Dynamically stepping over large obstacle utilizing PSO optimization in the B4LC system

This paper proposes a control structure to resolve the issue of dynamically stepping over large obstacles in the B4LC control system. We reform the local control units LegSwing, LockHip and KneeF lexion respectively. The optimization module with Particle Swarm Optimization (PSO) method is employed to tune the parameters of those controllers by formulating locomotion stability. The optimization process and further validation are conducted on a 3-dimensional simulated bipedal robot. The simulation results reveal that the suggested approach enables robot to dynamically step over a large obstacle with 20cm height by 15cm width in a short time duration

Using Model-based Optimal Control for Conceptional Motion Generation for the Humannoid Robot HRP-2 14 and Design Investigations for Exo-Skeletons

The research field of bipedal locomotion has been active since a few decades now. At one hand, the legged locomotion principle comprises highly flexible and robust mobility for technical applications. At the other hand, a thorough technical understanding of bipedalism supports efforts of clinicians and engineers to help people, suffering from reduced locomotion capabilities caused by fatal incidents. Since the technology enabled the construction of numerous robotic devices, among them: various humanoids, researchers started to investigate bipedalism by abstraction and adoption for technical applications. Findings from humanoid robotics are further exploited for the construction of devices for human performance augmentation and mobility support or gait rehabilitation, among them: orthosis and exo-skeletons. Although this research continuously progresses, the motion capacities of humanoid robots still lack far behind those of humans in terms of forward velocity, robustness and appearance of the overall motion. Generally, it is claimed that the difference of performance between humans and robotics is not only due to the limiting characteristics of the employed technology, e.g. constructive lack of specific determinants of gait for bipedalism or dynamic limits of the actuation system, but as well to the adopted methods for motion generation and control. For humanoid robotics, methods for motion generation are classified into optimization-based methods and those that employ heuristics, that are mostly distinguished based on the problem complexity (computation time) and the resulting dynamic error between the generated motion and the dynamics of the real robot. The implementation of the dynamic motion on the robotic platform is usually comprised with an on-line stabilizing control system. This control system must then identify and resolve instantaneously the dynamic error to maintain a continuously stable operation of the device. A large dynamic error and breach of the dynamic limits of the actuation system can quickly lead to a fatal destabilization of the device. This work proposes a contribution to the model computation and the strategy of the problem formulation of direct multiple-shooting based optimal control (Bock et. al.) for dynamically stable optimization-based motion generation. The computation of the whole-body dynamic model inside the optimization relies either on forward or inverse dynamics approach. As the inverse dynamics approach has frequently been perceived as less resource intensive than the forward dynamics approach, a new generic algorithm for insufficiently constrained, under-actuated dynamic systems has been developed and thoroughly tested to comply with all numerical restrictions of the enveloping optimization algorithm. Based on this contribution, various optimal control problems for the humanoid platform HRP-2 14 have been formulated to assess the influence of different biologically inspired optimization criteria on the final motion characteristics of walking motions. From thorough bibliographic researches a dynamically more accurate model was comprised, by taking into account the impact absorbing element in the ankle joint complex. Based on the experiences of the previous study, a problem formulation for the limiting case of, dynamically overstepping an obstacle of 20cm x 11cm (height x width) with only two steps, while maintaining its stable operation was accomplished. This is a new record for this platform. In a further part, this work proposes an iterative comprehensive model-based optimal control approach for the conception of a lower limb exo-skeleton that respects the integrated nature of such a mechatronic device. In this contribution, a human effectively wearing such a lower limb exo-skeleton is modeled. The approach then substantiates all system components in an iterative procedure, based on the complete system model, effectively resolving all complex inter-dependencies between the different components of the system. The study in this work is conducted on an important benchmark motion, walking, of a healthy human being. From this study the limiting characteristics of the system are determined and substantial propositions to the realization of various system components are formulated

Nonlinear Model Predictive Control for Motion Generation of Humanoids

Das Ziel dieser Arbeit ist die Untersuchung und Entwicklung numerischer Methoden zur Bewegungserzeugung von humanoiden Robotern basierend auf nichtlinearer modell-prädiktiver Regelung. Ausgehend von der Modellierung der Humanoiden als komplexe Mehrkörpermodelle, die sowohl durch unilaterale Kontaktbedingungen beschränkt als auch durch die Formulierung unteraktuiert sind, wird die Bewegungserzeugung als Optimalsteuerungsproblem formuliert. In dieser Arbeit werden numerische Erweiterungen basierend auf den Prinzipien der Automatischen Differentiation für rekursive Algorithmen, die eine effiziente Auswertung der dynamischen Größen der oben genannten Mehrkörperformulierung erlauben, hergeleitet, sodass sowohl die nominellen Größen als auch deren ersten Ableitungen effizient ausgewertet werden können. Basierend auf diesen Ideen werden Erweiterungen für die Auswertung der Kontaktdynamik und der Berechnung des Kontaktimpulses vorgeschlagen. Die Echtzeitfähigkeit der Berechnung von Regelantworten hängt stark von der Komplexität der für die Bewegungerzeugung gewählten Mehrkörperformulierung und der zur Verfügung stehenden Rechenleistung ab. Um einen optimalen Trade-Off zu ermöglichen, untersucht diese Arbeit einerseits die mögliche Reduktion der Mehrkörperdynamik und andererseits werden maßgeschneiderte numerische Methoden entwickelt, um die Echtzeitfähigkeit der Regelung zu realisieren. Im Rahmen dieser Arbeit werden hierfür zwei reduzierte Modelle hergeleitet: eine nichtlineare Erweiterung des linearen inversen Pendelmodells sowie eine reduzierte Modellvariante basierend auf der centroidalen Mehrkörperdynamik. Ferner wird ein Regelaufbau zur GanzkörperBewegungserzeugung vorgestellt, deren Hauptbestandteil jeweils aus einem speziell diskretisierten Problem der nichtlinearen modell-prädiktiven Regelung sowie einer maßgeschneiderter Optimierungsmethode besteht. Die Echtzeitfähigkeit des Ansatzes wird durch Experimente mit den Robotern HRP-2 und HeiCub verifiziert. Diese Arbeit schlägt eine Methode der nichtlinear modell-prädiktiven Regelung vor, die trotz der Komplexität der vollen Mehrkörperformulierung eine Berechnung der Regelungsantwort in Echtzeit ermöglicht. Dies wird durch die geschickte Kombination von linearer und nichtlinearer modell-prädiktiver Regelung auf der aktuellen beziehungsweise der letzten Linearisierung des Problems in einer parallelen Regelstrategie realisiert. Experimente mit dem humanoiden Roboter Leo zeigen, dass, im Vergleich zur nominellen Strategie, erst durch den Einsatz dieser Methode eine Bewegungserzeugung auf dem Roboter möglich ist. Neben Methoden der modell-basierten Optimalsteuerung werden auch modell-freie Methoden des verstärkenden Lernens (Reinforcement Learning) für die Bewegungserzeugung untersucht, mit dem Fokus auf den schwierig zu modellierenden Modellunsicherheiten der Roboter. Im Rahmen dieser Arbeit werden eine allgemeine vergleichende Studie sowie Leistungskennzahlen entwickelt, die es erlauben, modell-basierte und -freie Methoden quantitativ bezüglich ihres Lösungsverhaltens zu vergleichen. Die Anwendung der Studie auf ein akademisches Beispiel zeigt Unterschiede und Kompromisse sowie Break-Even-Punkte zwischen den Problemformulierungen. Diese Arbeit schlägt basierend auf dieser Grundlage zwei mögliche Kombinationen vor, deren Eigenschaften bewiesen und in Simulation untersucht werden. Außerdem wird die besser abschneidende Variante auf dem humanoiden Roboter Leo implementiert und mit einem nominellen modell-basierten Regler verglichen

The role of compliance in humans and humanoid robots locomotion

We build robots that are meant to look and work like humans, with humans, inspired by humans. But many are the human characteristics that we have not yet understood, as humans are highly complex systems. One fundamental characteristic is compliance, which characterizes human movements. If our body was completely rigid, we would not be able to climb up trees or walk on mountainous paths as easily as we do. But despite being inspired to be a copy of human beings, humanoid robots had rigid links connected with rigid joints since their first appearance. It is only recently that they started to be more “human-like”, with the development of compliant actuators. In this thesis the objective is to analyze of the role of compliance in human walking and in humanoid robots motions. We model both the human body and humanoid robots as rigid multi-body systems. Both systems are highly redundant, reason for which optimization represents an essential tool to achieve our goals. In particular, we adopt optimal control approaches. In many state of the art compliant walking mechanisms, compliance is introduced at joint level by means of elastic components with constant stiffness, due to the difficulty of varying stiffness and the considerable dimensions of currently available variable stiffness actuators. This is the reason for which many studies focused on finding constant joint stiffness during human walking. However, biomechanics studies have shown that stiffness changes in human joints during movements. The questions we want to address are therefore: how does stiffness modulate during human walking and what is the influence of such modulations on the gait? To answer these questions, we used walking motions from motion capture data and a 2D dynamic model of the human body, where the actuation of the leg joints are modeled with torsional springs and bi-articular coupling springs with variable stiffness. We computed the stiffness profiles of these springs, which showed how stiffness changes over the walking cycle and can also assume big values, contrasting with many state of the art walking mechanisms. We proceeded by analyzing how walking gaits are modified if the stiffness modulation is reduced. This further step showed that the original walking gait could be approximated in unconstrained walking scenarios such as level ground and slopes but not in constraint ones as stairs. This result demonstrated the importance of stiffness modulation during walking and can serve for future compliant actuators design. There are several existing humanoid robots with compliant actuators. Among these, the iCub is a widely spreaded advanced research humanoid that has recently acquired legs with Series Elastic Actuators (SEA). The reduced version of it, HeiCub, was delivered to Heidelberg University by the end of 2014 and is the robot used in this thesis. We first analyzed the motion of squatting. The problem is formulated as an optimal control problem where only the three pitch joints of the legs are considered active and the whole-body dynamics of the robot is used. Squat motions for different objective functions are generated for the robot with and without the use of SEA. A step further is taken in using all the actuated degrees of freedom of the robot to generate push recovery motions with the same approach, also considering the SEA. As there is a lack of literature and experiments of iCub walking, for this complex task we aimed at exploiting the capabilities of HeiCub by measuring its walking performances. We used the table cart model to generate walking trajectories on level ground, slope and stairs, which have never been achieved before by other iCub robots. In this way we could gain details of the platform that were unknown beforehand that are fundamental to be used in future optimal control formulations. Thanks to this study, future developments of walking control frameworks for the iCub family robots have now a point of reference

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

Optimization based exploitation of the ankle elasticity of HRP-2 for overstepping large obstacles

International audienceThis paper proposes a new generic strategy to investigate the dynamic limits of the humanoid robot HRP-2 based on whole body optimal control optimization. In this study we exploit the intuitive access to complex motion characteristics, given by optimal control, to effectively resolve a major technical coupling effect, namely between the ankle elasticity and the stabilizing algorithms. Control efforts are reduced to get a clearer view of the actual system limits and to exploit its capacities at maximum. As showcase we decided to focus on a stepping motion over a cylindrical obstacle. This study is further supported by real experiments on the HRP-2 14 robotic platform and we could successfully extend the present maximum of a dynamically overstepped obstacle to 20cm (height) x 11cm (width) (including safety margin) without multi-contact support