Push recovery with stepping strategy based on time-projection control

In this paper, we present a simple control framework for on-line push recovery with dynamic stepping properties. Due to relatively heavy legs in our robot, we need to take swing dynamics into account and thus use a linear model called 3LP which is composed of three pendulums to simulate swing and torso dynamics. Based on 3LP equations, we formulate discrete LQR controllers and use a particular time-projection method to adjust the next footstep location on-line during the motion continuously. This adjustment, which is found based on both pelvis and swing foot tracking errors, naturally takes the swing dynamics into account. Suggested adjustments are added to the Cartesian 3LP gaits and converted to joint-space trajectories through inverse kinematics. Fixed and adaptive foot lift strategies also ensure enough ground clearance in perturbed walking conditions. The proposed structure is robust, yet uses very simple state estimation and basic position tracking. We rely on the physical series elastic actuators to absorb impacts while introducing simple laws to compensate their tracking bias. Extensive experiments demonstrate the functionality of different control blocks and prove the effectiveness of time-projection in extreme push recovery scenarios. We also show self-produced and emergent walking gaits when the robot is subject to continuous dragging forces. These gaits feature dynamic walking robustness due to relatively soft springs in the ankles and avoiding any Zero Moment Point (ZMP) control in our proposed architecture.Comment: 20 pages journal pape

Reaction Null Space of a multibody system with applications in robotics

This paper provides an overview of implementation examples based on the Reaction Null Space formalism, developed initially to tackle the problem of satellite-base disturbance of a free-floating space robot, when the robot arm is activated. The method has been applied throughout the years to other unfixed-base systems, e.g. flexible-base and macro/mini robot systems, as well as to the balance control problem of humanoid robots. The paper also includes most recent results about complete dynamical decoupling of the end-link of a fixed-base robot, wherein the end-link is regarded as the unfixed-base. This interpretation is shown to be useful with regard to motion/force control scenarios. Respective implementation results are provided

Humanoid Balancing Behavior Featured by Underactuated Foot Motion

A novel control synthesis is proposed for humanoids to demonstrate unique foot-tilting behaviors that are comparable to humans in balance recovery. Our study of model-based behaviors explains the underlying mechanism and the significance of foot tilting well. Our main algorithms are composed of impedance control at the center of mass, virtual stoppers that prevent overtilting of the feet, and postural control for the torso. The proof of concept focuses on the sagittal scenario and the proposed control is effective to produce human-like balancing behaviors characterized by active foot tilting. The successful replication of this behavior on a real humanoid proves the feasibility of deliberately controlled underactuation. The experimental validation was rigorously performed, and the data from the submodules and the entire control were presented and analyzed

Fall Prediction and Controlled Fall for Humanoid Robots

Humanoids which resemble humans in their body structure and degrees of freedom are anticipated to work like them within infrastructures and environments constructed for humans. In such scenarios, even humans who have exceptional manipulation, balancing, and locomotion skills are vulnerable to fall, humanoids being their approximate imitators are no exception to this. Furthermore, their high center of gravity position in relation to their small support polygon makes them more prone to fall, unlike other robots such as quadrupeds. The consequences of these falls are so devastating that it can instantly annihilate both the robot and its surroundings. This has become one of the major stumbling blocks which humanoids have to overcome to operate in real environments. As a result, in this thesis, we have strived to address the imminent fall over of humanoids by developing different control techniques. The fall over problem as such can be divided into three subissues: fall prediction, controlled fall, and its recovery. In the presented work, the first two issues have been addressed, and they are presented in three parts. First, we define what is fall over for humanoids, different sources for it to happen, the effect fall over has both on the robot and to its surroundings, and how to deal with them. Following which, we give a brief introduction to the overall system which includes both the hardware and software components which have been used throughout the work for varied purposes. Second, the first sub-issue is addressed by proposing a generic method to predict the falling over of humanoid robots in a reliable, robust, and agile manner across various terrains, and also amidst arbitrary disturbances. The aforementioned characteristics are strived to attain by proposing a prediction principle inspired by the human balance sensory systems. Accordingly, the fusion of multiple sensors such as inertial measurement unit and gyroscope (IMU), foot pressure sensor (FPS), joint encoders, and stereo vision sensor, which are equivalent to the human\u2019s vestibular, proprioception, and vision systems are considered. We first define a set of feature-based fall indicator variables (FIVs) from the different sensors, and the thresholds for those FIVs are extracted analytically for four major disturbance scenarios. Further, an online threshold interpolation technique and an impulse adaptive counter limit are proposed to manage more generic disturbances. For the generalized prediction process, both the instantaneous and cumulative sum of each FIVs are normalized, and a suitable value is set as the critical limit to predict the fall over. To determine the best combination and the usefulness of multiple sensors, the prediction performance is evaluated on four different types of terrains, in three unique combinations: first, each feature individually with their respective FIVs; second, an intuitive performance based (PF); and finally, Kalman filter based (KF) techniques, which involve the usage of multiple features. For PF and KF techniques, prediction performance evaluations are carried out with and without adding noise. Overall, it is reported that KF performs better than PF and individual sensor features under different conditions. Also, the method\u2019s ability to predict fall overs during the robot\u2019s simple dynamic motion is also tested and verified through simulations. Experimental verification of the proposed prediction method on flat and uneven terrains was carried out with the WALK-MAN humanoid robot. Finally, in reference to the second sub-issue, i.e., the controlled fall, we propose two novel fall control techniques based on energy concepts, which can be applied online to mitigate the impact forces incurred during the falling over of humanoids. Both the techniques are inspired by the break-fall motions, in particular, Ukemi motion practiced by martial arts people. The first technique reduces the total energy using a nonlinear control tool, called energy shaping (ES) and further distributes the reduced energy over multiple contacts by means of energy distribution polygons (EDP). We also include an effective orientation control to safeguard the end-effectors in the event of ground impacts. The performance of the proposed method is numerically evaluated by dynamic simulations under the sudden falling over scenario of the humanoid robot for both lateral and sagittal falls. The effectiveness of the proposed ES and EDP concepts are verified by diverse comparative simulations regarding total energy, distribution, and impact forces. Following the first technique, we proposed another controller to generate an online rolling over motion based on the hypothesis that multi-contact motions can reduce the impact forces even further. To generate efficient rolling motion, critical parameters are defined by the insights drawn from a study on rolling, which are contact positions and attack angles. In addition, energy-injection velocity is proposed as an auxiliary rolling parameter to ensure sequential multiple contacts in rolling. An online rolling controller is synthesized to compute the optimal values of the rolling parameters. The first two parameters are to construct a polyhedron, by selecting suitable contacts around the humanoid\u2019s body. This polyhedron distributes the energy gradually across multiple contacts, thus called energy distribution polyhedron. The last parameter is to inject some additional energy into the system during the fall, to overcome energy drought and tip over successive contacts. The proposed controller, incorporated with energy injection, minimization, and distribution techniques result in a rolling like motion and significantly reduces the impact forces, and it is verified in numerical experiments with a segmented planar robot and a full humanoid model

Standing Posture Modeling and Control for a Humanoid Robot

Master'sMASTER OF ENGINEERIN

From walking to running: robust and 3D humanoid gait generation via MPC

Humanoid robots are platforms that can succeed in tasks conceived for humans. From locomotion in unstructured environments, to driving cars, or working in industrial plants, these robots have a potential that is yet to be disclosed in systematic every-day-life applications. Such a perspective, however, is opposed by the need of solving complex engineering problems under the hardware and software point of view. In this thesis, we focus on the software side of the problem, and in particular on locomotion control. The operativity of a legged humanoid is subordinate to its capability of realizing a reliable locomotion. In many settings, perturbations may undermine the balance and make the robot fall. Moreover, complex and dynamic motions might be required by the context, as for instance it could be needed to start running or climbing stairs to achieve a certain location in the shortest time. We present gait generation schemes based on Model Predictive Control (MPC) that tackle both the problem of robustness and tridimensional dynamic motions. The proposed control schemes adopt the typical paradigm of centroidal MPC for reference motion generation, enforcing dynamic balance through the Zero Moment Point condition, plus a whole-body controller that maps the generated trajectories to joint commands. Each of the described predictive controllers also feature a so-called stability constraint, preventing the generation of diverging Center of Mass trajectories with respect to the Zero Moment Point. Robustness is addressed by modeling the humanoid as a Linear Inverted Pendulum and devising two types of strategies. For persistent perturbations, a way to use a disturbance observer and a technique for constraint tightening (to ensure robust constraint satisfaction) are presented. In the case of impulsive pushes instead, techniques for footstep and timing adaptation are introduced. The underlying approach is to interpret robustness as a MPC feasibility problem, thus aiming at ensuring the existence of a solution for the constrained optimization problem to be solved at each iteration in spite of the perturbations. This perspective allows to devise simple solutions to complex problems, favoring a reliable real-time implementation. For the tridimensional locomotion, on the other hand, the humanoid is modeled as a Variable Height Inverted Pendulum. Based on it, a two stage MPC is introduced with particular emphasis on the implementation of the stability constraint. The overall result is a gait generation scheme that allows the robot to overcome relatively complex environments constituted by a non-flat terrain, with also the capability of realizing running gaits. The proposed methods are validated in different settings: from conceptual simulations in Matlab to validations in the DART dynamic environment, up to experimental tests on the NAO and the OP3 platforms

Climbing and Walking Robots

Nowadays robotics is one of the most dynamic fields of scientific researches. The shift of robotics researches from manufacturing to services applications is clear. During the last decades interest in studying climbing and walking robots has been increased. This increasing interest has been in many areas that most important ones of them are: mechanics, electronics, medical engineering, cybernetics, controls, and computers. Today’s climbing and walking robots are a combination of manipulative, perceptive, communicative, and cognitive abilities and they are capable of performing many tasks in industrial and non- industrial environments. Surveillance, planetary exploration, emergence rescue operations, reconnaissance, petrochemical applications, construction, entertainment, personal services, intervention in severe environments, transportation, medical and etc are some applications from a very diverse application fields of climbing and walking robots. By great progress in this area of robotics it is anticipated that next generation climbing and walking robots will enhance lives and will change the way the human works, thinks and makes decisions. This book presents the state of the art achievments, recent developments, applications and future challenges of climbing and walking robots. These are presented in 24 chapters by authors throughtot the world The book serves as a reference especially for the researchers who are interested in mobile robots. It also is useful for industrial engineers and graduate students in advanced study

Dynamic Balance and Gait Metrics for Robotic Bipeds

For legged robots to be useful in the real world, they must be able to balance and walk reliably. Both of these abilities improve when a system is more effective at moving itself around relative to its contacts (i.e., its feet). Achieving this type of movement depends both on the controller used to perform the motion and the physical properties of the system. Although much work has been done on the development of dynamic controllers for balance and gait, only limited research exists on how to quantify a system’s physical balance capabilities or how to modify the system to improve those capabilities. From the control perspective, there are three strategies for maintaining balance in bipeds: flexing, leaning, and stepping. Both stepping and leaning strategies typically depend on balance points (critical points used for maintaining or regaining balance) to determine whether or not a step is needed, and if so, where to step. Although several balance point estimators exist, the majority of these methods make undesirable assumptions (e.g., ignoring the impact dynamics, assuming massless legs, planar motion, etc.). From the physical design perspective, one promising approach for analyzing system performance is a set of dynamic ratios called velocity and momentum gains, which are dependent only on the (scale-invariant) dynamic parameters and instantaneous configuration of a system, enabling entire classes of mechanisms to be analyzed at the same time. This thesis makes four key contributions towards improving biped balancing capabilities. First, a dynamic bipedal controller is proposed which uses a 3D balance point estimator both to respond to disturbances and produce reliable stepping. Second, a novel balance point estimator is proposed that facilitates stepping while combining and expanding the features of existing 2D and 3D estimators to produce a generalized 3D formulation. Third, the momentum gain formulation is extended to general 2D and 3D systems, then both gains are compared to centroidal momentum via a spatial formulation and incorporated into a generalized gain definition. Finally, the gains are used as a metric in an optimization framework to design parameterized balancing mechanisms within a given configuration space. Effectively, this enables an optimization of how well a system could balance without the need to pre-specify or co-generate controllers and/or trajectories. To validate the control contributions, simulated bipeds are subjected to external disturbances while standing still and walking. For the gain contributions, the framework is used to compare gain-optimized mechanisms to those based on the cost of transport metric. Through the combination of gain-based physical design optimization and the use of predictive, real-time balance point estimators within dynamic controllers, bipeds and other legged systems will soon be able to achieve reliable balance and gait in the real world