected the design of the upper body. The development of ESCHER became necessary when it became obvious that the original design assumptions behind the previous robot THOR left it incapable of completing the DRC course and the necessary upgrades would require an almost complete redesign. Using the methods described in this paper ESCHER was designed manufactured and began initial testing within 10 months. One and a half months later ESCHER became the first humanoid to walk the 60 m course at the DRC. The methods described in this paper provide guidance on the decision making process behind the various subsystems on ESCHER. In addition the methodology of developing a dynamic simulation to predict performance before development of the platform helped provide design requirements that ensured the performance of the system. By setting design requirements ESCHER met or exceeded the goals of the team and remains a valuable development platform that can provide utility well beyond the DRC. / Master of Science

<p>U tezi je prikazan novi metod za sintezu i realizaciju dvonožnog<br />veštačkog hoda koji se zasniva na upotrebi jednostavnih pokreta čijim<br />je kombinovanjem moguće realizovati kompleksne pokrete kao što je<br />hod, a čiji se parametri mogu menjati tokom kretanja. Time je omogućeno<br />da se na osnovu informacija o nameravanom kretanju i stanja okoline<br />izvrši sinteza kretanja izborom i kombinacijom jednostavnih<br />bazičnih pokreta koje se nazivaju primitivi. Takođe je omogućeno da se,<br />tokom izvršavanja hoda bez njegovog prekida, menjaju parametri<br />kretanja kao što su brzina hoda, dužina koraka, pravac kretanja i<br />visina podizanja noge tokom prenosne faze. Potvrda je data kroz<br />eksperimentalne rezultate koji su sprovedeni simulacijom na<br />dinamičkom modelu humanoidnog robota.</p> / <p>This dissertation presents new method for the synthesis and realization of<br />biped artificial walk based on the use of simple movements that can be<br />combined in order to achieve complex movements such as walk, whereas it<br />is possible to change the motion parameters at any time. It means that,<br />based on the information about intended movement and current state of the<br />environment, it is possible to synthesize motion by selecting and tying simple<br />movements, i.e. motion primitives. It also enables the robot to change<br />walking parameters online such as walking speed, direction of walk, foot<br />length during swing phase and step length. Proof of this method is given by<br />experimental results obtained during the simulation on a dynamic model of<br />humanoid robot.</p>

Lorsque des changements inattendus de la surface du sol se produisent lors de la marche, le système nerveux central humain doit appliquer des mesures de contrôle appropriées pour assurer une stabilité dynamique. De nombreuses études dans le domaine de la commande moteur ont étudié les mécanismes d'un tel contrôle postural et ont largement décrit comment les trajectoires du centre de masse (COM), le placement des pas et l'activité musculaire s'adaptent pour éviter une perte d'équilibre. Les mesures que nous avons effectuées montrent qu'en arrivant sur un sol mou, les participants ont modulé de façon active les forces de réaction au sol (GRF) sous le pied de support afin d'exploiter les propriétés élastiques et déformables de la surface pour amortir l'impact et probablement dissiper l'énergie mécanique accumulée pendant la ‘chute’ sur la nouvelle surface déformable. Afin de contrôler plus efficacement l'interaction pieds-sol des robots humanoïdes pendant la marche, nous proposons d'ajouter des semelles extérieures souples (c'est-à-dire déformables) aux pieds. Elles absorbent les impacts et limitent les effets des irrégularités du sol pendant le mouvement sur des terrains accidentés. Cependant, ils introduisent des degrés de liberté passifs (déformations sous les pieds) qui complexifient les tâches d'estimation de l'état du robot et ainsi que sa stabilisation globale. Pour résoudre ce problème, nous avons conçu un nouveau générateur de modèle de marche (WPG) basé sur une minimisation de la consommation d'énergie qui génère les paramètres nécessaires pour utiliser conjointement un estimateur de déformation basé sur un modèle éléments finis (FEM) de la semelle souple pour prendre en compte sa déformation lors du mouvement. Un tel modèle FEM est coûteux en temps de calcul et empêche la réactivité en ligne. Par conséquent, nous avons développé une boucle de contrôle qui stabilise les robots humanoïdes lors de la marche avec des semelles souples sur terrain plat et irrégulier. Notre contrôleur en boucle fermée minimise les erreurs sur le centre de masse (COM) et le point de moment nul (ZMP) avec un contrôle en admittance des pieds basé sur un estimateur de déformation simplifié. Nous démontrons son efficacité expérimentalement en faisant marcher le robot humanoïde HRP-4 sur des graviers. / When unexpected changes of the ground surface occur while walking, the human central nervous system needs to apply appropriate control actions to assure dynamic stability. Many studies in the motor control field have investigated the mechanisms of such a postural control and have widely described how center of mass (COM) trajectories, step patterns and muscle activity adapt to avoid loss of balance. Measurements we conducted show that when stepping over a soft ground, participants actively modulated the ground reaction forces (GRF) under the supporting foot in order to exploit the elastic and compliant properties of the surface to dampen the impact and to likely dissipate the mechanical energy accumulated during the ‘fall’ onto the new compliant surface.In order to control more efficiently the feet-ground interaction of humanoid robots during walking, we propose adding outer soft (i.e. compliant) soles to the feet. They absorb impacts and cast ground unevenness during locomotion on rough terrains. However, they introduce passive degrees of freedom (deformations under the feet) that complexify the tasks of state estimation and overall robot stabilization. To address this problem, we devised a new walking pattern generator (WPG) based on a minimization of the energy consumption that offers the necessary parameters to be used jointly with a sole deformation estimator based on finite element model (FEM) of the soft sole to take into account the sole deformation during the motion. Such FEM computation is time costly and inhibit online reactivity. Hence, we developed a control loop that stabilizes humanoid robots when walking with soft soles on flat and uneven terrain. Our closed-loop controller minimizes the errors on the center of mass (COM) and the zero-moment point (ZMP) with an admittance control of the feet based on a simple deformation estimator. We demonstrate its effectiveness in real experiments on the HRP-4 humanoid walking on gravels.

<p>Synthesizing the realistic motion of a humanoid is a very sophisticated task, studied in different research areas. This work addresses the problem to synthetize realistic animations of 3D biped characters in a simulated environment, using genetic algorithms. Characters are represented as a structure of rigid bodies linked each other by 1DOF joints. Such joints are controlled by sinusoidal functions whose parameters are calculated by the genetic algorithm. Results, obtained by testing and comparing several different genetic operators, are presented. The system we have created allows the non-skilled user, to automatically create animations by setting only few key-poses of the characters.</p>

In this thesis, we study the development of linguistic concepts (corresponding to a subset of nouns, verbs and adjectives) on a humanoid robot. To accomplish this goal, we use affordances, a notion first proposed by J.J. Gibson to describe the action possibilities offered to an agent by the environment. Using the affordances formalization framework of Sahin et al., we have implemented a learning system on a humanoid robot and obtained the required data from the sensorimotor experiences of the robot. The system we developed (1) can learn verb, adjective and noun concepts, (2) represent them in terms of strings of prototypes and dependencies based on affordances, (3) can accurately recognize the concept of novel objects and events, and (4) can be used for tasks such as goal emulation and multi step planning.

This thesis focuses on implementing a program, using Python and the symbolic package SymPy, to evaluate balancing of a humanoid robot modelled as inverted pendulums. The balancing algorithm used to evaluate the program is the feedback controller LQR. The program has successfully implemented a working LQR algorithm together with features such as underactuation and a tilting plane as disturbance. We have shown that the energy is conserved for the falling pendulums and that it is possible to predict the behavior for certain parameter values of the pendulums, thus confirming that the program is working correctly. Furthermore we have shown that a fully-actuated system is more controllable than an under-actuated system, and for each actuator that is removed, the system becomes less controllable. Finally we discuss the program performance where some concern is given toward the seemingly poor execution time of the program. The program has been tested for up to five pendulums with successful results. Most of the results however, are revolving around three pendulum systems.